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1
V, Rajanesh. "A TRUST BASED CLUSTER HEAD SELECTION APPROACH USING RBFO AND HYBRID BFO-BSO FOR WIRELESS SENSOR NETWORK." ICTACT Journal on Communication Technology 11, no. 2 (2020): 2193–97. https://doi.org/10.21917/ijct.2020.0324.
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The devices to create a connected network are demanded by the communication task which involves in the wireless sensor network for disseminating and collecting the information based on the radio transmission. The network lifetime’s extension in the operational environment is the essential aim of the WSNs for exchanging the batteries of sensor node is an unfeasible or impossible activity probably. The selection of CHs is targeted in the clustered network that reduces the energy and transmission costs. It’s essential to make the optimal selection of CH to improve the lifetime of a network. However, Nondeterministic Polynomial (NP) hard is considered for CH selections. The natural swarm inspired algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Hybrid algorithm (Bacterial foraging Optimization) BFO-BSO (Bee Swarm Optimization) have search their path into the domain and effectiveness is proved. An approach of a trust-based cluster head selection is introduced for improving the efficiency in terms of choosing the cluster head. To compute a trust level for every node, a designing of trust model is done and is implemented. By using the additional three parameters in addition to the hybrid approach such as trust value, residual energy, and the number of neighbors, the cluster heads are chosen. For choosing of cluster head, the T-BOA is adapted to achieve the different objectives such as increased performance of a network, reduced end to end delay, and decreased usage of energy in this work.
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Drias, Yassine, and Habiba Drias. "Social Networks Discovery Based on Information Retrieval Technologies and Bees Swarm Optimization." International Journal of Systems and Service-Oriented Engineering 4, no. 3 (2014): 46–65. http://dx.doi.org/10.4018/ijssoe.2014070103.
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Unlike the previous works where detecting communities is performed on large graphs, our approach considers textual documents for discovering potential social networks. More precisely, the aim of this paper is to extract social communities from a collection of documents and a query specifying the domain of interest that may link the group. We propose a methodology that develops an information retrieval system capable to generate the documents that are in relationship with any topic. The authors of these documents are linked together to constitute the social community around the given thematic. The search process in the information retrieval system is designed using BSO, the bee swarm optimization method in order to optimize the retrieval time for large amount of documents. Our approach was implemented and tested on CACM and DBLP and the time of building a social network is quasi instant.
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Sobecki, Janusz. "Comparison of Selected Swarm Intelligence Algorithms in Student Courses Recommendation Application." International Journal of Software Engineering and Knowledge Engineering 24, no. 01 (2014): 91–109. http://dx.doi.org/10.1142/s0218194014500041.
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In this paper a comparison of a few swarm intelligence algorithms applied in recommendation of student courses is presented. Swarm intelligence algorithms are nowadays successfully used in many areas, especially in optimization problems. To apply each swarm intelligence algorithm in recommender systems a special representation of the problem space is necessary. Here we present the comparison of efficiency of grade prediction of several evolutionary algorithms, such as: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weed Optimization (IWO), Bee Colony Optimization (BCO) and Bat Algorithm (BA).
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Pavithra M.P and P.Maneesha. "HEART DISEASE PREDICTION USING BIO INSPIRED ALGORITHMS." international journal of engineering technology and management sciences 8, no. 1 (2024): 125–29. http://dx.doi.org/10.46647/ijetms.2024.v08i01.015.
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Health diseases are increasing day by day due to life style and hereditary. In this aspect, heart disease is the most important cause of demise in the human kind over past few years. The objective of this paper is to predict the Heart Disease by applying Artificial Neural Network using swarm Intelligence algorithm. Swarm intelligence (SI) is relatively new interdisciplinary field of research. The Swarm-based algorithms have recently emerged as a family of nature-inspired, population-based algorithms that are capable of producing low cost, fast, and robust solutions to several complex problems. There are so many swarm intelligence algorithms for optimization like Group Search Optimization (GSO), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) etc. This paper proposes Particle Swarm Optimization (PSO) is the most population Intelligence Algorithm and has good performance on optimization. This paper aims to predict the heart disease using Feed forward of Artificial Neural Network (ANN) to classifying patient as diseased and non-diseased. We have evaluated our new classification approach via the well known data sets .
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Рабійчук, І. О., and А. В. Фечан. "The main challenges of adaptability of swarm intelligence algorithms." Scientific Bulletin of UNFU 34, no. 5 (2024): 97–103. http://dx.doi.org/10.36930/40340513.
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Analyzed three swarm intelligence algorithms, namely Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), Particle Swarm Optimization (PSO) and the adaptability of these algorithms to a dynamic environment. Firstly, the ACO algorithm was analyzed, the behavior of ants in nature, the purpose of the algorithm, and its shortcomings in a dynamic environment. Then the existing modifications of this algorithm to changing environments were investigated, namely AСO with dynamic pheromone updating (AACO), ACO with adaptive memory (ACO-AP), ACO with multi-agent system (MAS-ACO), ACO with machine learning algorithms (MLACO). The advantages and disadvantages of these modifications are also discussed in detail. The software tools that implement the functionality of this algorithm, such as AntTweakBar, AntOpt, EasyAnt have been mentioned. These software tools provide an opportunity to develop new modifications of the ACO algorithms and to study existing ones. Furthermore, the capabilities of the BCO algorithm were clarified and the behavior and parameters of this algorithm were described, its pros and cons in a dynamic environment were investigated. The following BCO modifications were considered: Group Bee Algorithm (GBA), Artificial Bee Colony (ABC), and open source software: PySwarms, PyABC. The third part of the article investigates the work of the PSO algorithm, its advantages and disadvantages of adaptation to dynamic environments. Dynamic Particle Swarm Optimization with Permutation (DPSO-P), Dynamic Multi-swarm Particle Swarm Optimization Based on Elite Learning (DMS-P50-EL) are considered as modifications of PSO to adapt to dynamic environments. The libraries for work such as SciPy, DEAP, PyGAD, Particleswarm, JSwarm (has a wide API and well-written documentation), Dlib have been mentioned. Finally, a comparative table with the most important properties (resistance to environmental changes, complexity of implementation, the possibility of using for a UAV swarm, etc.) for all three algorithms was created, a brief description of similar articles comparing algorithms of swarm intelligence was also made, and the conclusions of the study were drawn.
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Pan, Yaxi, and Jian Dong. "Design and Optimization of an Ultrathin and Broadband Polarization-Insensitive Fractal FSS Using the Improved Bacteria Foraging Optimization Algorithm and Curve Fitting." Nanomaterials 13, no. 1 (2023): 191. http://dx.doi.org/10.3390/nano13010191.
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A frequency-selective surface (FSS) optimization method combining a curve-fitting technique and an improved bacterial foraging optimization (IBFO) algorithm is proposed. In the method, novel Koch curve-like FSS and Minkowski fractal islands FSS were designed with a desired resonance center frequency and bandwidth. The bacteria foraging optimization (BFO) algorithm is improved to enhance the performance of the FSS. A curve-fitting technique is provided to allow an intuitive and numerical analysis of the correspondence between the FSS structural parameters and the frequency response. The curve-fitting results are used to evaluate the fitness function of the IBFO algorithm, replacing multiple repeated calls to the electromagnetic simulation software with the curve-fitting equation and thus speeding up the design process. IBFO is compared with the classical BFO algorithm, the hybrid BFO-particle swarm optimization algorithm (BSO), and the artificial bee colony algorithm (ABC) to demonstrate its superior performance. The designed fractal FSS is fabricated and tested to verify the experimental results. The simulation and measurement results show that the proposed FSS has a fractional bandwidth of 91.7% in the frequency range of 3.41–9.19 GHz (S, C, and X-bands). In addition, the structure is very thin, with only 0.025λ and 0.067λ at the lowest and highest frequencies, respectively. The proposed fractal FSS has shown stable performance for both TE and TM polarizations at oblique incidence angles up to 45°. according to simulations and measurements.
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Aurasopon, Apinan, Chiraphon Takeang, and Wanchai Khamsen. "Enhanced Local Search for Bee Colony Optimization in Economic Dispatch with Smooth Cost Functions." Processes 13, no. 3 (2025): 787. https://doi.org/10.3390/pr13030787.
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This study introduces an Enhanced Local Search (ELS) technique integrated into the Bee Colony Optimization (BCO) algorithm to address the Economic Dispatch (ED) problem characterized by a continuous cost function. This paper combines Lambda Iteration and Golden Section Search with Bee Colony Optimization (BCO) into a more efficient method called Enhanced Local Search for Bee Colony Optimization (ELS-BCO). The proposed methodology seeks to enhance search efficiency and solution quality. One of the main challenges with standard BCO is random initialization, which can lead to slow convergence. The ELS-BCO algorithm overcomes this issue by using Lambda Iteration for better initial estimation and Golden Section Search to refine the movement direction of the bees. These enhancements significantly improve the algorithm’s capacity to identify optimal solutions. The performance of ELS-BCO was evaluated on two benchmark systems with three and six power generators, and the results were compared with those of the original BCO, LI-BCO, GS-BCO, and traditional optimization methods such as Particle Swarm Optimization (PSO), Hybrid PSO, Lambda Iteration with Simulated Annealing, the Sine Cosine Algorithm, Mountaineering Team-Based Optimization, and Teaching–Learning-Based Optimization. The results demonstrate that ELS-BCO achieves faster convergence and higher-quality solutions than these existing methods.
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Djenouri, Y., H. Drias, and Z. Habbas. "Hybrid Intelligent Method for Association Rules Mining Using Multiple Strategies." International Journal of Applied Metaheuristic Computing 5, no. 1 (2014): 46–64. http://dx.doi.org/10.4018/ijamc.2014010103.
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Association rules mining has attracted a lot of attention in the data mining community. It aims to extract the interesting rules from any given transactional database. This paper deals with association rules mining algorithms for very large databases and especially for those existing on the web. The numerous polynomial exact algorithms already proposed in literature processed the data sets of a medium-size in an efficient way. However, they are not capable of handling the huge amount of data in the web context where the response time must be very short. Moreover, the bio-inspired methods have proved to be paramount for the association rules mining problem. In this work, a new association rules mining algorithm based on an improved version of Bees Swarm Optimization and Tabu Search algorithms is proposed. BSO is chosen for its remarkable diversification process while tabu search for its efficient intensification strategy. To make the idea simpler, BSO will browse the search space in such a way to cover most of its regions and the local exploration of each bee is computed by tabu search. Also, the neighborhood search and three strategies for calculating search area are developed. The suggested strategies called (modulo, next, syntactic) are implemented and demonstrated using various data sets of different sizes. Experimental results reveal that the authors' approach in terms of the fitness criterion and the CPU time improves the ones that already exist.
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Wang, Chen, Lincoln C. Wood, Heng Li, Zhenye Aw, and Abolfazl Keshavarzsaleh. "Applied Artificial Bee Colony Optimization Algorithm in Fire Evacuation Routing System." Journal of Applied Mathematics 2018 (2018): 1–17. http://dx.doi.org/10.1155/2018/7962952.
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Every minute counts in an event of fire evacuation where evacuees need to make immediate routing decisions in a condition of low visibility, low environmental familiarity, and high anxiety. However, the existing fire evacuation routing models using various algorithm such as ant colony optimization or particle swarm optimization can neither properly interpret the delay caused by congestion during evacuation nor determine the best layout of emergency exit guidance signs; thus bee colony optimization is expected to solve the problem. This study aims to develop a fire evacuation routing model “Bee-Fire” using artificial bee colony optimization (BCO) and to test the routing model through a simulation run. Bee-Fire is able to find the optimal fire evacuation routing solutions; thus not only the clearance time but also the total evacuation time can be reduced. Simulation shows that Bee-Fire could save 10.12% clearance time and 15.41% total evacuation time; thus the congestion during the evacuation process could be effectively avoided and thus the evacuation becomes more systematic and efficient.
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Liyi Zhang, Liyi Zhang, Zuochen Ren Liyi Zhang, Ting Liu Zuochen Ren, and Jinyan Tang Ting Liu. "Improved Artificial Bee Colony Algorithm Based on Harris Hawks Optimization." 網際網路技術學刊 23, no. 2 (2022): 379–89. http://dx.doi.org/10.53106/160792642022032302016.
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<p>Artificial bee colony algorithm, as a kind of bio-like intelligent algorithm, used by various optimization problems because of its few parameters and simple structure. However, there are also shortcomings such as low convergence accuracy, slow convergence speed, and not easy to jump out of the local optimum. Aiming at this shortcoming, this paper proposes an evolutionary algorithm of improved artificial bee colony algorithm based on reverse learning Harris Hawk (HABC). The basic inspiration of HABC comes from the good convergence of Harris Hawk algorithm in the process of finding the best point of the function. First, introduce the Harris Hawks optimization progressive rapid dives stage in the onlooker bee phase to speed up the algorithm convergence; Secondly, Cauchy reverse learning is added in the scout phase to make the algorithm development more promising areas in order to find a better solution; Finally, 13 standard test functions and CEC-C06 2019 benchmark test results are used to test the proposed HABC algorithm and compare with ABC, Markov Chain based artificial bee colony algorithm (MABC), dragonfly algorithm (DA), particle swarm optimization (PSO), learner performance based behavior algorithm (LPB), and fitness dependent optimizer (FDO). Compared with other algorithms, the convergence speed, optimization accuracy and algorithm success rate of the HABC algorithm are relatively excellent.</p> <p>&nbsp;</p>
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Hafedh, Ali Shabat, and Raqim Raheem Khamael. "Analysis study of the bee algorithms as a mechanism for solving combinatorial problems." Analysis study of the bee algorithms as a mechanism for solving combinatorial problems 30, no. 2 (2023): 1091–98. https://doi.org/10.11591/ijeecs.v30.i2.pp1091-1098.
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Combinatorial optimization problems are problems that have a large number of discrete solutions and a cost function for evaluating those solutions in comparison to one another. With the vital need of solving the combinatorial problem, several research efforts have been concentrated on the biological entities behaviors to utilize such behaviors in population-based metaheuristic. This paper presents bee colony algorithms which is one of the sophisticated biological nature life. A brief detail of the nature of bee life has been presented with further classification of its behaviors. Furthermore, an illustration of the algorithms that have been derived from bee colony which are bee colony optimization, and artificial bee colony. Finally, a comparative analysis has been conducted between these algorithms according to the results of the traveling salesman problem solution. Where the bee colony optimization (BCO) rendered the best performance in terms of computing time and results.
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Berkani, Lamia, Lylia Betit, and Louiza Belarif. "BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks." JUCS - Journal of Universal Computer Science 27, no. (7) (2021): 667–92. https://doi.org/10.3897/jucs.70341.
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Clustering-based approaches have been demonstrated to be efficient and scalable to large-scale data sets. However, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we propose in this article an optimized multiview clustering approach for the recommendation of items in social networks. First, the selection of the initial medoids is optimized using the Bees Swarm optimization algorithm (BSO) in order to generate better partitions (i.e. refining the quality of medoids according to the objective function). Then, the multiview clustering (MV) is applied, where users are iteratively clustered from the views of both rating patterns and social information (i.e. friendships and trust). Finally, a framework is proposed for testing the different alternatives, namely: (1) the standard recommendation algorithms; (2) the clustering-based and the optimized clustering-based recommendation algorithms using BSO; and (3) the MV and the optimized MV (BSO-MV) algorithms. Experimental results conducted on two real-world datasets demonstrate the effectiveness of the proposed BSO-MV algorithm in terms of improving accuracy, as it outperforms the existing related approaches and baselines.
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WONG, LI-PEI, MALCOLM YOKE HEAN LOW, and CHIN SOON CHONG. "BEE COLONY OPTIMIZATION WITH LOCAL SEARCH FOR TRAVELING SALESMAN PROBLEM." International Journal on Artificial Intelligence Tools 19, no. 03 (2010): 305–34. http://dx.doi.org/10.1142/s0218213010000200.
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Many real world industrial applications involve the Traveling Salesman Problem (TSP), which is a problem that finds a Hamiltonian path with minimum cost. Examples of problems that belong to this category are transportation routing problem, scan chain optimization and drilling problem in integrated circuit testing and production. This paper presents a Bee Colony Optimization (BCO) algorithm for symmetrical TSP. The BCO model is constructed algorithmically based on the collective intelligence shown in bee foraging behaviour. The algorithm is integrated with a fixed-radius near neighbour 2-opt (FRNN 2-opt) heuristic to further improve prior solutions generated by the BCO model. To limit the overhead incurred by the FRNN 2-opt, a frequency-based pruning strategy is proposed. The pruning strategy allows only a subset of the promising solutions to undergo local optimization. Experimental results comparing the proposed BCO algorithm with existing approaches on a set of benchmark problems are presented. For 84 benchmark problems, the BCO algorithm is able to obtain an overall average solution quality of 0.31% from known optimum. The results also show that it is comparable to other algorithms such as Ant Colony Optimization and Particle Swarm Optimization.
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Fan, Changjun, and Fei Gao. "Enhanced Human Activity Recognition Using Wearable Sensors via a Hybrid Feature Selection Method." Sensors 21, no. 19 (2021): 6434. http://dx.doi.org/10.3390/s21196434.
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The study of human activity recognition (HAR) plays an important role in many areas such as healthcare, entertainment, sports, and smart homes. With the development of wearable electronics and wireless communication technologies, activity recognition using inertial sensors from ubiquitous smart mobile devices has drawn wide attention and become a research hotspot. Before recognition, the sensor signals are typically preprocessed and segmented, and then representative features are extracted and selected based on them. Considering the issues of limited resources of wearable devices and the curse of dimensionality, it is vital to generate the best feature combination which maximizes the performance and efficiency of the following mapping from feature subsets to activities. In this paper, we propose to integrate bee swarm optimization (BSO) with a deep Q-network to perform feature selection and present a hybrid feature selection methodology, BAROQUE, on basis of these two schemes. Following the wrapper approach, BAROQUE leverages the appealing properties from BSO and the multi-agent deep Q-network (DQN) to determine feature subsets and adopts a classifier to evaluate these solutions. In BAROQUE, the BSO is employed to strike a balance between exploitation and exploration for the search of feature space, while the DQN takes advantage of the merits of reinforcement learning to make the local search process more adaptive and more efficient. Extensive experiments were conducted on some benchmark datasets collected by smartphones or smartwatches, and the metrics were compared with those of BSO, DQN, and some other previously published methods. The results show that BAROQUE achieves an accuracy of 98.41% for the UCI-HAR dataset and takes less time to converge to a good solution than other methods, such as CFS, SFFS, and Relief-F, yielding quite promising results in terms of accuracy and efficiency.
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Thalamala, Ravi Chandran, A. Venkata Swamy Reddy, and B. Janet. "A Novel Bio-Inspired Algorithm Based on Social Spiders for Improving Performance and Efficiency of Data Clustering." Journal of Intelligent Systems 29, no. 1 (2018): 311–26. http://dx.doi.org/10.1515/jisys-2017-0178.
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Abstract Since the last decade, the collective intelligent behavior of groups of animals, birds or insects have attracted the attention of researchers. Swarm intelligence is the branch of artificial intelligence that deals with the implementation of intelligent systems by taking inspiration from the collective behavior of social insects and other societies of animals. Many meta-heuristic algorithms based on aggregative conduct of swarms through complex interactions with no supervision have been used to solve complex optimization problems. Data clustering organizes data into groups called clusters, such that each cluster has similar data. It also produces clusters that could be disjoint. Accuracy and efficiency are the important measures in data clustering. Several recent studies describe bio-inspired systems as information processing systems capable of some cognitive ability. However, existing popular bio-inspired algorithms for data clustering ignored good balance between exploration and exploitation for producing better clustering results. In this article, we propose a bio-inspired algorithm, namely social spider optimization (SSO), for clustering that maintains a good balance between exploration and exploitation using female and male spiders, respectively. We compare results of the proposed algorithm SSO with K means and other nature-inspired algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO) and improved bee colony optimization (IBCO). We find it to be more robust as it produces better clustering results. Although SSO solves the problem of getting stuck in the local optimum, it needs to be modified for locating the best solution in the proximity of the generated global solution. Hence, we hybridize SSO with K means, which produces good results in local searches. We compare proposed hybrid algorithms SSO+K means (SSOKC), integrated SSOKC (ISSOKC), and interleaved SSOKC (ILSSOKC) with K means+PSO (KPSO), K means+genetic algorithm (KGA), K means+artificial bee colony (KABC) and interleaved K means+IBCO (IKIBCO) and find better clustering results. We use sum of intra-cluster distances (SICD), average cosine similarity, accuracy and inter-cluster distance to measure and validate the performance and efficiency of the proposed clustering techniques.
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Singh, Dharmpal. "A Modified Bio Inspired." International Journal of Applied Metaheuristic Computing 9, no. 1 (2018): 60–77. http://dx.doi.org/10.4018/ijamc.2018010105.
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Metaheuristics algorithms are becoming powerful methods for solving many problems of market analysis, data mining, transportation, medical etc. The concept of BAT algorithm, particle swarm optimization, artificial bee colony optimization, cuckoo search, firefly algorithm and harmony search are powerful methods for solving many optimization problems. Here, an effort has been made to propose as modified form of the BAT algorithm based natural echolocation behaviour of bats to solve the optimization problems. The algorithm is also compared other 15 existing benchmark algorithms including statistical methods on five benchmarks data sets. Furthermore, modified BAT algorithm has outperformed the other algorithm in term of robustness and efficiency. The optimality of the algorithm has been also crosscheck with residual analysis and chi (χ2) square testing.
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Jovanović, Aleksandar, and Dušan Teodorović. "Fixed-Time Traffic Control at Superstreet Intersections by Bee Colony Optimization." Transportation Research Record: Journal of the Transportation Research Board 2676, no. 4 (2021): 228–41. http://dx.doi.org/10.1177/03611981211058104.
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The superstreet intersection (or restricted crossing U-turn-, J-turn intersection) fixed-time traffic control system was developed in this study. The optimal (or near-optimal) values of cycle length, splits, and offsets were discovered by minimizing the experienced travel time of all network users traveling through the superstreet intersection. The optimization procedure used was based on the bee colony optimization (BCO) metaheuristic. The BCO is a stochastic, random-search, population-based technique, inspired by the foraging behavior of honey bees. The BCO belongs to the class of swarm intelligence methods. A set of numerical experiments was performed. Superstreet intersection configurations that allowed direct left turns from the major street, as well as configurations with no direct left turns, were analyzed within numerical experiments. The obtained results showed that BCO outperformed the traditional Webster approach in the superstreet geometrical configurations considered.
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Puchta, Erickson, Priscilla Bassetto, Lucas Biuk, et al. "Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller." Energies 14, no. 12 (2021): 3385. http://dx.doi.org/10.3390/en14123385.
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This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.
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Sabery, Ghulam Ali, Ghulam Hassan Danishyar, and Ghulam Sarwar Mubarez. "A Comparative Study of Metaheuristic Optimization Algorithms for Solving Engineering Design Problems." Journal of Mathematics and Statistics Studies 4, no. 4 (2023): 56–69. http://dx.doi.org/10.32996/jmss.2023.4.4.6.
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Metaheuristic optimization algorithms (Nature-Inspired Optimization Algorithms) are a class of algorithms that mimic the behavior of natural systems such as evolution process, swarm intelligence, human activity and physical phenomena to find the optimal solution. Since the introduction of meta-heuristic optimization algorithms, they have shown their profound impact in solving the high-scale and non-differentiable engineering problems. This paper presents a comparative study of the most widely used nature-inspired optimization algorithms for solving engineering classical design problems, which are considered challenging. The teen metaheuristic algorithms employed in this study are, namely, Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Biogeography Based Optimization Algorithm (BBO), Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES), Cuckoo Search algorithm (CS), Differential Evolution (DE), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO). The efficiency of these algorithms is evaluated on teen popular engineering classical design problems using the solution quality and convergence analysis, which verify the applicability of these algorithms to engineering classical constrained design problems. The experimental results demonstrated that all the algorithms provide a competitive solution.
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Nwankwo, Kenneth, and Bartholomew Idoko. "Swarm Intelligence-Based Intrusion Detection Framework Using Neural Network & Bee Colony Optimiation." Kwaghe International Journal of Engineering and Information Technology 2, no. 2 (2025): 36–56. https://doi.org/10.58578/kijeit.v2i2.5452.
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An Intrusion Detection System (IDS) serves as a critical defense mechanism for safeguarding networks against unauthorized activities and cyber attacks. However, the processing of sophisticated datasets with contemporary detection methodologies often presents challenges due to their intricate scale, complicating the identification of complex threats. This study aims to enhance IDS operational efficacy through the development of a novel method integrating Bee Colony Optimization (BCO) and Neural Networks (NN). Employing a quasi-experimental design, the research evaluates the system's performance, demonstrating that the integration of BCO significantly optimizes neural network functionality, thereby improving both the speed of attack detection and the accuracy of feature selection. Utilizing the NSL-KDD dataset, the proposed framework notably minimizes false alerts while augmenting overall detection accuracy levels. The findings underscore that advancements in cybersecurity systems can be achieved through the synergy of Neural Networks and Swarm Intelligence technology, providing effective solutions for real-time intrusion detection systems. This research not only contributes to the theoretical understanding of IDS optimization but also has practical implications for enhancing cybersecurity measures in various organizational contexts.
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Wisam, Najm Al-Din Abed, Abbood Imran Omar, and Najim Abdullah Ali. "Sensored speed control of brushless DC motor based salp swarm algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 4832–40. https://doi.org/10.11591/ijece.v12i5.pp4832-4840.
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This article uses one of the newest and efficient meta-heuristic optimization algorithms inspired from nature called salp swarm algorithm (SSA). It imitates the exploring and foraging behavior of salps in oceans. SSA is proposed for parameters tuning of speed controller in brushless DC (BLDC) motor to achieve the best performance. The suggested work modeling and control scheme is done using MATLAB/Simulink and coding environments. In this work, a 6-step inverter is feeding a BLDC motor with a Hall sensor effect. The proposed technique is compared with other nature-inspired techniques such as cuckoo search optimizer (CSO), honey bee optimization (HBO), and flower pollination algorithm (FPA) under the same operating conditions. This comparison aims to show the superiority features of the proposed tuning technique versus other optimization strategies. The proposed tuning technique shows superior optimization features versus other bio-inspired tuning methods that are used in this work. It improves the controller performance of BLDC motor. It refining the speed response features which results in decreasing the rising time, steady-state error, peak overshoot, and settling time.
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Boonserm, Prasitchai, and Suchada Sitjongsataporn. "A Self-adaptive Hybrid Bio-inspired Optimization Algorithm by Using Sigmoidal Function." International Journal of Intelligent Engineering and Systems 13, no. 6 (2020): 405–18. http://dx.doi.org/10.22266/ijies2020.1231.36.
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The article presents a new hybrid algorithm, which designs based on traditional bio-inspired optimization algorithms. The algorithm leverages the advantage of Particle Swarm Optimization (PSO), Differential Evolution (DE), and Artificial Bee Colony (ABC), replacing other algorithm weaknesses. A new algorithm we proposed is the Fast bio-inspired Optimization Algorithm (FOA). The DE uses multi-parent for trial vector calculation. It increases the diversity of the solution, while the sigmoidal function adds a self-adaptive characteristic to the proposed algorithm. The function replaces a weighting scheme of PSO. In sub-optimal avoidance, the FOA includes a scout bee behavior from ABC. It makes FOA providing the solution faster than traditional versions, while the solution quality is maintained at an acceptable level. According to a new design, an FOA can reduce the algorithm runtime up to 43.57%, 37.14%, 40.78%, and 31.30% compared to PSO, DE, ABC, and DEPSO, respectively. The DEPSO is the hybrid algorithm between DE and PSO. The best solution to FOA is better than the traditional version of the algorithms. The new algorithm design and the optimization speed improvement are the highlight contribution of this article.
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Sedaghat, Mohammad, Esmaeel Rokrok, and Mohammad Bakhshipour. "A New DG Allocation Approach Based on Biogeography-Based Optimization with Considering Fuzzy Load Uncertainty." IAES International Journal of Artificial Intelligence (IJ-AI) 4, no. 3 (2015): 89. http://dx.doi.org/10.11591/ijai.v4.i3.pp89-96.
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A new distributed generation placement method based on biogeography-based optimization (BBO) is investigated in this paper. A significant novelty of this study lies in considering fuzzy load uncertainty. For this purpose a fuzzy backward- forward sweep load flow is proposed. The main objectives of this study is minimizing power losses and improving voltage profile. A comparative study between optimal location and sizing under typical load condition and fuzzy load uncertainty is presented. To verify the efficiency of proposed BBO method, it is conducted on IEEE 33 bus distribution system, also a comparative study between proposed BBO approach and particle swarm optimization (PSO), Technical-learning based optimization (TLBO), Artificial bee colony (ABC), Imperialist competitive algorithm (ICA) is investigated. The simulation results show the excellent and superior performance of proposed BBO approach in comparison with the other intelligent methods.
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Al-Din Abed, Wisam Najm, Omar Abbood Imran, and Ali Najim Abdullah. "Sensored speed control of brushless DC motor based salp swarm algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 4832. http://dx.doi.org/10.11591/ijece.v12i5.pp4832-4840.
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<p>This article uses one of the newest and efficient meta-heuristic optimization algorithms inspired from nature called salp swarm algorithm (SSA). It imitates the exploring and foraging behavior of salps in oceans. SSA is proposed for parameters tuning of speed controller in brushless DC (BLDC) motor to achieve the best performance. The suggested work modeling and control scheme is done using MATLAB/Simulink and coding environments. In this work, a 6-step inverter is feeding a BLDC motor with a Hall sensor effect. The proposed technique is compared with other nature-inspired techniques such as cuckoo search optimizer (CSO), honey bee optimization (HBO), and flower pollination algorithm (FPA) under the same operating conditions. This comparison aims to show the superiority features of the proposed tuning technique versus other optimization strategies. The proposed tuning technique shows superior optimization features versus other bio-inspired tuning methods that are used in this work. It improves the controller performance of BLDC motor. It refining the speed response features which results in decreasing the rising time, steady-state error, peak overshoot, and settling time.</p>
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Imad, El Hajjami, and Benhala Bachir. "Radio-frequency circular integrated inductors sizing optimization using bio-inspired techniques." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6320–31. https://doi.org/10.11591/ijece.v12i6.pp6320-6331.
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In this article, a comparative study is accomplished between three of the most used swarm intelligence (SI) techniques; namely artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) to carry out the optimal design of radio-frequency (RF) spiral inductors, the three algorithms are applied to the cost function of RF circular inductors for 180 nm beyond 2.50 GHz, the aim is to ensure optimal performance with less error in inductance, and a high-quality factor when compared to electromagnetic simulation. Simulation experiments are achieved and performances regarding convergence velocity, robustness, and computing time are checked. Also, this paper shows an impact study of technological parameters and geometric features on the inductance and the quality factor of the studied integrated inductor. The building method of constraints design with algorithms used has given good results and electromagnetic simulations are of good accuracy with an error of 2.31% and 4.15% on the quality factor and inductance respectively. The simulation shows that ACO provides more accuracy in circuit size and fewer errors than ABC and PSO, while PSO and ABC are better in terms of convergence velocity.
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Shakarami, Mahmoud Reza, Sina Khajeh Ahmad Attari, and Farhad Namdari. "A Comprehensive Study on Specifying an Intelligent Approach to Solve Network Reconfiguration Problem." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 3 (2016): 480. http://dx.doi.org/10.11591/ijeecs.v1.i3.pp480-489.
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<p>This paper presents an approach based on biogeography-based optimization (BBO) algorithm to solve the distribution network reconfiguration (DNR) problem for minimizing active power loss. Also it is demonstrated that with considering the nature of reconfiguration problem, among the intelligent algorithms, BBO approach could result the best performance. One of the remarkable advantages of this study is comparing seven different intelligent algorithms in solving network reconfiguration problem. This comparison, not only includes final fitness in optimization process, but also considers number of function evaluation (NFE). The effectiveness of the BBO method has been tested on two different distribution systems and the obtained simulation results are compared with Genetic algorithm (GA), Particle swarm optimization (PSO), Artificial bee colony (ABC), Gravitational search algorithm (GSA), Technical learning based optimization (TLBO) and Cuckoo algorithm (CA). The comparison results show that BBO approach can be an efficient and promising method for solving DNR problems.</p>
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Prayogo, Doddy, Jessica Chandra Sutanto, Hieronimus Enrico Suryo, and Samuel Eric. "A Comparative Study on Bio-Inspired Algorithms in Layout Optimization of Construction Site Facilities." Civil Engineering Dimension 20, no. 2 (2018): 102. http://dx.doi.org/10.9744/ced.20.2.102-110.
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A good arrangement of site layout on a construction project is a fundamental component of the project’s efficiency. Optimization on site layout is necessary in order to reduce the transportation cost of resources or personnel between facilities. Recently, the use of bio-inspired algorithms has received considerable critical attention in solving the engineering optimization problem. These methods have consistently provided better performance than traditional mathematical-based methods to a variety of engineering problems. This study compares the performance of particle swarm optimization (PSO), artificial bee colony (ABC), and symbiotic organisms search (SOS) algorithms in optimizing site layout planning problems. Three real-world case studies of layout optimization problems have been used in this study. The results show that SOS has a better performance in comparison to the other algorithms. Thus, this study provides useful insights to construction practitioners in the industry who are involved in dealing with optimization problems
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Jayaram, M. A. "Bio-Inspired Algorithms for Optimal Design of Trusses." IOP Conference Series: Earth and Environmental Science 982, no. 1 (2022): 012073. http://dx.doi.org/10.1088/1755-1315/982/1/012073.
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Abstract Trusses are one of the major civil engineering structural articulations that are studied for optimized design. However, application of bio-inspired algorithms for the design of planar trusses is found to be scanty. In this paper, four bio-inspired algorithms namely, Elitism based genetic algorithm (EBGA), Ant colony optimization (ACO), Artificial honey bee optimization (AHBO), and Particle swarm optimization (PSO) algorithms have been implemented for the optimization of size of the members of planar trusses. For this purpose, 4-planar trusses have been considered. The results show that the said algorithms vary marginally as far as the optimized weights are concerned. However, the differences are seen in terms of number of iterations required for convergence and standard deviation of weights. In this context, PSO and EBGA, converged quickly for all the four examples considered. Both the algorithms also showed lower values of standard deviation with respect to the optimized overall weight of the trusses.
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Ahmed Shaban, Awaz, and Ibrahim Mahmood Ibrahim. "Swarm intelligence algorithms: a survey of modifications and applications." International Journal of Scientific World 11, no. 1 (2025): 59–65. https://doi.org/10.14419/vhckcq86.
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Swarm Intelligence (SI) is a dynamic subfield of artificial intelligence that draws inspiration from the collective behaviors of natural systems such as ant colonies, bird flocks, and fish schools. This paper provides a comprehensive review of SI algorithms, examining their foundational principles, recent modifications, and applications across diverse domains. Prominent algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Bat Algorithm (BA) are analyzed alongside emerging approaches like Grey Wolf Optimizer (GWO), Zebra Optimization Algorithm (ZOA), and hybrid frameworks. A key focus is placed on algorithmic advancements, in-cluding adaptive inertia weights in PSO, pheromone update mechanisms in ACO, and hybridization techniques such as GWO-PSO and WOA-BA, addressing challenges related to convergence speed, scalability, and robustness against local optima. This review explores the practical applications of SI algorithms in engineering design, healthcare, robotics, logistics, education, and social media. Detailed performance comparisons reveal the strengths and limitations of each algorithm, supported by empirical results from benchmark problems such as the Traveling Salesman Problem (TSP), pressure vessel design optimization, and radiotherapy planning. Addi-tionally, the study highlights novel algorithms developed between 2020 and 2023, shedding light on their contributions to the field. The paper concludes by identifying current challenges, such as computational overhead and parameter sensitivity, and suggests future directions, including the integration of machine learning, lightweight adaptations for resource-constrained environments, and bio-inspired enhance-ments.
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S, Kalaivani, and Gopinath G. "MODIFIED BEE COLONY WITH BACTERIAL FORAGING OPTIMIZATION BASED HYBRID FEATURE SELECTION TECHNIQUE FOR INTRUSION DETECTION SYSTEM CLASSIFIER MODEL." ICTACT Journal on Soft Computing 10, no. 4 (2020): 2146–52. https://doi.org/10.21917/ijsc.2020.0305.
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Feature selection (FS) plays an essential role in creating machine learning models. The unrelated characteristics of the data disturb the precision of the perfection and upsurges the training time required to build the model. FS is a significant process in creating the Intrusion Detection System (IDS). In this document, we propose a technique for selecting container functions for IDS. To develop the performance capacity of the modified Artificial Bee Colony (ABC) procedure, a hybrid method is presented in which the swarm behavior of the Bacterial Foraging Optimization (BFO) algorithm is entered into the Modified Bee Colony (MBC) procedure to perform a local search. The proposed Hybrid MBC-BFO algorithm is analyzed with three different classification techniques which are separately analyzed to verify the proposed performance. The classification techniques are Artificial Neural Networks (ANN), Recursive Neural Network (ReNN), and Recurrent Neural Network (RNNs). The proposed algorithm has passed several algorithms for selecting advanced functions in terms of detection accuracy, recall, precision, false positive rate, and F-score.
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Hajjami, Imad El, and Bachir Benhala. "Radio-frequency circular integrated inductors sizing optimization using bio-inspired techniques." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6320. http://dx.doi.org/10.11591/ijece.v12i6.pp6320-6331.
Full textAbstract:
<span lang="EN-US">In this article, a comparative study is accomplished between three of the most used swarm intelligence (SI) techniques; namely artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) to carry out the optimal design of radio-frequency (</span><span lang="EN-US">RF) spiral inductors, the three algorithms are applied to the cost function of RF circular inductors for 180 nm beyond 2.50 GHz, the aim is to ensure optimal performance with less error in inductance, and a high-quality factor when compared to electromagnetic simulation. Simulation experiments are achieved and performances regarding convergence velocity, robustness, and computing time are checked. Also, this paper shows an impact study of technological parameters and geometric features on the inductance and the quality factor of the studied integrated inductor. The building method of constraints design with algorithms used has given good results and electromagnetic simulations are of good accuracy with an error of 2.31% and 4.15% on the quality factor and inductance respectively. The simulation shows that ACO provides more accuracy in circuit size and fewer errors than ABC and PSO, while PSO and ABC are better in terms of convergence velocity.</span>
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Kushwaha, Ajay Shriram. "Bio-inspired Algorithms for Optimization in Mechanical Design Systems." International Journal of Research in Modern Engineering & Emerging Technology 10, no. 12 (2022): 1–9. https://doi.org/10.63345/ijrmeet.org.v10.i12.1.
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Bio-inspired algorithms have emerged as powerful optimization techniques, offering robust, adaptive, and scalable solutions for complex engineering design problems. This manuscript investigates the application of four representative bio-inspired algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC)—to optimization challenges in mechanical design systems up to the year 2022. We present a detailed comparison of their performance on benchmark design tasks such as truss sizing, cam profile optimization, and mechanism parameter tuning. Statistical analysis over multiple independent runs evaluates convergence speed, solution quality, and robustness. The methodology section outlines the common framework, parameter settings, and evaluation metrics employed. Results indicate that while GA and PSO achieve high-quality solutions, ABC demonstrates superior robustness under noisy objective functions, and ACO excels in discrete combinatorial settings. The discussion interprets these findings in the context of mechanical design requirements, emphasizing trade‑offs between exploration and exploitation. Finally, we identify limitations and propose future research directions, including hybrid algorithm development, multi‑objective extensions, and real‑time optimization for adaptive manufacturing systems.
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Tjahjono, A., E. J. Wijayanti, D. Prayogo, and F. T. Wong. "A comparative study of several bio-inspired algorithms in cost optimization of cellular beams." IOP Conference Series: Earth and Environmental Science 907, no. 1 (2021): 012001. http://dx.doi.org/10.1088/1755-1315/907/1/012001.
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Abstract Castellated beams are commonly used in steel construction. This study will focus on castellated beams with circular-shaped openings, which are known as cellular beams. Cost optimization of cellular beams is needed to maintain cost efficiency. The optimization considers the selection of a root beam, the diameter of holes, and the total number of holes in the beam as the variables. Four metaheuristic algorithms are used to optimize the design, namely, the particle swarm optimization (PSO), differential evolution (DE), symbiotic organisms search (SOS), and artificial bee colony (ABC). A four-meter span beam with a 50 kN point live load in the middle of the beam and a 5 kN/m uniformly-distributed dead load are taken as the case study. The results indicate that the SOS algorithm yields the best optimization results in terms of the average, consistency, and convergence behavior with a 30 out of 30 success rates.
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Nisha Bhagirath, Swami, Vaibhav Bhatnagar, and Linesh Raja. "A comparative analysis of optimized CNN models using bio-inspired algorithms." Journal of Information and Optimization Sciences 46, no. 2 (2025): 531–40. https://doi.org/10.47974/jios-1957.
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Nitrogen is a macronutrient that is responsible for crop development. Accurate nitrogen prediction improves crop productivity. This study aims to enhance nitrogen prediction in wheat crops using convolutional neural networks (CNN) with bio-inspired algorithms. CNN, which is widely used for image classification, relies on carefully chosen parameters such as the number of layers, learning rate and kernel sizes to perform effectively. These parameters were optimized using five bio-inspired algorithms. Optimization algorithms aid in selecting the parameters of CNNs to make them more accurate and efficient. The algorithms are genetic algorithms grey wolf optimizer, particle swarm optimization, ant bee colony, and evolutionary algorithms. These findings indicate that employing bio-inspired optimization visibly increases the performance of CNN. The genetic algorithm achieved the best accuracy, making the model more accurate. Overall, these methods helped CNN model predict the nitrogen levels better. This methodology provides essential insights into precision farming, enabling the creation of more efficient nutrient management plans.
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Chandrakiran, Reddy Kasireddy Chandrakiran Reddy Kasireddy, Theja Kunta Sai Theja Kunta Sai, Jindam Srihitha Jindam Srihitha, and Madhu Dr.S Madhu Dr.S. "Optimizing Nature`s Blueprint: Comparative Study of Hybrid Algorithms for Terrain Features Extraction." International Journal for Research Trends and Innovation 8, no. 10 (2023): 317–24. https://doi.org/10.5281/zenodo.10101567.
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Extraction of terrain features is a significant conduct with many uses across numerous sectors. This research paper provides a comparative study of three nature-inspired algorithms for extraction of terrain features: the hybrid algorithm PBBO (Union of Biogeography-Based Optimization called as BBO and Particle Swarm Optimization called PSO), the fusion of Biogeography-Based Optimization and Artificial Bee Colony, and the third algorithm which is Hybrid Flower Pollination by Artificial Bees called as FPAB/Biogeography-Based Optimization. We are going to do a thorough comparative analysis to validate the performance of multiple algorithms in this research. We use criteria for evaluation like the kappa coefficient and accuracy to rate the algorithms' efficiency and predictability. The PBBO algorithm develops an optimized and reliable optimization strategy by combining the best qualities of PSO and BBO. Likewise, we combine BBO with ABC making it a hybrid algorithm, to improve the performance of BBO by applying ABC's clustering capabilities. We generate incredibly accurate results in satellite image classification by using flower pollination by artificial bees for data clustering and BBO for classification. The results of the comparative study of these three nature-inspired algorithms advance terrain analysis and support accurate and efficient decision-making spanning a range of applications.
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Duong, Minh, Thai Pham, Thang Nguyen, Anh Doan, and Hai Tran. "Determination of Optimal Location and Sizing of Solar Photovoltaic Distribution Generation Units in Radial Distribution Systems." Energies 12, no. 1 (2019): 174. http://dx.doi.org/10.3390/en12010174.
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This paper presents an effective biogeography-based optimization (BBO) for optimal location and sizing of solar photovoltaic distributed generation (PVDG) units to reduce power losses while maintaining voltage profile and voltage harmonic distortion at the limits. This applied algorithm was motivated by biogeography, that the study of the distribution of biological species through time and space. This technique is able to expand the searching space and retain good solution group at each generation. Therefore, the applied method can significantly improve performance. The effectiveness of the applied algorithm is validated by testing it on IEEE 33-bus and IEEE 69-bus radial distribution systems. The obtained results are compared with the genetic algorithm (GA), the particle swarm optimization algorithm (PSO) and the artificial bee colony algorithm (ABC). As a result, the applied algorithm offers better solution quality and accuracy with faster convergence.
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Benbraika, Mohamed Kamel, Okba Kraa, Yassine Himeur, Khaled Telli, Shadi Atalla, and Wathiq Mansoor. "Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications." Computers 13, no. 2 (2024): 44. http://dx.doi.org/10.3390/computers13020044.
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Device-to-Device (D2D) communication is an emerging technology that is vital for the future of cellular networks, including 5G and beyond. Its potential lies in enhancing system throughput, offloading the network core, and improving spectral efficiency. Therefore, optimizing resource and power allocation to reduce co-channel interference is crucial for harnessing these benefits. In this paper, we conduct a comparative study of meta-heuristic algorithms, employing Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Bee Life Algorithm (BLA), and a novel combination of matching techniques with BLA for joint channel and power allocation optimization. The simulation results highlight the effectiveness of bio-inspired algorithms in addressing these challenges. Moreover, the proposed amalgamation of the matching algorithm with BLA outperforms other meta-heuristic algorithms, namely, PSO, BLA, and GA, in terms of throughput, convergence speed, and achieving practical solutions.
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Saleh, Izzati, Nuradlin Borhan, and Wan Rahiman. "Smoothing RRT Path for Mobile Robot Navigation Using Bio-inspired Optimization Method." Pertanika Journal of Science and Technology 32, no. 5 (2024): 2327–42. http://dx.doi.org/10.47836/pjst.32.5.22.
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This research addresses the challenges of using the Rapidly Exploring Random Tree (RRT) algorithm as a mobile robot path planner. While RRT is known for its flexibility and wide applicability, it has limitations, including careful tuning, susceptibility to local minima, and generating jagged paths. The main objective is to improve the smoothness of RRT-generated trajectories and reduce significant path curvature. A novel approach is proposed to achieve these, integrating the RRT path planner with a modified version of the Whale Optimization Algorithm (RRT-WOA). The modified WOA algorithm incorporates parameter variation () specifically designed to optimize trajectory smoothness. Additionally, Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) instead of conventional splines for point interpolation further smoothes the generated paths. The modified WOA algorithm is thoroughly evaluated through a comprehensive comparative analysis, outperforming other popular population-based optimization algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Firefly Algorithm (FA) in terms of optimization time, trajectory smoothness, and improvement from the initial guess. This research contributes a refined trajectory planning approach and highlights the competitive advantage of the modified WOA algorithm in achieving smoother and more efficient trajectories compared to existing methods.
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Larabi-Marie-Sainte, Souad, Reham Alskireen, and Sawsan Alhalawani. "Emerging Applications of Bio-Inspired Algorithms in Image Segmentation." Electronics 10, no. 24 (2021): 3116. http://dx.doi.org/10.3390/electronics10243116.
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Image processing is one example of digital media. It consists of a set of operations to handle an image. Image segmentation is among its main important operations. It involves dividing the image into several parts or regions to extract vital information or identify relevant objects. Many techniques of artificial intelligence, including bio-inspired algorithms, have been used in this regard. This article collected the state-of-the-art studies presenting image-segmentation techniques combined with four bio-inspired algorithms including particle swarm optimization (PSO), genetic algorithms (GA), ant colony optimization (ACO), and artificial bee colonies (ABC). This research work aimed at showing the importance of image segmentation and its combination with these algorithms. This article provides insights on how these algorithms are adapted to image-segmentation combinatorial problems, which assist researchers to start the first hands-on application. It also discusses their setting parameters and the highly used algorithms such as PSO, GA, ACO, and ABC. The article presents new research directions in image segmentation based on bio-inspired algorithms.
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Zarrin, Saeed, Ahmad Mohammadi, and Mehdi Zeynali. "Predicting Audit Failure Using Metaheuristic Algorithms." Management Strategies and Engineering Sciences 6, no. 3 (2024): 20–31. https://doi.org/10.61838/msesj.6.3.2.
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The aim of the present study is to predict audit failure using metaheuristic algorithms in companies listed on the Tehran Stock Exchange. To achieve this objective, 1,848 firm-year observations (154 companies over 12 years) were collected from the annual financial reports of companies listed on the Tehran Stock Exchange during the period from 2011 to 2022. In this study, four metaheuristic algorithms (including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Bee Colony Optimization (BCO)) were utilized, as well as two methods for selecting the final research variables (the two-sample t-test and the forward stepwise selection method) to create the model. The results from the metaheuristic algorithms indicate that the overall accuracy of the GA, PSO, ACO, and BCO algorithms is 95.3%, 94.5%, 90.6%, and 92.8%, respectively, demonstrating the superiority of the Genetic Algorithm (GA) compared to other metaheuristic algorithms. Furthermore, the overall results from the variable selection methods indicate the efficiency of the stepwise method. Therefore, in companies listed on the Tehran Stock Exchange, the stepwise method and the Genetic Algorithm (GA) provide the most efficient model for predicting audit failure.
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Muslim, Much Aziz, Yosza Dasril, Muhammad Sam'an, and Yahya Nur Ifriza. "An improved light gradient boosting machine algorithm based on swarm algorithms for predicting loan default of peer-to-peer lending." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (2022): 1002–11. https://doi.org/10.11591/ijeecs.v28.i2.pp1002-1011.
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Internet finance and big data technology are booming in the world. The launch of peer to peer (P2P) lending platforms is a sign and a great opportunity for entrepreneurs to easily increase their capital injection. However, this great opportunity has a high risk of impacting the sustainability and security development of the platform. One way to minimize loan risk is to predict the possibility of loan default. Hence, this study aims to find the best predictive model for predicting loan default of P2P Lending Club dataset. An improved light gradient boosting machine (LightGBM) via features selection by using swarm algorithms i.e. Ant colony optimization (ACO) and bee colony optimization (BCO) to the prediction analysis process. The best feature selection process is selected 6 out of 18 features. The synthetic minority oversampling technique (SMOTE) method is also provided to solve the unbalance class problem in the dataset, then a series of operations such as data cleaning and dimension reduction are performed. The experimental results prove that the LightGBM algorithm has been successfully improved. This success is shown by the prediction accuracy of LightGBM+ACO is 95.64%, LighGBM+BCO is 94.70% and LightGBM is 94.38%. This success also demonstrates outstanding performance in predicting loan default and strong generalizations.
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C, Manjunatha Swamy, and Dr S. Meenakshi Sundaram. "A Survey of Bio Inspired Algorithms for Web Information Extraction and Optimization for Big Data Analytics." International Journal of Engineering and Advanced Technology 10, no. 2 (2020): 56–60. http://dx.doi.org/10.35940/ijeat.b2011.1210220.
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Information extraction is systematic process of extracting structured information from documents which has both unstructured and semi structured data set. Data available over the web is unstructured which is processed and delivered that may be challenging due to massive data over web. Bigdata analytics approach is used in the computation field where massive data is managed and processed as information. Data from various sources like industries, institutes are processed using algorithms in efficient means employing web of things or Internet of things used to mine such a large data. Bio inspired algorithms have evolved from application of heuristic approaches to meta-heuristic and hyper-heuristic methodologies. Bio inspired techniques are categorized into human inspired algorithms, Swarm Intelligence algorithms, evolutionary algorithms and ecology based algorithms. Genetic algorithms are purely heuristic in nature and are employed for computation and extracting information and from big data. This improves the computation speed effectively for extracting web related information as evolutionary algorithm resolves information extraction problems. The Ant colony and Particle Swarm Intelligence algorithms are of meta-heuristic in nature. The Cuckoo search, Artificial Bee Colony, Firefly algorithm and Bat algorithms are of hyper heuristic in nature i.e., they employ a combination of methods. Web information extraction using bio inspired concepts and genetic operators increases efficiency, capability to search particular information in massive data in web. Some of the tools that are available for data extraction and mining are DataMelt, Apache Mahout, Weka, Orange and Rapid Miner for enhancing web data extraction efficiency. This survey on bio inspired methodologies can be extended to parameter tuning and controlling is another big strategy that can be implemented, in addition to convergence speed up.
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Manjunatha, Swamy C., and Meenakshi Sundaram S. "A Survey of Bio Inspired Algorithms for Web Information Extraction and Optimization for Big Data Analytics." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 2 (2020): 56–58. https://doi.org/10.35940/ijeat.B2011.1210220.
Full textAbstract:
Information extraction is systematic process of extracting structured information from documents which has both unstructured and semi structured data set. Data available over the web is unstructured which is processed and delivered that may be challenging due to massive data over web. Bigdata analytics approach is used in the computation field where massive data is managed and processed as information. Data from various sources like industries, institutes are processed using algorithms in efficient means employing web of things or Internet of things used to mine such a large data. Bio inspired algorithms have evolved from application of heuristic approaches to meta-heuristic and hyper-heuristic methodologies. Bio inspired techniques are categorized into human inspired algorithms, Swarm Intelligence algorithms, evolutionary algorithms and ecology based algorithms. Genetic algorithms are purely heuristic in nature and are employed for computation and extracting information and from big data. This improves the computation speed effectively for extracting web related information as evolutionary algorithm resolves information extraction problems. The Ant colony and Particle Swarm Intelligence algorithms are of meta-heuristic in nature. The Cuckoo search, Artificial Bee Colony, Firefly algorithm and Bat algorithms are of hyper heuristic in nature i.e., they employ a combination of methods. Web information extraction using bio inspired concepts and genetic operators increases efficiency, capability to search particular information in massive data in web. Some of the tools that are available for data extraction and mining are DataMelt, Apache Mahout, Weka, Orange and Rapid Miner for enhancing web data extraction efficiency. This survey on bio inspired methodologies can be extended to parameter tuning and controlling is another big strategy that can be implemented, in addition to convergence speed up.
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Thanoon, Mohammad A., Sohaib R. Awad, and Ismael Kh Abdullah. "LQR controller design for stabilization of non-linear DIP system based on ABC algorithm." Eastern-European Journal of Enterprise Technologies 2, no. 2 (122) (2023): 36–44. http://dx.doi.org/10.15587/1729-4061.2023.275657.
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Inverted pendulum systems, such as double or single, rotational or translational inverted pendulums are non-linear and unstable, which have been the most dominant approaches for control systems. The double inverted pendulum is one kind of a non-linear, unstable system, multivariable, and strong coupling with a wide range of control methods. To model these types of systems, many techniques have been proposed so that motivating researchers to come up with new innovative solutions. The Linear Quadratic Regulator (LQR) controller has been a common controller used in this field. Meanwhile, the Artificial Bee Colony (ABC) technique has become an alternative solution for employing Bee Swarm Intelligence algorithms. The research solutions of the artificial bee colony algorithm in the literature can be beneficial, however, the utilization of discovered sources of food is ineffective. Thus, in this paper, we aim to provide a double inverted pendulum system for stabilization by selecting linear quadratic regulator parameters using a bio-inspired optimization methodology of artificial bee colony and weight matrices Q and R. The results show that when the artificial bee colony algorithm is applied to a linear quadratic regulator controller, it gains the capacity to autonomously tune itself in an online process. To further demonstrate the efficiency and viability of the suggested methodology, simulations have been performed and compared to conventional linear quadratic regulator controllers. The obtained results demonstrate that employing artificial intelligence (AI) together with the proposed controller outperforms the conventional linear quadratic regulator controllers by more than 50 % in transient response and improved time response and stability performance
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Mohammad, A. Thanoon, R. Awad Sohaib, and Kh. Abdullah Ismael. "LQR controller design for stabilization of non-linear DIP system based on ABC algorithm." Eastern-European Journal of Enterprise Technologies 2, no. 2(122) (2023): 36–44. https://doi.org/10.15587/1729-4061.2023.275657.
Full textAbstract:
Inverted pendulum systems, such as double or single, rotational or translational inverted pendulums are non-linear and unstable, which have been the most dominant approaches for control systems. The double inverted pendulum is one kind of a non-linear, unstable system, multivariable, and strong coupling with a wide range of control methods. To model these types of systems, many techniques have been proposed so that motivating researchers to come up with new innovative solutions. The Linear Quadratic Regulator (LQR) controller has been a common controller used in this field. Meanwhile, the Artificial Bee Colony (ABC) technique has become an alternative solution for employing Bee Swarm Intelligence algorithms. The research solutions of the artificial bee colony algorithm in the literature can be beneficial, however, the utilization of discovered sources of food is ineffective. Thus, in this paper, we aim to provide a double inverted pendulum system for stabilization by selecting linear quadratic regulator parameters using a bio-inspired optimization methodology of artificial bee colony and weight matrices Q and R. The results show that when the artificial bee colony algorithm is applied to a linear quadratic regulator controller, it gains the capacity to autonomously tune itself in an online process. To further demonstrate the efficiency and viability of the suggested methodology, simulations have been performed and compared to conventional linear quadratic regulator controllers. The obtained results demonstrate that employing artificial intelligence (AI) together with the proposed controller outperforms the conventional linear quadratic regulator controllers by more than 50 % in transient response and improved time response and stability performance
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Hong, Yang, Yuexia Zhang, and Shaoshuai Fan. "Research on Discrete Artificial Bee Colony Cache Strategy of UAV Edge Network." Processes 10, no. 9 (2022): 1838. http://dx.doi.org/10.3390/pr10091838.
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Unmanned aerial vehicle edge networks (UENs) can reduce the cache load of the core network and improve system performance to provide users with efficient content services. However, the time-varying characteristics of content popularity in UENs lead to a low accuracy of popularity prediction, and the capacity limitations of wireless channel conditions lead to a lower cache hit rate than the rates of traditional fiber-optic-based cache strategies. Therefore, this paper proposes the discrete artificial bee colony cache strategy of UENs (DABCCSU). First, the information–dynamics–dissemination model of UENs (IDDMU) is established to deduce the coupling relationship between the channel capacity and the service probability in IDDMU. The influence of the service probability change on the content dissemination process is discussed, and the content popularity in UENs is predicted by the state iteration matrix. Then, the discrete artificial bee colony cache (DABCC) optimization algorithm is proposed. The action function of the artificial bee colony is designed as a random action based on the historical cache strategy. The discrete cache strategy is used as an optimization variable, and the popularity prediction result obtained by IDDMU is used to maximize the cache hit rate. DABCC provides the optimal cache strategy for the UENs, and effectively improves the cache hit rate. The simulation result shows that the accuracy of DABCCSU in content popularity prediction is more than 90%, which achieves a good prediction effect. In terms of cache performance, the average cache hit rate of DABCCSU is 91.62%, which is better than the 51.09% of the Least Recently Used (LRU) strategy, 89.27% of the Greedy Algorithm (GA) and 54.26% of Binary Particle Swarm Optimization (BPSO). In addition, the cache hit rate of DABCCSU under different cache capacities is better than that of LRU, GA, and BPSO, showing a relatively stable performance. It shows that DABCCSU can achieve excellent content popularity prediction, and it can also maximize the cache hit rate under limited communication resources and cache resources to provide UENs with the optimal content cache strategy, and provides users with high-quality content services.
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Muslim, Much Aziz, Yosza Dasril, Muhammad Sam'an, and Yahya Nur Ifriza. "An improved light gradient boosting machine algorithm based on swarm algorithms for predicting loan default of peer-to-peer lending." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (2022): 1002. http://dx.doi.org/10.11591/ijeecs.v28.i2.pp1002-1011.
Full textAbstract:
Internet finance and big data technology are booming in the world. The launch of peer to peer (P2P) lending platforms is a sign and a great opportunity for entrepreneurs to easily increase their capital injection. However, this great opportunity has a high risk of impacting the sustainability and security development of the platform. One way to minimize loan risk is to predict the possibility of loan default. Hence, this study aims to find the best predictive model for predicting loan default of P2P Lending Club dataset. An improved light gradient boosting machine (LightGBM) via features selection by using swarm algorithms i.e. Ant colony optimization (ACO) and bee colony optimization (BCO) to the prediction analysis process. The best feature selection process is selected 6 out of 18 features. The synthetic minority oversampling technique (SMOTE) method is also provided to solve the unbalance class problem in the dataset, then a series of operations such as data cleaning and dimension reduction are performed. The experimental results prove that the LightGBM algorithm has been successfully improved. This success is shown by the prediction accuracy of LightGBM+ACO is 95.64%, LighGBM+BCO is 94.70% and LightGBM is 94.38%. This success also demonstrates outstanding performance in predicting loan default and strong generalizations.
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Prithi, S., and S. Sumathi. "A technical research survey on bio-inspired intelligent optimization grouping algorithms for finite state automata in intrusion detection system." International Journal of Engineering, Science and Technology 16, no. 2 (2024): 48–67. http://dx.doi.org/10.4314/ijest.v16i2.6.
Full textAbstract:
Network Security plays an essential role in the modern world. Current network services mainly rely on processing of payload in packets. Deep Packet Inspection (DPI) is a key factor in examining the packet payload which uses the signatures to identify the packet that carries any viruses, worms, malicious traffic, unauthorized access and attacks. DPI uses regular expression matching as a core operator to examine the packet payload. Finite State Automata (FSA) are natural representations for regular expression. FSA is usually too large to be constructed or deployed and has a huge overhead. Finite State Automata frequently leads to state explosion problem which require more storage space, high bandwidth and more computational time. To overcome this problem, Intelligent Optimization Grouping Algorithms (IOGA) can be used to distribute the regular expressions into various groups and for each group the Deterministic Finite Automata (DFA) are built independently. Grouping the regular expression efficiently solves the state explosion problem by achieving large-scale best tradeoff among the memory utilization and computational time. This paper reviews the various Intelligent Optimization Grouping Algorithms like Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bacterial Foraging Optimization, Artificial Bee Colony Algorithm, Biogeography Based Optimization, Cuckoo Search, Firefly Algorithm, Bat Algorithm and Flower Plant Optimization. The discussions states that by effectively using these grouping algorithms along with finite state automata can reduce the number of states by solving the state explosion blow up problem, providing a balance between the memory consumption, number of groups and provide faster convergence.
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Ouadfel, Salima, and Abdelmalik Taleb-Ahmed. "Performance Study of Harmony Search Algorithm for Multilevel Thresholding." Journal of Intelligent Systems 25, no. 4 (2016): 473–513. http://dx.doi.org/10.1515/jisys-2014-0147.
Full textAbstract:
AbstractThresholding is the easiest method for image segmentation. Bi-level thresholding is used to create binary images, while multilevel thresholding determines multiple thresholds, which divide the pixels into multiple regions. Most of the bi-level thresholding methods are easily extendable to multilevel thresholding. However, the computational time will increase with the increase in the number of thresholds. To solve this problem, many researchers have used different bio-inspired metaheuristics to handle the multilevel thresholding problem. In this paper, optimal thresholds for multilevel thresholding in an image are selected by maximizing three criteria: Between-class variance, Kapur and Tsallis entropy using harmony search (HS) algorithm. The HS algorithm is an evolutionary algorithm inspired from the individual improvisation process of the musicians in order to get a better harmony in jazz music. The proposed algorithm has been tested on a standard set of images from the Berkeley Segmentation Dataset. The results are then compared with that of genetic algorithm (GA), particle swarm optimization (PSO), bacterial foraging optimization (BFO), and artificial bee colony algorithm (ABC). Results have been analyzed both qualitatively and quantitatively using the fitness value and the two popular performance measures: SSIM and FSIM indices. Experimental results have validated the efficiency of the HS algorithm and its robustness against GA, PSO, and BFO algorithms. Comparison with the well-known metaheuristic ABC algorithm indicates the equal performance for all images when the number of thresholds M is equal to two, three, four, and five. Furthermore, ABC has shown to be the most stable when the dimension of the problem is too high.
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Rathipriya, R., and K. Thangavel. "A Discrete Artificial Bees Colony Inspired Biclustering Algorithm." International Journal of Swarm Intelligence Research 3, no. 1 (2012): 30–42. http://dx.doi.org/10.4018/jsir.2012010102.
Full textAbstract:
Biclustering methods are the potential data mining technique that has been suggested to identify local patterns in the data. Biclustering algorithms are used for mining the web usage data which can determine a group of users which are correlated under a subset of pages of a web site. Recently, many blistering methods based on meta-heuristics have been proposed. Most use the Mean Squared Residue as merit function but interesting and relevant patterns such as shifting and scaling patterns may not be detected using this measure. However, it is important to discover this type of pattern since commonly the web users can present a similar behavior although their interest levels vary in different ranges or magnitudes. In this paper a new correlation based fitness function is designed to extract shifting and scaling browsing patterns. The proposed work uses a discrete version of Artificial Bee Colony optimization algorithm for biclustering of web usage data to produce optimal biclusters (i.e., highly correlated biclusters). It’s demonstrated on real dataset and its results show that proposed approach can find significant biclusters of high quality and has better convergence performance than Binary Particle Swarm Optimization (BPSO).