To see the other types of publications on this topic, follow the link: Swarm intelligence algorithms.
Journal articles on the topic 'Swarm intelligence algorithms'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Swarm intelligence algorithms.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Ahmad Shaban, Awaz, Jayson A. Dela Fuente, Merdin Shamal Salih, and Resen Ismail Ali. "Review of Swarm Intelligence for Solving Symmetric Traveling Salesman Problem." Qubahan Academic Journal 3, no. 2 (2023): 10–27. http://dx.doi.org/10.48161/qaj.v3n2a141.
Full textAbstract:
Swarm Intelligence algorithms are computational intelligence algorithms inspired from the collective behavior of real swarms such as ant colony, fish school, bee colony, bat swarm, and other swarms in the nature. Swarm Intelligence algorithms are used to obtain the optimal solution for NP-Hard problems that are strongly believed that their optimal solution cannot be found in an optimal bounded time. Travels Salesman Problem (TSP) is an NP-Hard problem in which a salesman wants to visit all cities and return to the start city in an optimal time. In this article we are applying most efficient heuristic based Swarm Intelligence algorithms which are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bat algorithm (BA), and Ant Colony Optimization (ACO) algorithm to find a best solution for TSP which is one of the most well-known NP-Hard problems in computational optimization. Results are given for different TSP problems comparing the best tours founds by BA, ABC, PSO and ACO.
2
Issayeva, G. B. Issayeva, M. S. Ibraev, A. K. Koishybekova, et al. "SWARM INTELLIGENCE." EurasianUnionScientists 6, no. 8(77) (2020): 9–13. http://dx.doi.org/10.31618/esu.2413-9335.2020.6.77.998.
Full textAbstract:
This report investigates this discipline that deals with natural and artificial systems. In the past few years there has been a lot of research on the application of swarm intelligence. A large number of algorithms have been used in different spheres of our life. In this paper we give an overview of this research area. We identify one of the algorithms of swarm intelligence systems and we show how it is used to solve problems. In other words, we present Bee Algorithms, a general framework in which most swarm intelligence algorithms can be placed. After that, we give an extensive solution of existing problem, discussing algorithm’s advantages and disadvantages. We conclude with an overview of future research directions that we consider important for the further development of this field.
3
Shi, Yuhui. "Developmental Swarm Intelligence." International Journal of Swarm Intelligence Research 5, no. 1 (2014): 36–54. http://dx.doi.org/10.4018/ijsir.2014010102.
Full textAbstract:
In this paper, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from developmental learning perspective. Finally, a framework of a developmental swarm intelligence algorithm is given to help understand developmental swarm intelligence algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm.
4
Zangana, Hewa Majeed, Zina Bibo Sallow, Mohammed Hazim Alkawaz, and Marwan Omar. "Unveiling the Collective Wisdom: A Review of Swarm Intelligence in Problem Solving and Optimization." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 9, no. 2 (2024): 101–10. http://dx.doi.org/10.25139/inform.v9i2.7934.
Full textAbstract:
Swarm intelligence, inspired by the collective behaviour of natural swarms and social insects, represents a powerful paradigm for solving complex optimization and decision-making problems. In this review paper, we provide an overview of swarm intelligence, covering its definition, principles, algorithms, applications, performance evaluation, challenges, and future directions. We discuss prominent swarm intelligence algorithms, such as ant colony optimization, particle swarm optimization, and artificial bee colony algorithm, highlighting their applications in optimization, robotics, data mining, telecommunications, and other domains. Furthermore, we examine the performance evaluation and comparative studies of swarm intelligence algorithms, emphasizing the importance of metrics, comparative analysis, and case studies in assessing algorithmic effectiveness and practical applicability. Challenges facing swarm intelligence research, such as scalability, robustness, and interpretability, are identified, and potential future directions for addressing these challenges and advancing the field are outlined. In conclusion, swarm intelligence offers a versatile and effective approach to solving a wide range of optimization and decision-making problems, with applications spanning diverse domains and industries. By addressing current challenges, exploring new research directions, and embracing interdisciplinary collaborations, swarm intelligence researchers can continue to innovate and develop cutting-edge algorithms with profound implications for science, engineering, and society.
5
Schranz, M., and M. Sende. "Modeling Swarm Intelligence Algorithms for CPS Swarms." ACM SIGAda Ada Letters 40, no. 1 (2020): 64–73. http://dx.doi.org/10.1145/3431235.3431240.
Full text
6
Tang, Kezong, and Chengjian Meng. "Particle Swarm Optimization Algorithm Using Velocity Pausing and Adaptive Strategy." Symmetry 16, no. 6 (2024): 661. http://dx.doi.org/10.3390/sym16060661.
Full textAbstract:
Particle swarm optimization (PSO) as a swarm intelligence-based optimization algorithm has been widely applied to solve various real-world optimization problems. However, traditional PSO algorithms encounter issues such as premature convergence and an imbalance between global exploration and local exploitation capabilities when dealing with complex optimization tasks. To address these shortcomings, an enhanced PSO algorithm incorporating velocity pausing and adaptive strategies is proposed. By leveraging the search characteristics of velocity pausing and the terminal replacement mechanism, the problem of premature convergence inherent in standard PSO algorithms is mitigated. The algorithm further refines and controls the search space of the particle swarm through time-varying inertia coefficients, symmetric cooperative swarms concepts, and adaptive strategies, balancing global search and local exploitation. The performance of VASPSO was validated on 29 standard functions from Cec2017, comparing it against five PSO variants and seven swarm intelligence algorithms. Experimental results demonstrate that VASPSO exhibits considerable competitiveness when compared with 12 algorithms. The relevant code can be found on our project homepage.
7
Et. al., Vijaya Bhaskar K,. "Modern Swarm Intelligence based Algorithms for Solving Optimal Power Flow Problem in a Regulated Power System Framework." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 1786–93. http://dx.doi.org/10.17762/turcomat.v12i2.1515.
Full textAbstract:
This paper presents artificial swarm intelligent based algorithms viz., Firefly Algorithm (FFA), Dragonfly Algorithm (DA) and Moth Swarm Algorithm (MSA) to take care of the issues related to optimal power flow (OPF) problem in a power system network. The optimal values of various decision variables obtained by swarm intelligent based algorithms can optimize various objective function of OPF problem. This article is focused with four objectives such as minimization of total fuel cost (TFC) and total active power loss (TAPL); improvisation of total voltage profile (TVD) and voltage stability index (VSI). The effectiveness of various swam intelligent algorithms are investigated on a standard IEEE-30 bus. The performance of distinct algorithms is compared with statistical measures and convergence characteristics.
8
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.
Full textAbstract:
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).
9
Xu, Minghai, Li Cao, Dongwan Lu, Zhongyi Hu, and Yinggao Yue. "Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization." Biomimetics 8, no. 2 (2023): 235. http://dx.doi.org/10.3390/biomimetics8020235.
Full textAbstract:
Image processing technology has always been a hot and difficult topic in the field of artificial intelligence. With the rise and development of machine learning and deep learning methods, swarm intelligence algorithms have become a hot research direction, and combining image processing technology with swarm intelligence algorithms has become a new and effective improvement method. Swarm intelligence algorithm refers to an intelligent computing method formed by simulating the evolutionary laws, behavior characteristics, and thinking patterns of insects, birds, natural phenomena, and other biological populations. It has efficient and parallel global optimization capabilities and strong optimization performance. In this paper, the ant colony algorithm, particle swarm optimization algorithm, sparrow search algorithm, bat algorithm, thimble colony algorithm, and other swarm intelligent optimization algorithms are deeply studied. The model, features, improvement strategies, and application fields of the algorithm in image processing, such as image segmentation, image matching, image classification, image feature extraction, and image edge detection, are comprehensively reviewed. The theoretical research, improvement strategies, and application research of image processing are comprehensively analyzed and compared. Combined with the current literature, the improvement methods of the above algorithms and the comprehensive improvement and application of image processing technology are analyzed and summarized. The representative algorithms of the swarm intelligence algorithm combined with image segmentation technology are extracted for list analysis and summary. Then, the unified framework, common characteristics, different differences of the swarm intelligence algorithm are summarized, existing problems are raised, and finally, the future trend is projected.
10
Yang, Xin-She. "Diversity and Mechanisms in Swarm Intelligence." International Journal of Swarm Intelligence Research 5, no. 2 (2014): 1–12. http://dx.doi.org/10.4018/ijsir.2014040101.
Full textAbstract:
Swarm intelligence based algorithms such as particle swarm optimization have become popular in the last two decades. Various new algorithms such as cuckoo search and bat algorithm also show promising efficiency. In all these algorithms, it is essential to maintain the balance of exploration and exploitation by controlling directly and indirectly the diversity of the population. Different algorithms may use different mechanisms to control such diversity. In this review paper, the author reviews and analyzes the roles of diversity and relevant mechanisms in swarm intelligence. The author also discuss parameter tuning and parameter control. In addition, the author highlights some key open questions in swarm intelligence.
11
Peng, Qiang, Renjun Zhan, Husheng Wu, and Meimei Shi. "Comparative Study of Wolf Pack Algorithm and Artificial Bee Colony Algorithm." International Journal of Swarm Intelligence Research 15, no. 1 (2024): 1–24. http://dx.doi.org/10.4018/ijsir.352061.
Full textAbstract:
Swarm intelligence optimization algorithms have been widely used in the fields of machine learning, process control and engineering prediction, among which common algorithms include ant colony algorithm (ACO), artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). Wolf pack algorithm (WPA) as a newer swarm intelligence optimization algorithm has many similarities with ABC. In this paper, the basic principles, algorithm implementation processes, and related improvement strategies of these two algorithms were described in detail; A comparative analysis of their performance in solving different feature-based standard CEC test functions was conducted, with a focus on optimization ability and convergence speed, re-validating the unique characteristics of these two algorithms in searching. In the end, the future development trend and prospect of intelligent optimization algorithms was discussed, which is of great reference significance for the research and application of swarm intelligence optimization algorithms.
12
AlDeeb, Bashar Abedal Mohdi, Norita Md Norwawi, and Mohammed A. Al-Betar. "A Survey on Intelligent Water Drop Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 10 (2014): 5075–84. http://dx.doi.org/10.24297/ijct.v13i10.2329.
Full textAbstract:
In the optimization areas, there are different algorithms that have been applied such as swarm intelligence algorithms. The researchers have found different algorithms by simulating the behaviors of various swarms of insects and animals such as fishes, bees, and ants. The intelligent water drops algorithm is one of the recently developed algorithms in the swarm intelligence field; this algorithm mimicked the dynamic of river systems. The natural water drops used to develop Intelligent Water Drop (IWD) algorithm. Therefore, the mechanisms that happen in rivers have inspired the researchers mainly to create new algorithms. IWD is a population-based algorithm where each drop represents a solution and the sharing between the drops during the search lead to a better drops (or solutions). This paper presents recent developments of the IWD algorithms in terms of theory and application. This paper concludes many of research directions that are necessary for the future of IWD algorithm.
13
Yazdani, Danial, Alireza Sepas-Moghaddam, Atabak Dehban, and Nuno Horta. "A Novel Approach for Optimization in Dynamic Environments Based on Modified Artificial Fish Swarm Algorithm." International Journal of Computational Intelligence and Applications 15, no. 02 (2016): 1650010. http://dx.doi.org/10.1142/s1469026816500103.
Full textAbstract:
Swarm intelligence algorithms are amongst the most efficient approaches toward solving optimization problems. Up to now, most of swarm intelligence approaches have been proposed for optimization in static environments. However, numerous real-world problems are dynamic which could not be solved using static approaches. In this paper, a novel approach based on artificial fish swarm algorithm (AFSA) has been proposed for optimization in dynamic environments in which changes in the problem space occur in discrete intervals. The proposed algorithm can quickly find the peaks in the problem space and track them after an environment change. In this algorithm, artificial fish swarms are responsible for finding and tracking peaks and several behaviors and mechanisms are employed to cope with the dynamic environment. Extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of tested dynamic environments modeled by moving peaks benchmark.
14
Kaushal, Payal, Meenu Khurana, and K. R. Ramkumar. "A Systematic Review of Swarm Intelligence Algorithms to Perform Routing for VANETs Communication." ECS Transactions 107, no. 1 (2022): 5027–35. http://dx.doi.org/10.1149/10701.5027ecst.
Full textAbstract:
The performance of Vehicular Adhoc Networks (VANETs), the underlying technology for intelligent vehicles, has shown improvement with the application of swarm-based algorithms for routing. Swarm Intelligence is a self-intelligence group of similar agents functioning on distribution, flexibility, and communication among the agents. The multiple problems occurring in the modern communication systems, including VANETs, have been tried to be resolved by the application of swarm intelligence algorithms viz, Genetic Algorithms (GA), Differential Evolution (DE), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuck-oo Search Algorithm (CSA), Glowworm Swarm Optimization (GSO), etc., have been proposed in the literature. This paper provides a comparative analysis of operations of swarm-based algorithms. Points like their operations, basic entities, advantages, disadvantages, and applications are discussed.
15
Boursianis, Achilles D., Maria S. Papadopoulou, Marco Salucci, et al. "Emerging Swarm Intelligence Algorithms and Their Applications in Antenna Design: The GWO, WOA, and SSA Optimizers." Applied Sciences 11, no. 18 (2021): 8330. http://dx.doi.org/10.3390/app11188330.
Full textAbstract:
Swarm Intelligence (SI) Algorithms imitate the collective behavior of various swarms or groups in nature. In this work, three representative examples of SI algorithms have been selected and thoroughly described, namely the Grey Wolf Optimizer (GWO), the Whale Optimization Algorithm (WOA), and the Salp Swarm Algorithm (SSA). Firstly, the selected SI algorithms are reviewed in the literature, specifically for optimization problems in antenna design. Secondly, a comparative study is performed against widely known test functions. Thirdly, such SI algorithms are applied to the synthesis of linear antenna arrays for optimizing the peak sidelobe level (pSLL). Numerical tests show that the WOA outperforms the GWO and the SSA algorithms, as well as the well-known Particle Swarm Optimizer (PSO), in terms of average ranking. Finally, the WOA is exploited for solving a more computational complex problem concerned with the synthesis of an dual-band aperture-coupled E-shaped antenna operating in the 5G frequency bands.
16
Chen, Hanning, Yunlong Zhu, Kunyuan Hu, and Xiaoxian He. "Hierarchical Swarm Model: A New Approach to Optimization." Discrete Dynamics in Nature and Society 2010 (2010): 1–30. http://dx.doi.org/10.1155/2010/379649.
Full textAbstract:
This paper presents a novel optimization model called hierarchical swarm optimization (HSO), which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named ), based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms.
17
A.I., Kupin, and Kosei M.P. "Analysis of swarm intelligence algorithms." System technologies 3, no. 152 (2024): 69–80. http://dx.doi.org/10.34185/1562-9945-3-152-2024-07.
Full textAbstract:
This paper conducts a comprehensive review of swarm intelligence algorithms, highlighting the significant potential and development prospects of multi-agent systems and swarm intelligence. It underscores the ongoing research activity in this field and the continuous expansion of application areas. By examining various studies and publica-tions, the paper concludes the importance of integrating approaches from different scien-tific disciplines to tackle diverse and complex problems using swarm intelligence. Future research is aimed at providing a more detailed analysis and comparison of various swarm intelligence algorithms across different application domains, as well as exploring their integration with other artificial intelligence methods. This work points towards the growing relevance of swarm intelligence in solving real-world problems, showcasing its versatility and effectiveness across multiple sectors, including military, agriculture, search and rescue, and environmental monitoring.
18
Kravchuk, O. A., and V. D. Samoilov. "Application of Artificial Intelligence for Swarm Systems Managment." Èlektronnoe modelirovanie 46, no. 6 (2024): 29–42. https://doi.org/10.15407/emodel.46.06.029.
Full textAbstract:
The use of artificial intelligence methods in swarm systems of unmanned aerial vehicles is studied. The basic artificial intelligence (AI) algorithms that ensure adaptive and intelligent swarm behavior are presented, and their application in real-world scenarios is analyzed. Par-ticular attention is paid to the current problems and limitations of swarm systems, such as sys-tem scalability, communication reliability, adaptation to a dynamic environment, etc. Promis-ing directions for the development of AI-based algorithms aimed at increasing the efficiency, stability, and survivability of swarms are outlined.
19
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.
Full textAbstract:
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 .
20
Khairuddin, Ismail Mohd, Amira Sarayati Ahmad Dahalan, Amar Faiz Zainal Abidin, et al. "Modeling and Simulation of Swarm Intelligence Algorithms for Parameters Tuning of PID Controller in Industrial Couple Tank System." Advanced Materials Research 903 (February 2014): 321–26. http://dx.doi.org/10.4028/www.scientific.net/amr.903.321.
Full textAbstract:
Industrial tank system is widely used in consumer liquid processing and chemical processing industry. In liquid-based product manufacturing system, one of the main components consists of an industrial tank. This paper explores the applications of two swarm intelligence algorithms in optimizing the PID controller parameters. These swarm intelligence algorithms are Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). Each agent of the swarm intelligence will represent a possible solution of the problem where each dimension corresponds to the PID controllers parameters. Result obtained shows that there are potential in improving these algorithms to replace the conventional way of obtaining PID controllers parameters
21
Liang, Jianhui, Lifang Wang, and Miao Ma. "An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization." Biomimetics 8, no. 2 (2023): 210. http://dx.doi.org/10.3390/biomimetics8020210.
Full textAbstract:
With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper, which provides a new idea for solving high-dimensional optimization problems. First, in order to balance the algorithm’s search abilities in terms of breadth and depth, the value of parameter G is given by an adaptive dynamic adjustment method. Second, in this paper, a foraging-behavior-improvement strategy is utilized to improve the algorithm’s solution accuracy and depth-optimization ability. Third, the artificial fish swarm algorithm (AFSA) is introduced to construct a dual-population collaborative optimization strategy based on chicken swarms and artificial fish swarms, so as to improve the algorithm’s ability to jump out of local extrema. The simulation experiments on the 17 benchmark functions preliminarily show that the ADPCCSO algorithm is superior to some swarm-intelligence algorithms such as the artificial fish swarm algorithm (AFSA), the artificial bee colony (ABC) algorithm, and the particle swarm optimization (PSO) algorithm in terms of solution accuracy and convergence performance. In addition, the APDCCSO algorithm is also utilized in the parameter estimation problem of the Richards model to further verify its performance.
22
В. А., Галкин,, Гавриленко, Т. В., Смородинов, А. Д., and Бобровская, О. П. "Swarm Intelligence Algorithms Applicability for Minimizing Various Functions." Успехи кибернетики / Russian Journal of Cybernetics, no. 4(12) (December 28, 2022): 84–97. http://dx.doi.org/10.51790/2712-9942-2022-3-4-10.
Full textAbstract:
в статье рассматриваются существующие роевые алгоритмы, приводятся их особенности. Подробно описывается алгоритм роя частиц, обеспечивающий оптимизацию функции. Проводятся эксперименты с различными параметрами роевого алгоритма (размера роя и времени жизни роя) для функций разных классов, как с одной точкой минимума, так и с несколькими. Делаются выводы о применимости алгоритма роя частиц для решения задач оптимизации. this study considers the existing swarm intelligence algorithms and their features. The particle swarm algorithm used for function optimization is described in detail. We adjusted various swarm algorithm properties (swarm size and lifetime) for minimizing functions of different classes, both with one and multiple minimums. It was found that the particle swarm algorithm is applicable to solving optimization problems.
23
Kareem, Shahab Wahhab, Shavan Askar, Roojwan Sc Hawezi, Glena Aziz Qadir, and Dina Yousif Mikhail. "A comparative Evaluation of Swarm Intelligence Algorithm Optimization: A Review." Journal of Electronics, Electromedical Engineering, and Medical Informatics 3, no. 3 (2021): 111–18. http://dx.doi.org/10.35882/jeeemi.v3i3.1.
Full textAbstract:
Swarm intelligence (SI), an important aspect of artificial intelligence, is increasingly gaining popularity as more and more high-complexity challenges necessitate solutions that are sub-optimal but still feasible in a fair amount of time. Artificial intelligence that mimics the collective behavior of a group of animals is known as swarm intelligence. Attempting to survive. It is primarily influenced by biological systems. The main aim of our article is to find out more about the guiding principle, classify possible implementation areas, and include a thorough analysis of several SI algorithms. Swarms can be observed in ant colonies, fish schools, bird flocks, among other fields. During this article, we will look at some Swarm instances and their behavior. We see many Swarm Intelligence systems, like Ant colony Optimization, which explains ant activity, nature, and how they conquer challenges; in birds, we see Particle Swarm Optimization is a swarm intelligence-based optimization technique, and how the locations must be positioned based on the three concepts. The Bee Colony Optimization follows, and explores the behavior of bees, their relationships, as well as movement and how they work in a swarm. This paper explores some of the methods and algorithms.
24
L, Meghana, and Jaya R. "Swarm Intelligence Algorithms - A Survey." International Journal of Computer Sciences and Engineering 6, no. 2 (2018): 184–88. http://dx.doi.org/10.26438/ijcse/v6i2.184188.
Full text
25
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.
Full textAbstract:
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.
26
P., Matrenin, Myasnichenko V., Sdobnyakov N., et al. "Generalized swarm intelligence algorithms with domain-specific heuristics." International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 157–65. https://doi.org/10.11591/ijai.v10.i1.pp157-165.
Full textAbstract:
In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free swarm intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domainspecific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.
27
Dou, Siqi, Junjie Li, and Fei Kang. "Parameter identification of concrete dams using swarm intelligence algorithm." Engineering Computations 34, no. 7 (2017): 2358–78. http://dx.doi.org/10.1108/ec-03-2017-0110.
Full textAbstract:
Purpose Parameter identification is an important issue in structural health monitoring and damage identification for concrete dams. The purpose of this paper is to introduce a novel adaptive fireworks algorithm (AFWA) into inverse analysis of parameter identification. Design/methodology/approach Swarm intelligence algorithms and finite element analysis are integrated to identify parameters of hydraulic structures. Three swarm intelligence algorithms: AFWA, standard particle swarm optimization (SPSO) and artificial bee colony algorithm (ABC) are adopted to make a comparative study. These algorithms are introduced briefly and then tested by four standard benchmark functions. Inverse analysis methods based on AFWA, SPSO and ABC are adopted to identify Young’s modulus of a concrete gravity dam and a concrete arch dam. Findings Numerical results show that swarm intelligence algorithms are powerful tools for parameter identification of concrete structures. The proposed AFWA-based inverse analysis algorithm for concrete dams is promising in terms of accuracy and efficiency. Originality/value Fireworks algorithm is applied for inverse analysis of hydraulic structures for the first time, and the problem of parameter selection in AFWA is studied.
28
Jiang, Qiang, Yongzhao Yan, Yinxing Dai, et al. "Autonomous Task Planning of Intelligent Unmanned Aerial Vehicle Swarm Based on Deep Deterministic Policy Gradient." Drones 9, no. 4 (2025): 272. https://doi.org/10.3390/drones9040272.
Full textAbstract:
Intelligent swarm is a powerful tool for targeting high-value objectives. Within the Anti-Access/Area Denial (A2/AD) context, an unmanned aerial vehicle (UAV) swarm must leverage its autonomous decision-making capability to execute tasks with independence. This paper focuses on the Suppression of Enemy Air Defenses (SEAD) mission for intelligent stealth UAV swarms. The current research field mainly faces challenges in fully simulating the complexity of real-world scenarios and in insufficient autonomous task planning capabilities. To address these issues, this paper develops a representative problem model, establishes a six-tier standardized simulation environment, and selects the Deep Deterministic Policy Gradient (DDPG) algorithm as the core intelligent algorithm to enhance the autonomous task planning capabilities of UAV swarms. At the algorithm level, this paper designs reward functions corresponding to UAV swarm behaviors, aiming to motivate UAV swarms to adopt more effective action strategies, thereby achieving autonomous task planning. Simulation results demonstrate that the scenario and architectural design are feasible and that artificial intelligence algorithms can enable the UAV swarm to show a higher level of intelligence.
29
Matrenin, P., V. Myasnichenko, N. Sdobnyakov, et al. "Generalized swarm intelligence algorithms with domain-specific heuristics." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (2021): 157. http://dx.doi.org/10.11591/ijai.v10.i1.pp157-165.
Full textAbstract:
<span lang="EN-US">In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free Swarm Intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domain-specific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a Swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.</span>
30
Al-Obaidi, Ahmed T. Sadiq, Hasanen S. Abdullah, and Zied O. Ahmed. "Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 354. http://dx.doi.org/10.11591/ijeecs.v10.i1.pp354-360.
Full textAbstract:
<p>Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.</p>
31
Ahmed, T. Sadiq Al-Obaidi, S. Abdullah Hasanen, and O. Ahmed Zied. "Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 354–60. https://doi.org/10.11591/ijeecs.v10.i1.pp354-360.
Full textAbstract:
Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.
32
Semwal, Archit, Sadik Shikalgar, and Dr Ramesh Solanki. "The Use of Artificial Intelligence in Swarm Drones." International Journal for Research in Applied Science and Engineering Technology 11, no. 7 (2023): 1052–57. http://dx.doi.org/10.22214/ijraset.2023.54799.
Full textAbstract:
Abstract: Swarm robotics, a field that draws inspiration from the collective behavior observed in natural swarms, has gained significant attention in recent years. Swarm drones, a specific subset of swarm robotics, involve the coordination and collaboration of multiple autonomous drones to accomplish complex tasks. With the advancements in artificial intelligence (AI) techniques, the integration of AI algorithms and approaches has revolutionized swarm drone systems. This research paper provides a comprehensive review of the use of AI in swarm drones, covering various aspects such as swarm formation, task allocation, navigation, communication, and decision-making. The paper discusses the current state of the art, challenges, and potential future directions in this exciting field
33
Dahiya, Brahm Prakash, Shaveta Rani, and Paramjeet Singh. "A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space." International Journal of Mathematical, Engineering and Management Sciences 4, no. 2 (2019): 471–88. http://dx.doi.org/10.33889/ijmems.2019.4.2-039.
Full textAbstract:
Meta-heuristic algorithms are used to get optimal solutions in different engineering branches. Here four types of meta-heuristics algorithms are used such as evolutionary algorithms, swarm-based algorithms, physics based algorithms and human based algorithms respectively. Swarm based meta-heuristic algorithms are given more effective result in optimization problem issues and these are generated global optimal solution. Existing swarm intelligence techniques are suffered with poor exploitation and exploration in given search space. Therefore, in this paper Hybrid Artificial Grasshopper Optimization (HAGOA) meta-heuristic algorithm is proposed to improve the exploitation and exploration in given search space. HAGOA is inherited Salp swarm behaviors. HAGOA performs balancing in exploitation and exploration search space. It is capable to make chain system between exploitation and exploration phases. The efficiency of HAGOA meta-heuristic algorithm will analyze using 19 benchmarks functions from F1 to F19. In this paper, HAGOA algorithm is performed efficiency analyze test with Artificial Grasshopper optimization (AGOA), Hybrid Artificial Bee Colony with Salp (HABCS), Modified Artificial Bee Colony (MABC), and Modify Particle Swarm Optimization (MPSO) swarm based meta-heuristic algorithms using uni-modal and multi-modal functions in MATLAB. Comparison results are shown that HAGOA meta-heuristic algorithm is performed better efficiency than other swarm intelligence algorithms on the basics of high exploitation, high exploration, and high convergence rate. It also performed perfect balancing between exploitation and exploration in given search space.
34
Yong, Wang, Wang Tao, Zhang Cheng-Zhi, and Huang Hua-Juan. "A New Stochastic Optimization Approach — Dolphin Swarm Optimization Algorithm." International Journal of Computational Intelligence and Applications 15, no. 02 (2016): 1650011. http://dx.doi.org/10.1142/s1469026816500115.
Full textAbstract:
A novel nature-inspired swarm intelligence (SI) optimization is proposed called dolphin swarm optimization algorithm (DSOA), which is based on mimicking the mechanism of dolphins in detecting, chasing after, and preying on swarms of sardines to perform optimization. In order to test the performance, the DSOA is evaluated against the corresponding results of three existing well-known SI optimization algorithms, namely, particle swarm optimization (PSO), bat algorithm (BA), and artificial bee colony (ABC), in the terms of the ability to find the global optimum of a range of the popular benchmark functions. The experimental results show that the proposed optimization seems superior to the other three algorithms, and the proposed algorithm has the performance of fast convergence rate, and high local optimal avoidance.
35
Zheng, Wei. "Improvement of Wolf Pack Algorithm and Its Application to Logistics Distribution Problems." Scientific Programming 2022 (September 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/7532076.
Full textAbstract:
In logistics distribution systems, the constrained optimisation of the cargo dispensing problem has been the focus of research in related fields. At present, many scholars try to solve the problem by introducing swarm intelligence algorithms, including genetic algorithm, particle swarm algorithm, bee swarm algorithm, fish swarm algorithm, etc. Each swarm intelligence algorithm has different characteristics, but they all have certain advantages for the optimisation of complex problems. In recent years, the Wolf Pack algorithm, an emerging swarm intelligence algorithm, has shown good global convergence and computational robustness in solving complex high-dimensional functions. Therefore, this article chooses to use the Wolf Pack algorithm to solve a multi-vehicle and multi-goods dispensing problem model. First, the principle and process of the Wolf Pack algorithm are introduced, and two improvements are proposed for the way of location update and the way of step update. Then, a mathematical model of the multi-vehicle and multi-goods dispensing problem is developed. Next, the mathematical model is solved using the proposed improved Wolf Pack algorithm. The experimental results show that the proposed improved Wolf Pack algorithm effectively solves the cargo dispatching problem. In addition, the proposed improved Wolf Pack algorithm can effectively reduce the number of vehicles to be dispatched compared with other swarm intelligence algorithms.
36
Reis, Cecília, and J. A. Tenreiro Machado. "Computational Intelligence in Circuit Synthesis." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 9 (2007): 1122–27. http://dx.doi.org/10.20965/jaciii.2007.p1122.
Full textAbstract:
This paper is devoted to the synthesis of combinational logic circuits through computational intelligence or, more precisely, using evolutionary computation techniques. Are studied two evolutionary algorithms, the Genetic and the Memetic Algorithm (GAs, MAs) and one swarm intelligence algorithm, the Particle Swarm Optimization (PSO). GAs are optimization and search techniques based on the principles of genetics and natural selection. MAs are evolutionary algorithms that include a stage of individual optimization as part of its search strategy, being the individual optimization in the form of a local search. The PSO is a population-based search algorithm that starts with a population of random solutions called particles. This paper presents the results for digital circuits design using the three above algorithms. The results show the statistical characteristics of this algorithms with respect to the number of generations required to achieve the solutions. The article analyzes also a new fitness function that includes an error discontinuity measure, which demonstrated to improve significantly the performance of the algorithm.
37
Belkadi, Mohamed, and Abdelhamid Daamouche. "Swarm Intelligence Approach to QRS Detection." International Arab Journal of Information Technology 17, no. 4 (2020): 480–87. http://dx.doi.org/10.34028/iajit/17/4/6.
Full textAbstract:
The QRS detection is a crucial step in ECG signal analysis; it has a great impact on the beats segmentation and in the final classification of the ECG signal. The Pan-Tompkins is one of the first and best-performing algorithms for QRS detection. It performs filtering for noise suppression, differentiation for slope dominance, and thresholding for decision making. All of the parameters of the Pan-Tompkins algorithm are selected empirically. However, we think that the Pan-Tompkins method can achieve better performance if the parameters were optimized. Therefore, we propose an adaptive algorithm that looks for the best set of parameters that improves the Pan-Tompkins algorithm performance. For this purpose, we formulate the parameter design as an optimization problem within a particle swarm optimization framework. Experiments conducted on the 24 hours recording of the MIT/BIH arrhythmia benchmark dataset achieved an overall accuracy of 99.83% which outperforms the state-of-the-art time-domain algorithms
38
Yena, Maksym. "Uav urban mobility control: swarm intelligence and collision avoidance." INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, no. 4(30) (December 11, 2024): 59–66. https://doi.org/10.30837/2522-9818.2024.4.059.
Full textAbstract:
Subject matter: Intelligent management of traffic flows in urban environments using swarm intelligence principles and collision avoidance algorithms to ensure safe and efficient urban mobility. Special attention is given to the management of unmanned vehicles and drones. Goal: To develop and analyze an approach to managing urban mobility that combines swarm intelligence principles and collision avoidance algorithms to optimize traffic flows, improve traffic safety, and reduce the number of accidents. Tasks: Investigate the safety and efficiency problems of urban transportation in the context of growing urbanization; develop a model that integrates swarm intelligence and collision avoidance algorithms for managing the movement of unmanned vehicles; conduct a series of experiments to test the effectiveness of the proposed approach; analyze the results of the experiments and determine the potential for improving urban mobility and ensuring road safety. Methods: Mathematical modeling of traffic flows using the swarm intelligence algorithm to coordinate the movement of unmanned vehicles and avoid collisions. Results: The proposed urban mobility management algorithm has demonstrated the ability to improve traffic flows, reduce the risk of collisions, and increase overall road safety. The results of the experiments confirmed the effectiveness of using swarm intelligence for coordination vehicles and collision avoidance algorithms to prevent accidents.
39
Iskandar, Alaa, and Béla Kovács. "A Survey on Automatic Design Methods for Swarm Robotics Systems." Carpathian Journal of Electronic and Computer Engineering 14, no. 2 (2021): 1–5. http://dx.doi.org/10.2478/cjece-2021-0006.
Full textAbstract:
Abstract Swarm robots are a branch of robotics that draws inspiration from biological swarms to mimic their collective behavior. Automatic design methods are part of swarm engineering, depend on artificial intelligence algorithms to produce the collective behavior of robots. In general, they follow two-approach evolutionary algorithms like practical swarm optimization and reinforcement learning. This paper studies these approaches, illustrating the effect of modifications and enhancements of algorithms for both directions, showing important parameters considered for the best performance of the swarm, and explaining the methods and advantages of using deep learning to reinforcement learning.
40
Ludwig, Simone A., and Deepak Dawar. "Parallelization of Enhanced Firework Algorithm using MapReduce." International Journal of Swarm Intelligence Research 6, no. 2 (2015): 32–51. http://dx.doi.org/10.4018/ijsir.2015040102.
Full textAbstract:
Swarm intelligence algorithms are inherently parallel since different individuals in the swarm perform independent computations at different positions simultaneously. Hence, these algorithms lend themselves well to parallel implementations thereby speeding up the optimization process. FireWorks Algorithm (FWA) is a recently proposed swarm intelligence algorithm for optimization. This work investigates the scalability of the parallelization of the Enhanced FireWorks Algorithm (EFWA), which is an improved version of FWA. The authors use the MapReduce platform for parallelizing EFWA, investigate its ability to scale, and report on the speedup obtained on different benchmark functions for increasing problem dimensions.
41
Liu, Yang. "Using Deep Learning and Swarm Intelligence to Achieve Personalized English-Speaking Education." International Journal of Swarm Intelligence Research 15, no. 1 (2024): 1–15. http://dx.doi.org/10.4018/ijsir.343989.
Full textAbstract:
This paper presents a pioneering approach to personalized English oral education through the integration of deep learning and swarm intelligence algorithms. Leveraging deep learning techniques, our system offers precise evaluation of various aspects of spoken language, including pronunciation, fluency, and grammatical accuracy. Furthermore, we combine swarm intelligence algorithms to optimize model parameters to achieve optimal performance. We compare the proposed optimization algorithm based on swarm intelligence and its corresponding original algorithm for training comparison to test the effect of the proposed optimizer. Experimental results show that in most cases, the accuracy of the test set using the optimization algorithm based on the swarm intelligence algorithm is better than the corresponding original version, and the training results are more stable. Our experimental results demonstrate the efficacy of the proposed approach in enhancing personalized English oral education, paving the way for transformative advancements in language learning technologies.
42
Puusepp, Andres, Tanel Tammet, and Enar Reilent. "Covering an Unknown Area with an RFID-Enabled Robot Swarm." Applied Mechanics and Materials 490-491 (January 2014): 1157–62. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1157.
Full textAbstract:
Our goal is to improve the coverage of an area using robots with simple sensors and simple, robust algorithms usable for any kind of room. We investigate the advantage of the swarm - compared to a single robot - and three different algorithms for the task of searching landmarks in a previously unknown area. The guidance of the robot is based on landmarks, implemented by RFID tags irregularly placed in the room. The experiments are conducted using a custom made simulator of RFID-equipped Roomba cleaning robots, based on our previous work with real-life Roomba swarms. We show that for the simple room coverage algorithms the speedup gained from increasing the size of the swarm diminishes as the swarm grows and most importantly, for larger swarm sizes the information available and the intelligence of the algorithm becomes less important.
43
Rana, Md Masud, and Umar Muhammad Ibrahim. "Exploring the Role of Reinforcement Learning in Area of Swarm Robotic." European Journal of Electrical Engineering and Computer Science 8, no. 3 (2024): 15–24. http://dx.doi.org/10.24018/ejece.2024.8.3.619.
Full textAbstract:
Swarm robotics, which draws inspiration from collective behaviours observed in nature, has become a potential approach for creating intelligent robotic systems that can perform collaborative and decentralised operations. This research investigates the incorporation of Reinforcement Learning (RL) methods into swarm robotics, utilising autonomous learning to improve the flexibility and effectiveness of robotic swarms. The exploration commences with thoroughly examining swarm robotics, highlighting its definitions, applications, and basic correlation with swarm intelligence. An in-depth analysis of temporal-difference (TD) learning offers valuable insights into the role of value-based RL approaches in the learning mechanisms of a swarm. The subject encompasses both on-policy and off-policy algorithms, elucidating the subtleties of their mechanics within the realm of swarm robots. The study examines task allocation, a crucial element of swarm behaviour, and emphasises how reinforcement learning enables robotic swarms to independently assign duties according to environmental conditions and objectives. Path planning, a crucial element, demonstrates how reinforcement learning-based adaptive navigation algorithms improve the effectiveness of swarm robots in changing situations. Communication and collaboration are shown to be crucial applications, demonstrating how RL algorithms enable enhanced information sharing and coordinated behaviours among swarm agents. The text examines the benefits and challenges of incorporating reinforcement learning (RL) into swarm robots. It provides a fair assessment of the advantages and considerations related to this method. The study culminates with a comprehensive summary, highlighting the profound influence of RL on swarm robotics in attaining collective intelligence, flexibility, and efficient job completion. The findings emphasise the project’s role in the changing field of robotics, creating opportunities for additional research and progress in swarm intelligence and autonomous robotic systems.
44
Sachin Karadgi, Rashmi Benni, Shashikumar Totad, Karibasappa K. G,. "A Comparative Study of Evolutionary and Swarm Intelligence Algorithms for Job Scheduling on Identical Parallel Machines." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (2023): 3734–45. http://dx.doi.org/10.52783/tjjpt.v44.i4.1531.
Full textAbstract:
In parallel computing systems, job scheduling plays a crucial role in enhancing system efficiency and minimizing the makespan. In recent years, evolutionary and swarm intelligence algorithms have gained prominence as effective approaches for solving combinatorial optimization problems. In the present work, we have considered genetic algorithm (GA) for evolutionary algorithms and particle swarm optimization (PSO) for swarm intelligence algorithms. Evolutionary algorithms (EA) and swarm intelligence algorithms (SIA) have shown promising results in solving job scheduling challenges. In this study, we collate the performance of EA and SIA approaches for job scheduling on parallel machines. We use different benchmark instances to evaluate the algorithms' makespan and computational time performance. The results show that SIA algorithms outperform EA algorithms regarding makespan and computational time for all benchmark instances. Furthermore, the study provides insights into the strengths and weaknesses of EA and SIA algorithms for job scheduling on parallel machines. Our findings provide useful insights for researchers and practitioners interested in applying optimization techniques to solve job scheduling problems on parallel machines.
45
Hezam, Ibrahim M., Osama Abdel Raouf, and Mohey M. Hadhoud. "A New Compound Swarm Intelligence Algorithms for Solving Global Optimization Problems." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 9 (2013): 2010–20. http://dx.doi.org/10.24297/ijct.v10i9.1389.
Full textAbstract:
This paper proposes a new hybrid swarm intelligence algorithm that encompasses the feature of three major swarm algorithms. It combines the fast convergence of the Cuckoo Search (CS), the dynamic root change of the Firefly Algorithm (FA), and the continuous position update of the Particle Swarm Optimization (PSO). The Compound Swarm Intelligence Algorithm (CSIA) will be used to solve a set of standard benchmark functions. The research study compares the performance of CSIA with that of CS, FA, and PSO, using the same set of benchmark functions. The comparison aims to test if the performance of CSIA is Competitive to that of the CS, FA, and PSO algorithms denoting the solution results of the benchmark functions.
46
Igiri, Chinwe P., Yudhveer Singh, and Ramesh C. Poonia. "A Review Study of Modified Swarm Intelligence: Particle Swarm Optimization, Firefly, Bat and Gray Wolf Optimizer Algorithms." Recent Advances in Computer Science and Communications 13, no. 1 (2020): 5–12. http://dx.doi.org/10.2174/2213275912666190101120202.
Full textAbstract:
Background: Limitations exist in traditional optimization algorithms. Studies show that bio-inspired alternatives have overcome these drawbacks. Bio-inspired algorithm mimics the characteristics of natural occurrences to solve complex problems. Particle swarm optimization, firefly algorithm, bat algorithms, gray wolf optimizer, among others are examples of bio-inspired algorithms. Researchers make certain assumptions while designing these models which limits their performance in some optimization domains. Efforts to find a solution to deal with these challenges leads to the multiplicity of variants. Objective: This study explores the improvement strategies in four popular swarm intelligence in the literature. Specifically, particle swarm optimization, firefly algorithm, bat algorithm, and gray wolf optimizer. It also tries to identify the exact modification position in the algorithm kernel that yielded the positive outcome. The primary goal is to understand the trends and the relationship in their performance. Methods: The best evidence review methodology approach is employed. Two ancient but valuable and two recent and efficient swarm intelligence, are selected for this study. Results: Particle swarm optimization, firefly algorithm, bat algorithm, and gray wolf optimizer exhibit local optima entrapment in their standard states. The same enhancement strategy produced effective outcome across these four swarm intelligence. The exact approach is chaotic-based optimization. However, the implementation produced the desired result at different stages of these algorithms. Conclusion: Every bio-inspired algorithm comprises two or more updating functions. Researchers need a proper guide on what and how to apply a strategy for an optimum result.
47
Saeed, Ayesha, Ali Husnain, Anam Zahoor, and Mehmood Gondal. "A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation." International Journal of Innovative Research in Computer Science and Technology 12, no. 4 (2024): 1–9. http://dx.doi.org/10.55524/ijircst.2024.12.4.1.
Full textAbstract:
The Graph Coloring Problem (GCP) is a significant optimization challenge widely suitable to solve scheduling problems. Its goal is to specify the minimum colors (k) required to color a graph properly. Due to its NP-completeness, exact algorithms become impractical for graphs exceeding 100 vertices. As a result, approximation algorithms have gained prominence for tackling large-scale instances. In this context, the Cat Swarm algorithm, a novel population-based metaheuristic in the domain of swarm intelligence, has demonstrated promising convergence properties compared to other population-based algorithms. This research focuses on designing and implementing the Cat Swarm algorithm to address the GCP. By conducting a comparative study with established algorithms, our investigation revolves around quantifying the minimum value of k required by the Cat Swarm algorithm for each graph instance. The evaluation metrics include the algorithm's running time in seconds, success rate, and the mean count of iterations or assessments required to reach a goal.
48
Zhao, Zirui. "An Improved BSA with Dynamic Grouping Strategy and Its Application in UAV Path Planning." Mathematical Modeling and Algorithm Application 3, no. 2 (2024): 61–64. https://doi.org/10.54097/38mpj028.
Full textAbstract:
Due to the issues of slow convergence speed and low accuracy in swarm intelligence algorithms for UAV path planning, this paper proposes An Improved Bird Swarm Algorithm with dynamic grouping strategy(DGSBSA).This method introduces strategies such as dynamic grouping and reverse foraging to enhance the algorithm's performance.During the iteration process, dynamic grouping is performed based on the positions of the bird swarm to enhance population diversity.The experimental results demonstrate that the proposed DGSBSA algorithm improves the algorithm's accuracy and enhances the convergence speed in the path planning process.
49
Khaleel, Shahbaa I., and Ragad W. Khaled. "Image retrieval based on swarm intelligence." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5390. http://dx.doi.org/10.11591/ijece.v11i6.pp5390-5401.
Full textAbstract:
To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.
50
Shahbaa, I. Khaleel, and W. Khaled Ragad. "Image retrieval based on swarm intelligence." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5390–401. https://doi.org/10.11591/ijece.v11i6.pp5390-5401.
Full textAbstract:
To keep pace with the development of modern technology in this information technology era, and the immense image databases, whether personal or commercial, are increasing, is requiring the management of these databases to strong and accurate systems to retrieve images with high efficiency. Because of the swarm intelligence algorithms are great importance in solving difficult problems and obtaining the best solutions. Here in this research, a proposed system is designed to retrieve color images based on swarm intelligence algorithms. Where the algorithm of the ant colony optimization (ACOM) and the intelligent water drop (IWDM) was used to improve the system's work by conducting the clustering process in these two methods on the features extracted by annular color moment method (ACM) to obtain clustered data, the amount of similarity between them and the query image, is calculated to retrieve images from the database, efficiently and in a short time. In addition, improving the work of these two methods by hybridizing them with fuzzy method, fuzzy gath geva clustering algorithm (FGCA) and obtaining two new high efficiency hybrid algorithms fuzzy ant colony optimization method (FACOM) and fuzzy intelligent water drop method (FIWDM) by retrieving images whose performance values are calculated by calculating the values of precision, recall and the f-measure. It proved its efficiency by comparing it with fuzzy method, FGCA and by methods of swarm intelligence without hybridization, and its work was excellent.