Journal articles: 'Bio-inspired swarms'

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Lewis, Michael, Michael Goodrich, Katia Sycara, and Mark Steinberg. "Human Factors issues for Interaction with Bio-Inspired Swarms." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 56, no. 1 (September 2012): 61–64. http://dx.doi.org/10.1177/1071181312561033.

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Petráček, Pavel, Viktor Walter, Tomáš Báča, and Martin Saska. "Bio-inspired compact swarms of unmanned aerial vehicles without communication and external localization." Bioinspiration & Biomimetics 16, no. 2 (December 18, 2020): 026009. http://dx.doi.org/10.1088/1748-3190/abc6b3.

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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 (February 14, 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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Fatès, Nazim, and Nikolaos Vlassopoulos. "A Robust Scheme for Aggregating Quasi-Blind Robots in an Active Environment." International Journal of Swarm Intelligence Research 3, no. 3 (July 2012): 66–80. http://dx.doi.org/10.4018/jsir.2012070105.

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The question of how to aggregate autonomous agents with limited abilities in the absence of centralized coordination is known as the Decentralized Gathering Problem. The authors present a bio-inspired aggregation scheme that solves this problem and study a first application of this scheme to a small team of robots. The robots (Alice and Khepera III) obey simple rules and have only a rudimentary perception of their environment. The collective behavior is based on stigmergic principles and uses an active environment to relay the communications between robots. This results in an aggregation process that shows good properties of robustness and that can in principle be extended to swarms of robots.

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Dogan, Rezarta Islamaj, Yolanda Gil, Haym Hirsh, Narayanan C. Krishnan, Michael Lewis, Cetin Mericli, Parisa Rashidi, et al. "Reports on the 2012 AAAI Fall Symposium Series." AI Magazine 34, no. 1 (December 17, 2012): 93. http://dx.doi.org/10.1609/aimag.v34i1.2457.

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The Association for the Advancement of Artificial Intelligence was pleased to present the 2012 Fall Symposium Series, held Friday through Sunday, November 2–4, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia were as follows: AI for Gerontechnology (FS-12-01), Artificial Intelligence of Humor (FS-12-02), Discovery Informatics: The Role of AI Research in Innovating Scientific Processes (FS-12-03), Human Control of Bio-Inspired Swarms (FS-12-04), Information Retrieval and Knowledge Discovery in Biomedical Text (FS-12-05), Machine Aggregation of Human Judgment (FS-12-06), Robots Learning Interactively from Human Teachers (FS-12-07), and Social Networks and Social Contagion (FS-12-08). The highlights of each symposium are presented in this report.

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Meza Álvarez, Joaquín Javier, Juan Manuel Cueva Lovelle, and Helbert Eduardo Espitia. "REVISIÓN SOBRE ALGORITMOS DE OPTIMIZACIÓN MULTI-OBJETIVO GENÉTICOS Y BASADOS EN ENJAMBRES DE PARTÍCULAS." Redes de Ingeniería 6, no. 2 (March 9, 2016): 54. http://dx.doi.org/10.14483/udistrital.jour.redes.2015.2.a06.

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El enfoque evolutivo como también el comportamiento social han mostrado ser una muy buena alternativa en los problemas de optimización donde se presentan varios objetivos a optimizar. De la misma forma, existen todavía diferentes vias para el desarrollo de este tipo de algoritmos. Con el fin de tener un buen panorama sobre las posibles mejoras que se pueden lograr en los algoritmos de optimización bio-inspirados multi-objetivo es necesario establecer un buen referente de los diferentes enfoques y desarrollos que se han realizado hasta el momento.En este documento se revisan los algoritmos de optimización multi-objetivo más recientes tanto genéticos como basados en enjambres de partículas. Se realiza una revisión critica con el fin de establecer las características más relevantes de cada enfoque y de esta forma identificar las diferentes alternativas que se tienen para el desarrollo de un algoritmo de optimización multi-objetivo bio-inspirado.Review about genetic multi-objective optimization algorithms and based in particle swarmABSTRACTThe evolutionary approach as social behavior have proven to be a very good alternative in optimization problems where several targets have to be optimized. Likewise, there are still different ways to develop such algorithms. In order to have a good view on possible improvements that can be achieved in the optimization algorithms bio-inspired multi-objective it is necessary to establish a good reference of different approaches and developments that have taken place so far. In this paper the algorithms of multi-objective optimization newest based on both genetic and swarms of particles are reviewed. Critical review in order to establish the most relevant characteristics of each approach and thus identify the different alternatives have to develop an optimization algorithm multi-purpose bio-inspired design is performed.Keywords: evolutionary computation, evolutionary multi-objective optimization.

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Dong, Xiaoguang, and Metin Sitti. "Controlling two-dimensional collective formation and cooperative behavior of magnetic microrobot swarms." International Journal of Robotics Research 39, no. 5 (January 28, 2020): 617–38. http://dx.doi.org/10.1177/0278364920903107.

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Magnetically actuated mobile microrobots can access distant, enclosed, and small spaces, such as inside microfluidic channels and the human body, making them appealing for minimally invasive tasks. Despite their simplicity when scaling down, creating collective microrobots that can work closely and cooperatively, as well as reconfigure their formations for different tasks, would significantly enhance their capabilities such as manipulation of objects. However, a challenge of realizing such cooperative magnetic microrobots is to program and reconfigure their formations and collective motions with under-actuated control signals. This article presents a method of controlling 2D static and time-varying formations among collective self-repelling ferromagnetic microrobots (100 [Formula: see text]m to 350 [Formula: see text]m in diameter, up to 260 in number) by spatially and temporally programming an external magnetic potential energy distribution at the air–water interface or on solid surfaces. A general design method is introduced to program external magnetic potential energy using ferromagnets. A predictive model of the collective system is also presented to predict the formation and guide the design procedure. With the proposed method, versatile complex static formations are experimentally demonstrated and the programmability and scaling effects of formations are analyzed. We also demonstrate the collective mobility of these magnetic microrobots by controlling them to exhibit bio-inspired collective behaviors such as aggregation, directional motion with arbitrary swarm headings, and rotational swarming motion. Finally, the functions of the produced microrobotic swarm are demonstrated by controlling them to navigate through cluttered environments and complete reconfigurable cooperative manipulation tasks.

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Albani, Dario, Wolfgang Hönig, Daniele Nardi, Nora Ayanian, and Vito Trianni. "Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms." Applied Sciences 11, no. 7 (March 31, 2021): 3115. http://dx.doi.org/10.3390/app11073115.

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Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.

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Aihara, Ikkyu, Daichi Kominami, Yasuharu Hirano, and Masayuki Murata. "Mathematical modelling and application of frog choruses as an autonomous distributed communication system." Royal Society Open Science 6, no. 1 (January 2019): 181117. http://dx.doi.org/10.1098/rsos.181117.

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Interactions using various sensory cues produce sophisticated behaviour in animal swarms, e.g. the foraging behaviour of ants and the flocking of birds and fish. Here, we investigate the behavioural mechanisms of frog choruses from the viewpoints of mathematical modelling and its application. Empirical data on male Japanese tree frogs demonstrate that (1) neighbouring male frogs avoid call overlaps with each other over a short time scale and (2) they collectively switch between the calling state and the silent state over a long time scale. To reproduce these features, we propose a mathematical model in which separate dynamical models spontaneously switch due to a stochastic process depending on the internal dynamics of respective frogs and also the interactions among the frogs. Next, the mathematical model is applied to the control of a wireless sensor network in which multiple sensor nodes send a data packet towards their neighbours so as to deliver the packet to a gateway node by multi-hop communication. Numerical simulation demonstrates that (1) neighbouring nodes can avoid a packet collision over a short time scale by alternating the timing of data transmission and (2) all the nodes collectively switch their states over a long time scale, establishing high network connectivity while reducing network power consumption. Consequently, this study highlights the unique dynamics of frog choruses over multiple time scales and also provides a novel bio-inspired technology that is applicable to the control of a wireless sensor network.

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Sadiku, Matthew N. O., Mahamadou Tembely, and Sarhan M. Musa. "Swarm Intelligence: A Primer." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 5 (June 2, 2018): 100. http://dx.doi.org/10.23956/ijarcsse.v8i5.681.

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Swarm intelligence is the emergent collective intelligence of groups of simple agents. It belongs to the emerging field of bio-inspired soft computing. It is inspired from the biological entities such as birds, fish, ants, wasps, termites, and bees. Bio-inspired computation is a field of study that is closely related to artificial intelligence. This paper provides a brief introduction to swarm intelligence.

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Singh, Dharmpal. "A Modified Bio Inspired." International Journal of Applied Metaheuristic Computing 9, no. 1 (January 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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Agirbas, Asli. "Optimization test of a rule-based swarm intelligence simulation for the conceptual design process." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 34, no. 4 (July 15, 2020): 477–91. http://dx.doi.org/10.1017/s0890060420000323.

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AbstractToday, in the field of architecture, bio-inspired algorithms can be used to design and seek solutions to design problems. Two of the most popular algorithms are the genetic algorithm (GA) and swarm intelligence algorithm. However, no study has examined the simultaneous use of these two bio-inspired algorithms in the field of architecture. Therefore, this study aims to test whether these two bio-inspired algorithms can work together. To this end, GA is used in this study to optimize the rule-based swarm algorithm for the conceptual design process. In this optimization test, the objective was to increase the surface area, and the constraints are parcel boundary and building height. Further, the forms associated with swarm agents were determined as variables. Following the case studies, the study concludes that the two bio-inspired algorithms can effectively work together.

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Latifah, Nyayu Husni, Ade Silvia, Ekawati Prihatini, Siti Nurmaini, and Irsyadi Yani. "Swarm Intelligent in Bio-Inspired Perspective: A Summary." Computer Engineering and Applications Journal 7, no. 2 (July 13, 2018): 109–28. http://dx.doi.org/10.18495/comengapp.v7i2.255.

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This paper summarizes the research performed in the field of swarm intelligent in recent years. The classification of swarm intelligence based on behavior is introduced.Â The principles of each behaviors, i.e. foraging, aggregating, gathering, preying, echolocation, growth, mating, clustering, climbing, brooding, herding, and jumping are described. 3 algorithms commonly used in swarm intelligent are discussed. Â At the end of summary, the applications of the SI algorithms are presented.

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Singh, Dharmpal. "A New Bio-Inspired Social Spider Algorithm." International Journal of Applied Metaheuristic Computing 12, no. 1 (January 2021): 79–93. http://dx.doi.org/10.4018/ijamc.2021010105.

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The concept of bio-inspired algorithms is used in real-world problems to search the efficient problem-solving methods. Evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques of metahuristics. In this paper, an effort has been made to propose a modified social spider algorithm to solve global optimization problems in the real world. Social spiders used the foraging strategy, vibrations on the spider web to determine the positions of prey. The selection of vibration, estimated new position and calculation of the fitness function, has been furnished in details way as compared to different previously proposed swarm intelligence algorithms. Moreover, experimental result has been carried out by modified social spider on series of widely-used benchmark problem with four benchmark algorithms. Furthermore, a modified form of the proposed algorithm has superior performance as compared to other state-of-the-art metaheuristics algorithms.

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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 (March 13, 2020): 5–12. http://dx.doi.org/10.2174/2213275912666190101120202.

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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.

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Ghouzali, Sanaa, and Souad Larabi. "Face Identification based Bio-Inspired Algorithms." International Arab Journal of Information Technology 17, no. 1 (January 1, 2019): 118–27. http://dx.doi.org/10.34028/iajit/17/1/14.

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Most biometric identification applications suffer from the curse of dimensionality as the database size becomes very large, which could negatively affect both the identification performance and speed. In this paper, we use Projection Pursuit (PP) methods to determine clusters of individuals. Support Vector Machine (SVM) classifiers are then applied on each cluster of users separately. PP clustering is conducted using Friedman and Kurtosis projection indices optimized by Genetic Algorithm and Particle Swarm Optimization methods. Experimental results obtained using YALE face database showed improvement in the performance and speed of face identification system

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Stolfi, Daniel H., and Enrique Alba. "Green Swarm: Greener routes with bio-inspired techniques." Applied Soft Computing 71 (October 2018): 952–63. http://dx.doi.org/10.1016/j.asoc.2018.07.032.

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Nicolaou, Andreas, Stavros Shiaeles, and Nick Savage. "Mitigating Insider Threats Using Bio-Inspired Models." Applied Sciences 10, no. 15 (July 22, 2020): 5046. http://dx.doi.org/10.3390/app10155046.

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Insider threats have become a considerable information security issue that governments and organizations must face. The implementation of security policies and procedures may not be enough to protect organizational assets. Even with the evolution of information and network security technology, the threat from insiders is increasing. Many researchers are approaching this issue with various methods in order to develop a model that will help organizations to reduce their exposure to the threat and prevent damage to their assets. In this paper, we approach the insider threat problem and attempt to mitigate it by developing a machine learning model based on Bio-inspired computing. The model was developed by using an existing unsupervised learning algorithm for anomaly detection and we fitted the model to a synthetic dataset to detect outliers. We explore swarm intelligence algorithms and their performance on feature selection optimization for improving the performance of the machine learning model. The results show that swarm intelligence algorithms perform well on feature selection optimization and the generated, near-optimal, subset of features has a similar performance to the original one.

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Meng, Xian-Bing, X. Z. Gao, Lihua Lu, Yu Liu, and Hengzhen Zhang. "A new bio-inspired optimisation algorithm: Bird Swarm Algorithm." Journal of Experimental & Theoretical Artificial Intelligence 28, no. 4 (June 17, 2015): 673–87. http://dx.doi.org/10.1080/0952813x.2015.1042530.

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Varughese, Joshua Cherian, Ronald Thenius, Paul Leitgeb, Franz Wotawa, and Thomas Schmickl. "A Model for Bio-Inspired Underwater Swarm Robotic Exploration." IFAC-PapersOnLine 51, no. 2 (2018): 385–90. http://dx.doi.org/10.1016/j.ifacol.2018.03.066.

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Zhang, Wei, Weifeng Shi, and Jinbao Zhuo. "Shipboard Power System Stabilizer Optimization Using GA and QPSO Algorithm." International Journal of Computational Intelligence and Applications 16, no. 03 (September 2017): 1750015. http://dx.doi.org/10.1142/s1469026817500158.

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In order to improve shipboard power system dynamic stability, two bio-inspired algorithms, the genetic algorithm (GA) and quantum-behaved particle swarm optimization (QPSO), method are proposed for the shipboard power system stabilizer (PSS) optimization. The proposed PSS optimization method is inspired by a hybrid-coordinated stabilizer for diesel engine generator and the bio-inspired algorithm. The simulations are conducted under load change disturbance and short-circuit fault case for the marine generator with/without the diesel engine speed governor. Simulation results show that the quantum particle swarm optimize strategy could improve the dynamic performance of the marine generator better than the GA method. The dynamic performance for shipboard power system always indicates the effectiveness, feasibility and robustness of the proposed approach.

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Daud, N. Atiqah, and Salihatun Md Salleh. "Modeling of Heat Exchanger by Using Bio-Inspired Algorithm." Applied Mechanics and Materials 660 (October 2014): 831–35. http://dx.doi.org/10.4028/www.scientific.net/amm.660.831.

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Modelling of heat exchanger helps to define the error that occurs during the operation. Hence by optimizing it using genetic algorithm and particle swarm optimization, the error that occurred could be minimized and compared between both algorithms. The primary objective of this study was to obtain structural model using ARMAX equation. In this study, data from heat exchanger experiment was used to determine the parameter of ARMAX equation. Using genetic algorithm and particle swarm optimization, ARMAX parameters are optimized. Hence, the transfer function represents the plant for modelling. Validation test used were autocorrelation and cross-correlation to validate between normalised data input and error. Based on the result obtained, for GA, the input parameters are-0.000214, -0.000728, -0.0020, and-0.000804 while the output parameters are-1.0000, -0.1783, -0.1473 and 0.3248. For PSO, the input parameters are 0.0104, -0.0122, -0.0067 and 0.0118 while the output parameters are-0.4274, -0.1256, -0.1865 and-0.2614. From validation test, GA produced smoother and effective result compared to PSO with less noise exists.

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Nguyen, Tri-Hai, Luong Vuong Nguyen, Jason J. Jung, Israel Edem Agbehadji, Samuel Ofori Frimpong, and Richard C. Millham. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges." Sustainability 12, no. 20 (October 15, 2020): 8495. http://dx.doi.org/10.3390/su12208495.

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Sustainable energy development consists of design, planning, and control optimization problems that are typically complex and computationally challenging for traditional optimization approaches. However, with developments in artificial intelligence, bio-inspired algorithms mimicking the concepts of biological evolution in nature and collective behaviors in societies of agents have recently become popular and shown potential success for these issues. Therefore, we investigate the latest research on bio-inspired approaches for smart energy management systems in smart homes, smart buildings, and smart grids in this paper. In particular, we give an overview of the well-known and emerging bio-inspired algorithms, including evolutionary-based and swarm-based optimization methods. Then, state-of-the-art studies using bio-inspired techniques for smart energy management systems are presented. Lastly, open challenges and future directions are also addressed to improve research in this field.

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Schmickl, T., and K. Crailsheim. "Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm." Autonomous Robots 25, no. 1-2 (December 22, 2007): 171–88. http://dx.doi.org/10.1007/s10514-007-9073-4.

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Al Shayokh, Md, and Soo Young Shin. "Bio Inspired Distributed WSN Localization Based on Chicken Swarm Optimization." Wireless Personal Communications 97, no. 4 (August 9, 2017): 5691–706. http://dx.doi.org/10.1007/s11277-017-4803-1.

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Tan, Ying, and Yuhui Shi. "Editorial: Special Section on Bio-Inspired Swarm Computing and Engineering." IEEE/ACM Transactions on Computational Biology and Bioinformatics 14, no. 1 (January 1, 2017): 1–3. http://dx.doi.org/10.1109/tcbb.2016.2566438.

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Gélvez, Nancy, Helbert Espitia, and Jhon Bayona. "Testing of a Virtualized Distributed Processing System for the Execution of Bio-Inspired Optimization Algorithms." Symmetry 12, no. 7 (July 17, 2020): 1192. http://dx.doi.org/10.3390/sym12071192.

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Due to the stochastic characteristics of bio-inspired optimization algorithms, several executions are often required; then a suitable infrastructure must be available to run these algorithms. This paper reviews a virtualized distributed processing scheme to establish an adequate infrastructure for the execution of bio-inspired algorithms. In order to test the virtualized distributed system, the well known versions of genetic algorithms, differential evolution and particle swarm optimization, are used. The results show that the revised distributed virtualized schema allows speeding up the execution of the algorithms without altering their result in the objective function.

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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 (December 30, 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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Sun, Yongbin, and Haibin Duan. "Pigeon-inspired optimization and lateral inhibition for image matching of autonomous aerial refueling." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 232, no. 8 (March 9, 2017): 1571–83. http://dx.doi.org/10.1177/0954410017696110.

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Autonomous aerial refueling (AAR) is an essential application of unmanned aerial vehicles for both military and civilian domains. In this paper, a hybrid algorithm of the pigeon-inspired optimization (PIO) and lateral inhibition (LI), called LI-PIO, is proposed for image matching problem of AAR. LI is adopted for image pre-processing to enhance the edges and contrast of images. PIO, inspired from the homing characteristics of pigeons, is a novel bio-inspired swarm intelligence algorithm. To demonstrate the effectiveness and feasibility of our proposed algorithm, we make extensive comparative experiments with particle swarm optimization (PSO), particle swarm optimization based on lateral inhibition (LI-PSO), and PIO. It can be concluded from the experimental results that our proposed LI-PIO has excellent performances for image matching problem of AAR, especially in convergent rate and computation speed.

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Hamou, Reda Mohamed, Hadj Ahmed Bouarara, and Abdelmalek Amine. "Bio-Inspired Techniques in the Clustering of Texts." International Journal of Applied Metaheuristic Computing 6, no. 4 (October 2015): 39–68. http://dx.doi.org/10.4018/ijamc.2015100103.

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Today, the development of a large scale access network internet/intranet has increased the amount of textual information available online/offline, where billions of documents have been created. In the last few years, bio inspired techniques which invaded the world of text-mining such, as clustering, represents a critical problem in the digital society especially over the world of information retrieval (IR). The content of this article is a recapitulation of a set of works as a comparative study between the authors' experiments realized by applying a set of bio-inspired techniques (social spiders(SS), 2D Cellular automata (2D-CA), 3D cellular automata (3D-CA), Artificial immune system (AIS), Particle swarm optimization (PSO)) and other techniques founded in literature (Ants Colony Optimization (ACO) and Genetic algorithms (GAs)) for solving the text clustering challenge by using the benchmark Reuter 21785. They analyse the different results in term of entropy, f-measure, execution time, and clusters number in order to find the ideal configuration (similarity measure and text representation method) for each technique. Their objectives are to improve the efficiency of text clustering systems and make decisions that can be the starting point for other researchers.

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Zheng, Yu-Jun, Sheng-Yong Chen, Yao Lin, and Wan-Liang Wang. "Bio-Inspired Optimization of Sustainable Energy Systems: A Review." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/354523.

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Sustainable energy development always involves complex optimization problems of design, planning, and control, which are often computationally difficult for conventional optimization methods. Fortunately, the continuous advances in artificial intelligence have resulted in an increasing number of heuristic optimization methods for effectively handling those complicated problems. Particularly, algorithms that are inspired by the principles of natural biological evolution and/or collective behavior of social colonies have shown a promising performance and are becoming more and more popular nowadays. In this paper we summarize the recent advances in bio-inspired optimization methods, including artificial neural networks, evolutionary algorithms, swarm intelligence, and their hybridizations, which are applied to the field of sustainable energy development. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.

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32

Shelar, A. S. "ASSOCIATION RULE MINING USING BIO INSPIRED BEES SWARM INTELLIGENCE ON CUDA." International Journal of Advanced Research in Computer Science 8, no. 7 (August 20, 2017): 1137–41. http://dx.doi.org/10.26483/ijarcs.v8i7.4572.

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Ji, Bai, Xiaozheng Lu, Geng Sun, Wei Zhang, Jiahui Li, and Yinzhe Xiao. "Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach." IEEE Access 8 (2020): 85989–6002. http://dx.doi.org/10.1109/access.2020.2992752.

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Wu, Shinq-Jen, and Cheng-Tao Wu. "A bio-inspired optimization for inferring interactive networks: Cockroach swarm evolution." Expert Systems with Applications 42, no. 6 (April 2015): 3253–67. http://dx.doi.org/10.1016/j.eswa.2014.11.039.

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Mirjalili, Seyedali, Amir H. Gandomi, Seyedeh Zahra Mirjalili, Shahrzad Saremi, Hossam Faris, and Seyed Mohammad Mirjalili. "Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems." Advances in Engineering Software 114 (December 2017): 163–91. http://dx.doi.org/10.1016/j.advengsoft.2017.07.002.

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Wang, Jian, Yongxin Liu, Shuteng Niu, and Houbing Song. "Bio-inspired routing for heterogeneous Unmanned Aircraft Systems (UAS) swarm networking." Computers & Electrical Engineering 95 (October 2021): 107401. http://dx.doi.org/10.1016/j.compeleceng.2021.107401.

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Castillo, Mauricio, Ricardo Soto, Broderick Crawford, Carlos Castro, and Rodrigo Olivares. "A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models." Mathematics 9, no. 12 (June 18, 2021): 1417. http://dx.doi.org/10.3390/math9121417.

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Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.

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Boudia, Mohamed Amine, Reda Mohamed Hamou, and Abdelmalek Amine. "Comparative Study Between Two Swarm Intelligence Automatic Text Summaries." International Journal of Applied Metaheuristic Computing 9, no. 1 (January 2018): 15–39. http://dx.doi.org/10.4018/ijamc.2018010102.

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This article is a comparative study between two bio-inspired approach based on the swarm intelligence for automatic text summaries: Social Spiders and Social Bees. The authors use two techniques of extraction, one after the other: scoring of phrases, and similarity that aims to eliminate redundant phrases without losing the theme of the text. While the optimization use the bio-inspired approach to performs the results of the previous step. Its objective function of the optimization is to maximize the sum of similarity between phrases of the candidate summary in order to keep the theme of the text, minimize the sum of scores in order to increase the summarization rate; this optimization also will give a candidate's summary where the order of the phrases changes compared to the original text. The third and final step concerned in choosing a best summary from all candidates summaries generated by optimization layer, the authors opted for the technique of voting with a simple majority.

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Bouarara, Hadj Ahmed, Reda Mohamed Hamou, and Abdelmalek Amine. "Novel Bio-Inspired Technique of Artificial Social Cockroaches (ASC)." International Journal of Organizational and Collective Intelligence 5, no. 2 (April 2015): 47–79. http://dx.doi.org/10.4018/ijoci.2015040103.

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This paper deals about a new bio-inspired algorithm that can be classified in the family of swarm optimization algorithms. The authors' algorithm, which is called Artificial Social Cockroaches (ASC), is inspired from the social behaviour of cockroaches. This inspiration is based on the general phenomenon of real cockroaches that resides in grouping them under the same shelter (place with less lightness) and the way of choosing which shelter and how to get into it. This algorithm has as input a population of artificial cockroaches that will cooperate among them from iteration to another to solve a specific problem using simple rules as the attraction method and the aggregation operators (interaction, individual preference and evaluation). In order to evaluate our algorithm, the authors confronted several experiments facing clustering problem by applying this model on Reuters benchmark and basing on three essential measures: number of Bell, number of Stirling and time complexity.

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Boutekkouk, Fateh. "Real-Time Embedded Systems Scheduling Optimization." International Journal of Applied Evolutionary Computation 12, no. 1 (January 2021): 43–73. http://dx.doi.org/10.4018/ijaec.2021010104.

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The embedded real-time scheduling problem is qualified as a hard multi-objective optimization problem under constraints since it should compromise between three key conflictual objectives that are tasks deadlines guarantee, energy consumption reduction, and reliability enhancement. On this fact, conventional approaches can easily fail to find a good tradeoff in particular when the design space is too vast. On the other side, bio-inspired meta-heuristics have proved their efficiency even if the design space is very large. In this framework, the authors review the most pertinent works of literature targeting the application of bio-inspired methods to resolve the real-time scheduling problem for embedded systems, notably artificial immune systems, machine learning, cellular automata, evolutionary algorithms, and swarm intelligence. A deep discussion is conducted putting the light on the main challenges of using bio-inspired methods in the context of embedded systems. At the end of this review, the authors highlight some of the future directions.

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Fortes, Anderson, Luiz Antonio Da Silva Jr, Robson Domanski, and Alessandro Girardi. "Two-Stage OTA Sizing Optimization Using Bio-Inspired Algorithms." Journal of Integrated Circuits and Systems 14, no. 3 (December 27, 2019): 1–10. http://dx.doi.org/10.29292/jics.v14i3.74.

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The analog part of a mixed-signal integrated circuit represents a great amount of the circuit sizing effort. It is necessary to size each device separately and, in cases with several variables, the design space becomes quite large. The analog integrated circuit sizing can be modeled as an optimization problem and solved by optimization heuristics. In this work, we compare three bio-inspired heuristics to size a two-stage CMOS Miller operational transconductance amplifier: Particle Swarm Optimization (PSO), Cuckoo Search (CS) and Firefly Algorithm (FA). The goal is to evaluate the applicability of these heuristics for the analog sizing problem and to determine the best configuration of the algorithms parameters for optimizing performance of the generated circuit, mainly power consumption and silicon area. Results show that PSO and CS are more suitable to find optimized solutions, while FA presents less efficient exploration of the design space. Although PSO is faster and generates good solutions, the best overall solution was achieved with CS algorithm.

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Menaga, D., and I. Humaira Begum. "Bio-Inspired Algorithms for Preserving the Privacy of Data." Journal of Computational and Theoretical Nanoscience 17, no. 11 (November 1, 2020): 4971–79. http://dx.doi.org/10.1166/jctn.2020.9279.

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Security of the data is also concerned with the privacy of the data since the data or the information can be easily disclosed. Data sharing also plays a key role in security. Recently, patterns are disclosed using associative rule mining and the sensitive information are one of the imposing threats to the security aspects in data mining. Preserving the data as well as the privacy of the user using several PPDM approaches leads to provide authorized access for such sensitive information. The security threats for preserving privacy are provided by developing a sanitization process. The sanitization process is considered to be one of the biggest challenges in the mining of data. In this paper, different approaches such as GA-based and PSO based algorithms are surveyed and analyzed for preserving the privacy of data. The purpose of data sanitization and the use of Bio-Inspired algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are discussed.

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Xie, Yuxin, Liang Han, Xiwang Dong, Qingdong Li, and Zhang Ren. "Bio-inspired adaptive formation tracking control for swarm systems with application to UAV swarm systems." Neurocomputing 453 (September 2021): 272–85. http://dx.doi.org/10.1016/j.neucom.2021.05.015.

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Yin, Zhuang Wei, Hai Shen, Yu Fu Deng, and Mo Zhang. "Lifecycle-Based Swarm Optimization for Constrained Problem of Engineering." Applied Mechanics and Materials 281 (January 2013): 710–14. http://dx.doi.org/10.4028/www.scientific.net/amm.281.710.

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There are many constrained optimization problems in engineering. Bio-inspired optimization algorithms have been widely used to solve various engineering problems. This paper presents a novel optimization algorithm called Lifecycle-based Swarm Optimization, inspired by biology life cycle. LSO algorithm imitates biologic life cycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on a Vehicle Routing Problem with Time Windows for demonstration the effectiveness and stability. The results demonstrate remarkable performance of the LSO algorithm on chosen case when compared to two successful optimization techniques.

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OYEKAN, JOHN O., HUOSHENG HU, and DONGBING GU. "BIO-INSPIRED COVERAGE OF INVISIBLE HAZARDOUS SUBSTANCES IN THE ENVIRONMENT." International Journal of Information Acquisition 07, no. 03 (September 2010): 193–204. http://dx.doi.org/10.1142/s0219878910002154.

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Inspired by the simplicity of how nature solves its problems, a controller based upon the bacteria chemotaxis behavior and flocking of starlings in nature is developed and presented. It would enable the localization and subsequent mapping of pollutants in the environment. The pollutants could range from chemical leaks to invisible air borne hazardous materials. Simulation is used to explore the feasibility of the proposed controller and then a brief discussion on how to implement it onto a real robotic platform is presented. By using the advantages offered by swarm robotics, it is possible to achieve a collective mapping of an invisible pollutant spread over a large area. The approach presented is very simple, computational efficient, easily tuned and yet highly effective (desirable characteristics of biological systems) in generating a representation of an invisible pollutant.

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Anandakumar, H., and K. Umamaheswari. "A bio-inspired swarm intelligence technique for social aware cognitive radio handovers." Computers & Electrical Engineering 71 (October 2018): 925–37. http://dx.doi.org/10.1016/j.compeleceng.2017.09.016.

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Braik, Malik Shehadeh. "Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems." Expert Systems with Applications 174 (July 2021): 114685. http://dx.doi.org/10.1016/j.eswa.2021.114685.

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48

Wu and Feng. "On-Off Control of Range Extender in Extended-Range Electric Vehicle using Bird Swarm Intelligence." Electronics 8, no. 11 (October 26, 2019): 1223. http://dx.doi.org/10.3390/electronics8111223.

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The bird swarm algorithm (BSA) is a bio-inspired evolution approach to solving optimization problems. It is derived from the foraging, defense, and flying behavior of bird swarm. This paper proposed a novel version of BSA, named as BSAII. In this version, the spatial distance from the center of the bird swarm instead of fitness function value is used to stand for their intimacy of relationship. We examined the performance of two different representations of defense behavior for BSA algorithms, and compared their experimental results with those of other bio-inspired algorithms. It is evident from the statistical and graphical results highlighted that the BSAII outperforms other algorithms on most of instances, in terms of convergence rate and accuracy of optimal solution. Besides the BSAII was applied to the energy management of extended-range electric vehicles (E-REV). The problem is modified as a constrained global optimal control problem, so as to reduce engine burden and exhaust emissions. According to the experimental results of two cases for the new European driving cycle (NEDC), it is found that turning off the engine ahead of time can effectively reduce its uptime on the premise of completing target distance. It also indicates that the BSAII is suitable for solving such constrained optimization problem.

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Akkar, Hanan A. R., and Sameem A. Salman. "Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent." Engineering and Technology Journal 38, no. 2A (February 25, 2020): 255–64. http://dx.doi.org/10.30684/etj.v38i2a.319.

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Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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Zhao, Jian, Jia Qing Luo, Jian Wu, Wei Wen Deng, and Bing Zhu. "Optimal Distribution Strategy for Vehicle Active Yaw Moment Using Bio-Inspired Computing." Applied Mechanics and Materials 461 (November 2013): 961–66. http://dx.doi.org/10.4028/www.scientific.net/amm.461.961.

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This article aims to propose an optimized active yaw moment distribution strategy to improve vehicle safety and stability effectively. A controller based on a 2 DOF vehicle model and a PID controller is designed for the target active yaw moment, which is further allocated into longitudinal tire forces optimally by a particle swarm optimization (PSO) algorithm. The optimal distribution strategy is analyzed using Carsim and Matlab/Simulink co-simulation. The results show that the vehicle handling and stability are improved effectively through the lower workload of the actuators by the proposed control strategy.

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Journal articles on the topic 'Bio-inspired swarms'

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