Journal articles: 'Swarm Intelligence'

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Hinchey, Michael G., Roy Sterritt, and Chris Rouff. "Swarms and Swarm Intelligence." Computer 40, no. 4 (April 2007): 111–13. http://dx.doi.org/10.1109/mc.2007.144.

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Vehlken, Sebastian. "Pervasive Intelligence." Digital Culture & Society 4, no. 1 (March 1, 2018): 107–32. http://dx.doi.org/10.14361/dcs-2018-0108.

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Abstract This article seeks to situate collective or swarm robotics (SR) on a conceptual pane which on the one hand sheds light on the peculiar form of AI which is at play in such systems, whilst on the other hand it considers possible consequences of a widespread use of SR with a focus on swarms of Unmanned Aerial Systems (Swarm UAS). The leading hypothesis of this article is that Swarm Robotics create a multifold “spatial intelligence”, ranging from the dynamic morphologies of such collectives via their robust self-organization in changing environments to representations of these environments as distributed 4D-sensor systems. As is shown on the basis of some generative examples from the field of UAS, robot swarms are imagined to literally penetrate space and control it. In contrast to classical forms of surveillance or even “sousveillance”, this procedure could be called perveillance.

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Issayeva, G. B. Issayeva, M. S. Ibraev, A. K. Koishybekova, B. R. Absatarova, A. A. Aitkazina, Sh P. Sh.P. Zhumagulova, N. Vodolazkina, and Z. M. Ibraeva. "SWARM INTELLIGENCE." EurasianUnionScientists 6, no. 8(77) (September 13, 2020): 9–13. http://dx.doi.org/10.31618/esu.2413-9335.2020.6.77.998.

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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.

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Wanka, Rolf. "Swarm intelligence." it - Information Technology 61, no. 4 (August 27, 2019): 157–58. http://dx.doi.org/10.1515/itit-2019-0034.

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Tarasewich, Peter, and Patrick R. McMullen. "Swarm intelligence." Communications of the ACM 45, no. 8 (August 2002): 62–67. http://dx.doi.org/10.1145/545151.545152.

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Dorigo, Marco, and Mauro Birattari. "Swarm intelligence." Scholarpedia 2, no. 9 (2007): 1462. http://dx.doi.org/10.4249/scholarpedia.1462.

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Shatkin, Max A. "The problem of privacy in a digital society of swarm intelligence." Izvestiya of Saratov University. Philosophy. Psychology. Pedagogy 24, no. 1 (March 21, 2024): 62–66. http://dx.doi.org/10.18500/1819-7671-2024-24-1-62-66.

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Introduction. The development of digital technologies requires the specification of the type of the emerging digital society. One of the development scenarios is the formation of a digital society of swarm intelligence. Theoretical analysis. The concept of swarm intelligence means an optimization algorithm that imitates the behavior of swarms or colonies of insects and bird flocks. Human society is characterized by the manifestation of swarm intelligence as a condition of joint organized activity, but the development of digital technologies brings swarm intelligence to a higher level. Creation of private swarms causes risks in the form of “Byzantine robot” – a hacked particle of the swarm, transmitting false information and jeopardizing the whole swarm. The solution is to incorporate elements of the economic game that support the values of honesty, cooperation and solidarity into the particle interactions. This leads to making privacy a “process” and changing its value status. Protecting privacy in swarm intelligence society has the potential to cause labor to lose its public status and acquire a private, hidden status and anonymize employment. Conclusion. The possible scenario of human society development towards the digital society of swarm intelligence can become a source of profound social changes and, above all, a rethinking of the social stratification based on the division of labor and social hierarchy in general.

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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 (July 31, 2023): 1052–57. http://dx.doi.org/10.22214/ijraset.2023.54799.

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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

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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 (May 10, 2024): 101–10. http://dx.doi.org/10.25139/inform.v9i2.7934.

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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.

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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 (July 27, 2023): 10–27. http://dx.doi.org/10.48161/qaj.v3n2a141.

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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.

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Mashingaidze, Sivave. "Benefits of collective intelligence: Swarm intelligent foraging, an ethnographic research." Journal of Governance and Regulation 3, no. 4 (2014): 193–201. http://dx.doi.org/10.22495/jgr_v3_i4_c2_p2.

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Wisdom of crowds; bees, colonies of ants, schools of fish, flocks of birds, and fireflies flashing synchronously are all examples of highly coordinated behaviors that emerge from collective, decentralized intelligence. This article is an ethnographic study of swarm intelligence foraging of swarms and the benefits derived from collective decision making. The author used using secondary data analysis to look at the benefits of swarm intelligence in decision making to achieve intended goals. Concepts like combined decision making and consensus were discussed and four principles of swarm intelligence were also discussed viz; coordination, cooperation, deliberation and collaboration. The research found out that collective decision making in swarms is the touchstone of achieving their goals. The research further recommended corporate to adopt collective intelligence for business sustainability

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Schranz, M., and M. Sende. "Modeling Swarm Intelligence Algorithms for CPS Swarms." ACM SIGAda Ada Letters 40, no. 1 (October 20, 2020): 64–73. http://dx.doi.org/10.1145/3431235.3431240.

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Gonsalves, Tad. "Two Diverse Swarm Intelligence Techniques for Supervised Learning." International Journal of Swarm Intelligence Research 6, no. 4 (October 2015): 55–66. http://dx.doi.org/10.4018/ijsir.2015100103.

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Particle Swarm Optimization (PSO) and Enhanced Fireworks Algorithm (EFWA) are two diverse optimization techniques of the Swarm Intelligence paradigm. The inspiration of the former comes from animate swarms like those of birds and fish efficiently hunting for prey, while that of the latter comes from inanimate swarms like those of fireworks illuminating the night sky. This novel study, aimed at extending the application of these two Swarm Intelligence techniques to supervised learning, compares and contrasts their performance in training a neural network to perform the task of classification on datasets. Both the techniques are found to be speedy and successful in training the neural networks. Further, their prediction accuracy is also found to be high. Except in the case of two datasets, the training and prediction accuracies of the Enhanced Fireworks Algorithm driven neural net are found to be superior to those of the Particle Swarm Optimization driven neural net.

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Wang, Guo-Yin, Dong-Dong Cheng, De-You Xia, and Hai-Huan Jiang. "Swarm Intelligence Research: From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence." Machine Intelligence Research 20, no. 1 (January 10, 2023): 121–44. http://dx.doi.org/10.1007/s11633-022-1367-7.

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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 (October 4, 2021): 111–18. http://dx.doi.org/10.35882/jeeemi.v3i3.1.

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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.

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Shi, Yuhui. "Developmental Swarm Intelligence." International Journal of Swarm Intelligence Research 5, no. 1 (January 2014): 36–54. http://dx.doi.org/10.4018/ijsir.2014010102.

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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.

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Chittka, L., and A. Mesoudi. "Insect Swarm Intelligence." Science 331, no. 6016 (January 27, 2011): 401–2. http://dx.doi.org/10.1126/science.1199780.

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Jacob, Christian J., Gerald Hushlak, Jeffrey E. Boyd, Paul Nuytten, Maxwell Sayles, and Marcin Pilat. "SwarmArt: Interactive Art from Swarm Intelligence." Leonardo 40, no. 3 (June 2007): 248–54. http://dx.doi.org/10.1162/leon.2007.40.3.248.

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Swarms of bees, colonies of ants, schools of fish, flocks of birds, and fireflies flashing synchronously are all examples of highly coordinated behaviors that emerge from collective, decentralized intelligence. Local interactions among a multitude of agents or “swarmettes” lead to a variety of dynamic patterns that may seem like choreographed movements of a meta-organism. This paper describes SwarmArt, a collaborative project between several computer scientists and an artist, which resulted in interactive installations that explore and incorporate basic mechanisms of swarm intelligence. The authors describe the scientific context of the artwork, how user interaction is provided through video surveillance technology, and how the swarm-based simulations were implemented at exhibitions and galleries.

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Aziz, Nor Azlina Ab, Marizan Mubin, Zuwairie Ibrahim, and Sophan Wahyudi Nawawi. "Statistical Analysis for Swarm Intelligence — Simplified." International Journal of Future Computer and Communication 4, no. 3 (2015): 193–97. http://dx.doi.org/10.7763/ijfcc.2015.v4.383.

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Takanobu, Hideaki, Masumi Iida, Kenji Suzuki, Hirofumi Miura, Masanao Futakami, Tomohiro Endo, and Yoshinobu Inada. "Swarm Intelligence Robot : 3D swarm motion by airship and mobile robots." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 61–66. http://dx.doi.org/10.1299/jsmeicam.2010.5.61.

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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 (June 28, 2024): 15–24. http://dx.doi.org/10.24018/ejece.2024.8.3.619.

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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.

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M, Amsalakshmi, Madhumitha Prabhakaran, Gowtham S, Gautham Sidharth, and Jayasri V. "Swarm Robotics and Intelligence." International Research Journal of Computer Science 10, no. 06 (June 23, 2023): 225–32. http://dx.doi.org/10.26562/irjcs.2023.v1005.22.

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It's fascinating to observe how the concept of Swarm Intelligence in natural systems may be applied to human groupings, specifically in the realm of finance. According to the study you referenced, groups of financial traders were able to improve their accuracy in predicting weekly market indices by constructing online systems modeled after natural swarms. The increase in accuracy from 61% to 77% when predicting jointly as a real-times warm is a considerable improvement and demonstrates that the adoption of SI algorithms has the potential to considerably improve financial forecasting accuracy. It is also worth noting that the results' statistical significance (p=0.001) shows that the findings are reputable. Overall, the findings indicate that employing SI algorithms to allow groups of traders to construct real-time systems online could be a beneficial tool in the field of finance, potentially leading to more accurate financial predictions. More research is needed, however, to validate these findings and to investigate the possible applications and limitations of this approach.

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A.S., Baraniuk. "Research on applications and problem of control of swarm intelligence and robotics." Artificial Intelligence 25, no. 1 (March 9, 2020): 44–50. http://dx.doi.org/10.15407/jai2020.01.044.

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This article provides overview of the swarm intelligence and robotics fields, main characteristics of such systems provided, their advantages and disadvantages as well as differences from other multi-agent systems. Also, main fields of application for swarm systems with examples provided apart from short information on swarm optimizations. The problem of swarms’ control described and possible solutions for it such as algorithm replacement, parameters change, control through environment and leaders. Apart from that fields for possible future research noted.

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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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Rolling, James Haywood. "Swarm Intelligence and Collaboration." Art Education 69, no. 5 (August 15, 2016): 4–6. http://dx.doi.org/10.1080/00043125.2016.1201400.

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Vesilind, P. A. "Swarm intelligence [Book Review]." IEEE Technology and Society Magazine 21, no. 1 (2002): 9. http://dx.doi.org/10.1109/mtas.2002.993594.

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Zahiri, Seyed-Hamid, and Seyed-Alireza Seyedin. "Swarm intelligence based classifiers." Journal of the Franklin Institute 344, no. 5 (August 2007): 362–76. http://dx.doi.org/10.1016/j.jfranklin.2005.12.006.

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Bogue, Robert. "Swarm intelligence and robotics." Industrial Robot: An International Journal 35, no. 6 (October 17, 2008): 488–95. http://dx.doi.org/10.1108/01439910810909475.

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Yao, Baozhen, Rui Mu, and Bin Yu. "Swarm Intelligence in Engineering." Mathematical Problems in Engineering 2013 (2013): 1–3. http://dx.doi.org/10.1155/2013/835251.

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Goodarzi, Mohammad. "Swarm Intelligence for Chemometrics." NIR news 26, no. 7 (November 2015): 7–11. http://dx.doi.org/10.1255/nirn.1556.

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Rupinder Kaur, Vikas Kumar Garg and Shikha. "A Review on Various Soft Computing and Swarm Intelligence Techniques." International Journal for Modern Trends in Science and Technology 7, no. 07 (February 20, 2022): 81–85. http://dx.doi.org/10.46501/ijmtst051237.

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This paper gives you the overview of the Soft computing techniques i.e. Genetic algorithm (GA), Artificial neural networks (ANN) and some of Swarm Intelligence techniques i.e. Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) etc. Soft Computing is basically an optimization technique which helps to find the results of problems which are very difficult to reply. It is the integration of methodologies which are planned to represent and find the solutions to real world problems that are not represented or which are very hard to represent mathematically. Swarm Intelligence (SI) can be defined as a subfield of Artificial Intelligence which is used to represent the collaborative behavior of communal swarms in nature, such as ant colonies, honey bees, bird flocks, grey wolves, fireflies and cuckoo search. The term swarm is used for the collection of animals such as fish schools, bird flocks and insect colonies which use their sorroundings and services significantly to communicate by mutual intelligence. In this paper, all these algorithms are discussed in brief

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Sharma, Pallavi, and Rajesh Kochher. "Enhanced RZ-Leach using Swarm Intelligence Technique." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (February 28, 2018): 693–700. http://dx.doi.org/10.31142/ijtsrd8315.

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Weichert, Stephan. "From Swarm Intelligence to Swarm Malice: An Appeal." Social Media + Society 2, no. 1 (January 6, 2016): 205630511664056. http://dx.doi.org/10.1177/2056305116640560.

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Boyle, Jordan H. "An interactive simulation of control and coordination strategies for swarms of autonomous construction robots." SPOOL 11, no. 1 (July 20, 2024): 5–22. http://dx.doi.org/10.47982/spool.2024.1.01.

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There is an established idea – found in science fiction, architectural studios, and scientific papers alike – of stainable buildings crafted from bio-based materials, colonized by plant and animal life, and blending seamlessly into the natural surroundings. Such buildings might one day be built, maintained and remodelled by swarms of autonomous robots, allowing them to evolve in response to the changing needs of their inhabitants. Inspired by that vision, this paper contributes to the field of swarm intelligence with a focus on robotic construction and human-swarm interaction. Along with a short literature review on robotic building, swarm intelligence and biocompatible building materials, the paper presents an open-source simulation of abstracted termite-like swarm construction. The focus is mainly on human-swarm interaction, specifically how to influence the emergent behaviour of an autonomous swarm in order to elicit a desired outcome while retaining the robustness and adaptability of a self-organized system. The simulator is used to demonstrate a set of four autonomous swarm behaviours that are representative of construction tasks.

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Zaur Alakbarov, Lala Bekirova, Zaur Alakbarov, Lala Bekirova. "UAV SWARM SYSTEMS." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 34, no. 11 (November 2, 2023): 274–84. http://dx.doi.org/10.36962/pahtei34112023-274.

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The rapid development of technology has introduced new and groundbreaking approaches to unmanned aerial vehicles (UAVs). One such innovation is the swarm UAV systems. These systems represent a technology where multiple small and often autonomous aircraft collaborate to perform specific tasks. An important component of this system is artificial intelligence. Swarm UAV systems can operate more effectively and coordinatedly by utilising artificial intelligence algorithms. Each UAV is equipped with autonomous control systems and artificial intelligence software. Through data analysis, object recognition, route planning, environmental adaptation and other complex tasks, artificial intelligence aids these UAVs in successfully completing tasks. As a result, swarm UAVs are capable of performing more complex and dynamic missions. The integration of artificial intelligence into swarm UAV systems holds great potential for several application fields. For instance, in search and rescue operations, AI can perform image analysis to detect individuals trapped under debris. In agriculture applications, AI can detect plant diseases or pests and optimize irrigation requirements. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. In this article, we will examine what swarm UAV systems are, how they operate, and the contribution of integrating artificial intelligence to this technology. Additionally, we will focus on the potential future application areas of swarm UAV systems and the potential impacts of this integration. Swarm UAV systems and artificial intelligence represent a significant transformation in the world of technology, and this article will provide a resource to better understand these exciting developments. Keywords: Research of UAVs, integration of artificial intelligence, flight programs, autonomous control.

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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 (December 1, 2021): 1–5. http://dx.doi.org/10.2478/cjece-2021-0006.

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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.

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R., Gohilai, and Prashanth K. "Artificial Intelligence Based MPPT Techniques of Photo Voltaic System." International Journal of Innovative Research in Advanced Engineering 10, no. 07 (July 31, 2023): 518–22. http://dx.doi.org/10.26562/ijirae.2023.v1007.13.

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In this paper, Particle Swarm Optimisation (PSO) investigates the global optimal solution by taking advantage of the memory of the particle and the swarm. PSO has evolved into one of the most significant Swam Intelligence techniques and Evolutionary Computation algorithms due to its characteristics of low constraint on the continuity of goal function and joint of search space, and capacity to adapt to dynamic environments. The development of algorithms over the years is then discussed, along with applications in multi-objective optimisation, neural networks, electronics, etc. The remaining issues and potential prospects for PSO research are then examined. One of the concepts of swarm intelligence introduced in the field of computing and artificial intelligence is particle swarm optimisation (PSO). PSO is a novel collective and distributed intelligent paradigm for problem solving, primarily in the field of optimisation, without centralized control or the provision of a global model. In this work, the basic PSO, its improvements, its applications to various systems, including electric power systems, and its premature convergence as well as its combination with other intelligent algorithms to enhance search capacity and shorten the time required to exit local optimums are all thoroughly reviewed.

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A.I., Kupin, and Kosei M.P. "Analysis of swarm intelligence algorithms." System technologies 3, no. 152 (April 17, 2024): 69–80. http://dx.doi.org/10.34185/1562-9945-3-152-2024-07.

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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.

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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.

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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.

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Banarse, Onasvee. "Autonomous UAV Swarms: Powering Up with AI." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 3513–21. http://dx.doi.org/10.22214/ijraset.2023.54336.

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Abstract: This study offers a thorough analysis of autonomous UAV swarming, focusing on its merits, important technologies, applications, difficulties, and prospects for the future. The idea of autonomous UAV swarming entails the coordination and decentralised cooperation of several UAVs to accomplish common objectives. The foundations of UAV swarming are thoroughly covered, including its essential ideas, advantages, architectures, and swarm behaviour. We emphasise the role of artificial intelligence (AI), more especially reinforcement learning (RL) and particle swarm optimisation (PSO), in promoting thoughtful decision-making, task allocation, and coordination within UAV swarms. The study examines the many uses of autonomous UAV swarming in fields including infrastructure inspection, search and rescue, agriculture, and disaster response. It also discusses UAV swarming's difficulties and restrictions, such as scalability, communication, fault tolerance, and ethical issues. In addition to standardisation initiatives, the study suggests future research possibilities for swarm intelligence, edge computing, cognitive capacities, and human swarm interaction.

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REBOLLO, ISRAEL, MANUEL GRAÑA, and BLANCA CASES. "EFFECT OF SPATIAL PERCOLATION ON THE CONVERGENCE OF A GRAPH COLORING BOID SWARM." International Journal on Artificial Intelligence Tools 21, no. 06 (December 2012): 1250015. http://dx.doi.org/10.1142/s0218213012500157.

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Graph Coloring Boid Swarm (GCBS) is the successful application of Reynolds' Boid Swarm to solve the graph coloring problem, following appropriate mapping of the problem into boids' behaviors and interpretation of the global swarm states as problem solutions. A population P of boids moves in a closed torus-shaped space S. Each individual boid perceives a disk of radius R of its surrounding space, being able to exchange information with other boids lying inside this area of perception. Two mutually perceiving boids exchanging information are connected. In this paper we show that the ratio of the radius of perception to the space size can be critical for the convergence of the Boid Swarm to the optimal configurations. First, we derive the Percolation threshold for Elementary Boid Swarms (EBS), whose dynamics are the aggregation of the boids into spatial clusters. When the radius of perception is above this percolation threshold for Elementary Boid Swarms (EBS), whose dynamics are the aggregation of the boids into spatial clusters. When the radius of perception is above this percolation threshold there is complete connectivity and convergence to a single cluster. Second, we show empirically the existence of such a Percolation effect on the Graph Coloring Boid Swarm (GCBS). We find that complete connectivity has an adverse effect on the performance of the GCBS. Finally we show a comparison of GCBS with some other algorithms over a family of generated graphs.

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Kano, Takeshi, and Yuichiro Sueoka. "Special Issue on Design of Swarm Intelligence Through Interdisciplinary Approach." Journal of Robotics and Mechatronics 35, no. 4 (August 20, 2023): 889. http://dx.doi.org/10.20965/jrm.2023.p0889.

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In biological and social systems, a “swarm” refers to a group of individual units that behave as a single intelligent entity. “Swarm behavior,” the collective result of the local interactions among the group members, exhibits what is called “swarm intelligence.” By identifying the design principles of such swarm intelligence, we may be able to create swarm robots that are highly adaptable, fault tolerant, and dimensionally flexible. An interdisciplinary approach, including disciplines ranging from technology to biology to the mathematical sciences, for example, is used to elucidate the design principles of swarm intelligence. We believe that such knowledge will lead to transformations in the field of swarm robotics. This special issue highlights 19 exciting papers, including 13 research papers, five review papers, and one letter. Some papers focus on understanding the mechanism of real swarm phenomena, while the other papers focus on designing intelligent swarm systems. The keywords of the papers are as follows. • Swarm intelligence • Interdisciplinary approach • Decentralized control • Swarm robot • Collective behavior We would like to express our gratitude to all authors and reviewers, and we hope that this special issue contributes to future research and development in swarm intelligence.

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Kutsenok, Alex, and Victor Kutsenok. "Swarm AI: A General-purpose Swarm Intelligence Design Technique." Design Principles and Practices: An International Journal—Annual Review 5, no. 1 (2011): 7–16. http://dx.doi.org/10.18848/1833-1874/cgp/v05i01/37798.

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Apostolidis, Georgios K., and Leontios J. Hadjileontiadis. "Swarm decomposition: A novel signal analysis using swarm intelligence." Signal Processing 132 (March 2017): 40–50. http://dx.doi.org/10.1016/j.sigpro.2016.09.004.

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Eichmann, Christian, and Carsten Mueller. "Team Formation Based on Nature-Inspired Swarm Intelligence." Journal of Software 10, no. 3 (March 2015): 344–54. http://dx.doi.org/10.17706/jsw.10.3.344-354.

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Reddy, Kumeshan, and Akshay Kumar Saha. "An Investigation into the Utilization of Swarm Intelligence for the Design of Dual Vector and Proportional–Resonant Controllers for Regulation of Doubly Fed Induction Generators Subject to Unbalanced Grid Voltages." Energies 15, no. 20 (October 11, 2022): 7476. http://dx.doi.org/10.3390/en15207476.

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This work presents an investigation into the use of swarm intelligence techniques for the control of the doubly fed induction generator under unbalanced grid voltages. Swarm intelligence is a concept that was introduced in the late 20th century but has since undergone constant evolution and modifications. Similarly, the doubly fed induction generator has recently come under intense investigation. Owing to the direct grid connection of the DFIG, an unbalanced grid voltage harshly impacts its output power. Established mitigation measures include the use of the dual vector and proportional–resonant control methods. This work investigates the effectiveness of utilizing swarm intelligence for the purpose of controller gain optimization. A comparison of the application of swarm intelligence to the dual vector and proportional–resonant controllers was carried out. Three swarm intelligence techniques from across the timeline were utilized including particle swarm optimization, the bat algorithm, and the gorilla troops optimization algorithm. The system was subject to single-phase voltage dips of 5% and 10%. The results indicate that modern swarm intelligence techniques are effective at optimizing controller gains. This shows that as swarm intelligence techniques evolve, they may be suitable for use in the optimization of controller gains for numerous applications.

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Talamali, Mohamed S., Thomas Bose, Matthew Haire, Xu Xu, James A. R. Marshall, and Andreagiovanni Reina. "Sophisticated collective foraging with minimalist agents: a swarm robotics test." Swarm Intelligence 14, no. 1 (October 10, 2019): 25–56. http://dx.doi.org/10.1007/s11721-019-00176-9.

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Abstract How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here, we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarm-size-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour.

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Kusuma, Purba Daru, and Faisal Candrasyah Hasibuan. "Half mirror algorithm: a metaheuristic that hybridizes swarm intelligence and evolution-based system." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (June 1, 2024): 3320. http://dx.doi.org/10.11591/ijece.v14i3.pp3320-3331.

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This paper promotes a new metaheuristic called the half mirror algorithm (HMA). As its name suggests, HMA offers a new kind of mirroring search. HMA is developed by hybridizing swarm intelligence and the evolution system. Swarm intelligence is adopted by constructing several autonomous agents called swarms. On the other hand, the evolution system is adopted using arithmetic crossover based on a particular reference called a mirror. Four mirrors are used in HMA: the best swarm member, a randomly selected swarm member, the central point of the space, and the corresponding swarm member. During the confrontative assessment, HMA is confronted with average and subtraction-based optimization (ASBO), total interaction algorithm (TIA), walrus optimization algorithm (WaOA), coati optimization algorithm (COA), and clouded leopard optimization (CLO). The result shows that HMA is superior to ASBO, TIA, WaOA, COA, and CLO in 20, 19, 19, 20, and 20 out of 23 functions, respectively. Moreover, HMA has found the global optimal of eight functions. It means the superiority of HMA occurs in almost entire functions. In the future, the mirroring search can be combined with the guided and neighborhood search to construct a more powerful metaheuristic.

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A., Nathaniel, and Eva N. "Malaria Diagnosis using Swarm Intelligence." International Journal of Computer Applications 181, no. 9 (August 14, 2018): 24–28. http://dx.doi.org/10.5120/ijca2018917602.

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L, Meghana, and Jaya R. "Swarm Intelligence Algorithms - A Survey." International Journal of Computer Sciences and Engineering 6, no. 2 (February 28, 2018): 184–88. http://dx.doi.org/10.26438/ijcse/v6i2.184188.

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Journal articles on the topic 'Swarm Intelligence'

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