Swarm intelligence: problem-solving societies (a review)
Inteligencia de enjambres: sociedades para la solución de problemas (una revisión)
DOI:
https://doi.org/10.15446/ing.investig.v28n2.14901Keywords:
computational intelligence, evolutionary computing, optimisation algorithm, swarm intelligence (en)algoritmos de optimización, computación evolutiva, inteligencia computacional, inteligencia de enjambres (es)
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This paper presents a review of the basic concepts of swarm intelligence and some views regarding the future of research in this area aimed at establishing a starting point for future work in different engineering fields. A bibliographic search of the most updated databases regarding classic articles on the subject and the most recent applications and results was used for constructing this review, especially in the areas of automatic control, signal and image processing and robotics. The main concepts were selected and organised in chronological order. A taxonomy was obtained for evolutionary computing techniques, a clear differentiation between swarm intelligence and other evolutionary algorithms and an overview of the different techniques and applications.
En este artículo se presenta una revisión de los conceptos de inteligencia de enjambres, y algunas perspectivas en la investigación con estas técnicas, con el objetivo de establecer un punto de partida para trabajos futuros en diferentes áreas de la ingeniería. Para la construcción de esta revisión se llevó a cabo una búsqueda bibliográfica en las bases de datos más actualizadas de los artículos clásicos del tema y de las últimas aplicaciones y resultados publicados, en particular en las áreas de control automático, procesamiento de señales e imágenes, y robótica, extrayendo su concepto más relevante y organizándolo de manera cronológica. Como resultado se obtuvo taxonomía de la computación evolutiva, la diferencia entre la inteligencia de enjambres y otros algoritmos evolutivos, y una visión amplia de las diferentes técnicas y aplicaciones.
References
Abbass, H. A., Teo, J., A true annealing approach to the marriage in honey-bees optimization algorithm., Memorias del Inaugural workshop on Artificial Life (AL’01), 2001.
Alfonso, W., Regulación de temperatura en la plataforma UV–PTM01 basada en agentes cooperativos para la asignación dinámica de recursos., tesis presentada a la Universidad del Valle para optar al grado de Ingeniero Electrónico, 2007.
Alfonso, W., Muñoz, M., López, J., Caicedo, E., Optimización de funciones inspirada en el comportamiento de búsqueda de néctar en abejas., Memorias del Congreso Internacional de Inteligencia Computacional (CIIC2007), 2007.
Bartz-Beielstein, T., Parsopoulos, K., Vrahatis, M., Analysis of particle swarm optimization using computational statistics., Memorias del International Conference of Numerical Analysis and Applied Mathematics (ICNAAM), 2004, pp. 34–37.
Beattie, P., Bishop, J., Self-localisation in the ‘scenario’ autonomous wheelchair., Journal of Intelligent and Robotic Systems, Vol. 22, 1998, pp. 255–267. DOI: https://doi.org/10.1023/A:1008033229660
Bilchev, G., Parmee, I., The ant colony metaphor for searching continuous design spaces., Evolutionary Computing, Selected Papers AISB Workshop, Vol. 993 of Lecture Notes in Computer Science, T. Fogarty (ed.), Springer, 1995, pp. 25–39. DOI: https://doi.org/10.1007/3-540-60469-3_22
Bishop, J., Anarchic Techniques for Pattern Classification, tesis presentada a la University of
Reading para optar al grado de Doctor of Philosophy, 1989a.
Bishop, J., Stochastic searching Networks., Memorias del First IEE Conference on Artificial Neural Networks, 1989b, pp. 329–331.
Bishop, J., Nasuto, S., Communicating neurons – an alternative connectionism., Memorias del Weightless Neural Networks Workshop, 1999.
Bishop, J., Torr, P., The stochastic search network., Neural Networks for Images, Speech and Natural Language, R. Linggard, D. Myers y C. Nightingale (eds.), Chapman & Hall, 1992, pp. 370–387. DOI: https://doi.org/10.1007/978-94-011-2360-0_24
Bremermann, H., Chemotaxis and optimization., Journal of the Franklin Institute, Vol. 297, No. 5, 1974, pp. 397– 404. DOI: https://doi.org/10.1016/0016-0032(74)90041-6
Bullnheimer, B., Hartl, R. F., Strauss, C., A new rank-based version of the ant system: a computational study., Technical Report POM-03/97, Institute of Management Science, University of Vienna, 1997.
Clerc, M., The swarm and the queen: towards a deterministic and adaptive particle swarm optimization., Memorias del 1999 Congress on Evolutionary Computation (CEC 99), 1999.
Clerc, M., Kennedy, J., The particle swarm–explosion, stability, and convergence in a multidimensional complex space., IEEE Transactions on Evolutionary Computation, Vol 6, No. 1, 2002, pp. 58–73. DOI: https://doi.org/10.1109/4235.985692
Colorni, A., Dorigo, M., Maniezzo, V., Distributed optimization by ant colonies., Memorias del European Conference on Artificial Life (ECAL91), Elsevier Publishing, 1991, pp. 134–142.
Colorni, A., Dorigo, M., Maniezzo, V., An investigation of some properties of an “ant algorithm”., Memorias del Parallel Problem Solving from Nature Conference, Elsevier Publishing, 1992, pp. 509–520.
Cordón, O., de Viana, I. F., Herrera, F., Moreno, L., A new aco model integrating evolutionary computation concepts: The best-worst ant system., Memorias del ANTS 2000 - From Ant Colonies to Artificial Ants: Third International Workshop on Ant Algorithms, 2000, pp. 22–29.
De Meyer, K., Bishop, J., Nasuto, S., Stochastic diffusion: Using recruitment for search., Memorias del Symposium on Evolvability & Interaction, 2003.
De Vries, H., Biesmeijer, J. C., Modelling collective foraging by means of individual behaviour rules in honey-bees., Behav Ecol Sociobiol, Vol. 44, 1998, pp. 109–124. DOI: https://doi.org/10.1007/s002650050522
Dorigo, M., Gambardella, L. M., A study of some properties of ANT-Q., Memorias del Fourth International Conference on Parallel Problem Solving from Nature, H. M. Voigt, W. Ebeling, I. Rechenberg y H. S. Schwefel (eds.), Springer Verlag, Berlin, Germany, 1996, pp. 656–665.
Dorigo, M., Gambardella, L. M., Ant colony system: A cooperative learning approach to the traveling salesman
problem., IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1, 1997, pp. 53–66. DOI: https://doi.org/10.1109/4235.585892
Dorigo, M., Maniezzo, V., Colorni, A., Ant system: An autocatalytic optimizing process., Technical Report 91-016, Politecnico di Milano, Italy, 1991a.
Dorigo, M., Maniezzo, V., Colorni, A., Positive feedback as a search strategy, Technical Report 91-016, Politecnico di Milano, Italy, 1991b.
Dorigo, M., Maniezzo, V., Colorni, A., The ant system: Optimization by a colony of cooperating agents., IEEE Transactions on Systems, Man, and Cybernetics-Part B, Vol 26, No. 1, 1996, pp. 1–13. DOI: https://doi.org/10.1109/3477.484436
Easter Selvan, S., Subramanian, S., Theban Solomon, S., Novel technique for PID tuning by particle swarm optimization. Memorias del Seventh Annual Swarm Users/ Researchers Conference (SwarmFest 2003), 2003.
Eberhart, R., Kennedy, J., A new optimizer using particle swarm theory., Memorias del Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39–43.
El-Gallad, A. I., El-Hawary, M., Sallam, A. A., Kalas, A., Swarm-intelligently trained neural network for power transformer protection., Memorias del Canadian Conference on Electrical and Computer Engineering, 2001, pp. 265–269.
Esmin, A. A. A., Aoki, A. R., Lambert-Torres, G., Particle swarm optimization for fuzzy membership functions optimi zation., Memorias del IEEE International Conference on Systems, Man and Cybernetics, 2002, pp. 108–113.
Gambardella, L. M., Dorigo, M., Ant-Q: A reinforcement learning approach to the traveling salesman problem, Memorias del Twelfth International Conference on Machine Learning, Morgan Kaufmann, 1995, pp. 252–260. DOI: https://doi.org/10.1016/B978-1-55860-377-6.50039-6
Gazi, V., Passino, K. M., Stability analysis of swarms., IEEE Transactions on Automatic Control Vol 48, No. 4, 2003, pp. 692–697. DOI: https://doi.org/10.1109/TAC.2003.809765
Gazi, V., Passino, K. M., Stability analysis of social foraging swarms., IEEE Transactions of Systems, Man and Cybernetics - Part B, Vol 34, No. 1, 2004, pp. 539–557. DOI: https://doi.org/10.1109/TSMCB.2003.817077
Grech-Cini, H., Locating Facial Features., tesis presentada a la University of Reading, para optar al grado de Doctor of Philosophy, 1995.
Grosan, C., Abraham, A., Han, S., Gelbukh, A., Hybrid particle swarm – evolutionary algorithm for search and optimization., Memorias del 4th Mexican International Conference on Artificial Intelligence (MICAI’05), Lecture Notes in Computer Science, 2005, pp. 623–632. DOI: https://doi.org/10.1007/11579427_63
Heegaard, P. E., Wittner, O., Incola, V. F., Helvik, B., Distributed asynchronous algorithm for cross-entropy based combinatorial optimization., Memorias del International Workshop on Rare Event Simulation & Combinatorial Optimization (RESIM 2004), 2004.
Hertz, A., Kobler, D., A framework for the description of evolutionary algorithms., European Journal of Operational Research, Vol 126, No. 1, 2000, pp. 1–12. DOI: https://doi.org/10.1016/S0377-2217(99)00435-X
Hinton, G., A parallel computation that assigns canonical objects–based frames of reference., Memorias del 7th International Joint Conference on Artificial Intelligence, 1981.
Ismail, A., Engelbrecht, A. P., Training product units in feedforward neural networks using particle swarm optimization. Memorias del International Conference on Artificial Intelligence, 1999, pp. 36–40.
Karaboga, D., An idea based on honey bee swarm for numerical optimization., Technical Report TR06, Erciyes University, 2005.
Kennedy, J., Eberhart, R., Particle swarm optimization., Memorias del IEEE International Conference on Neural Networks, 1995, pp. 1942–1498.
Kennedy, J., Eberhart, R., A discrete binary version of the particle swarm algorithm., Memorias del World Multiconference on Systemics, Cybernetics and Informatics, 1997, pp. 4104–4109.
Kim, D. H., Abraham, A., Cho, J. H., A hybrid genetic algorithm and bacterial foraging approach for global optimization., Information Sciences, Vol. 177, 2007, pp. 3918–3937. DOI: https://doi.org/10.1016/j.ins.2007.04.002
Kim, D. H., Cho, J. H., Adaptive tuning of PID controller for multivariable system using bacterial foraging based optimization., Advances in Web Intelligence, Vol. 3528 of Lecture Notes in Computer Science, Springer, 2005, pp. 231–235. DOI: https://doi.org/10.1007/11495772_36
Krohling, R. A., d.S. Coelho, L., Shi, Y., Cooperative particle swarm optimization for robust control system design., Memorias del 7th Online World Conference on Soft Computing in Industrial Applications, 2002. DOI: https://doi.org/10.1007/978-1-4471-3744-3_30
Leiviskä, K., Joensuu, I., Chemotaxis for controller tuning, Memorias del 2nd Annual Symposium of the Nature inspired Smart Information Systems (NiSIS 2006), 2006.
Leontitsis, A., Kontogiorgos, D., Pagge, J., Repel the swarm to the optimum! Applied Mathematics and Computation, Vol, 173, 2006, pp. 265–272. DOI: https://doi.org/10.1016/j.amc.2005.04.004
Liu, H., Abraham, A., Fuzzy adaptive turbulent particle swarm optimization., Memorias del Fifth International Conference on Hybrid Intelligent Systems, 2005, pp. 6–9.
Liu, Y., Passino, K. M., Biomimicry of social foraging bacteria for distributed optimization: Models, principles and emergent behaviors., Journal of Optimization Theory and Applications, Vol. 115, No. 3, 2002, pp. 603–628. DOI: https://doi.org/10.1023/A:1021207331209
Lucic, P., Teodorovic, D., Computing with bees: Attacking complex transportation engineering problems., International Journal on Artificial Intelligence Tools, Vol. 12, No. 3, 2003, pp. 375–394. DOI: https://doi.org/10.1142/S0218213003001289
Løvbjerg, M., Kink, T., Hybrid particle swarm optimiser with breeding and subpopulations., Memorias del Genetic and Evolutionary Computation Conference (GECCO), 2001.
Løvbjerg, M., Kink, T., Extending particle swarm optimizers with self-organized criticality., Memorias del IEEE Congress on Evolutionary Computation (CEC2002), 2002.
Meuleau, N., Dorigo, M., Ant colony optimization and stochastic gradient descent, Technical Report TR/IRIDIA/2000-36, IRIDIA, Université Libre de Bruxelles, Belgium, 2000.
Miranda, V., Fonseca, N., EPSO – evolutionary particle swarm optimization, a new algorithm with applications in power systems., Memorias del Asia Pacific Transmission and Distribution Conference and Exhibition, Vol. 2, 2002a, pp. 745–750.
Miranda, V., Fonseca, N., EPSO – best-of-two-worlds meta heuristic applied to power system problems., 2002 Congress on Evolutionary Computation (CEC02), Vol. 2, 2002b, pp. 1080–1085.
Müller, S. D., Marchetto, J., Airaghi, S., Koumoutsakos, P., Optimization based on bacterial chemotaxis., IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, 2002, pp. 16–29. DOI: https://doi.org/10.1109/4235.985689
Monson, C. K., Seppi, K. D., The kalman swarm: A new approach to particle motion in swarm optimization, Memorias del Genetic and Evolutionary Computation Conference 2004 (GECCO2004), 2004, pp. 140–150. DOI: https://doi.org/10.1007/978-3-540-24854-5_13
Muñoz, M., López, J., Caicedo, E., An artificial bee hive for continuous optimization., Aceptado en International Journal of Intelligent Systems, 2008.
Myatt, D., Bishop, J., Nasuto, S., Minimum stable convergence criteria for stochastic diffusion search., Electronics Letters, Vol, 40, No. 2, 2004, pp. 112–113. DOI: https://doi.org/10.1049/el:20040096
Nasuto, S., Resource Allocation Analysis of the Stochastic Diffusion Search., tesis presentada a la University of Reading para optar al grado de Doctor of Philosophy, 1999.
Nasuto, S., Bishop, J., Convergence analysis of stochastic diffusion search., Journal of Parallel Algorithms and Applications, Vol. 14, No. 2, 1999, pp. 89–107. DOI: https://doi.org/10.1080/10637199808947380
Nasuto, S., Bishop, J., Lauria, S., Time complexity of stochastic diffusion search., Memorias del Neural Computation ’98, 1998.
Nunez de Castro, L., Fundamentals of natural computing: an overview., Physics of Life Reviews Vol. 4, 2007, pp. 1– 36. DOI: https://doi.org/10.1016/j.plrev.2006.10.002
Omran, M. G. H., Engelbrecht, A. P., Salman, A., Dynamic clustering using particle swarm optimization with application in unsupervised image classification., Transactions on Engineering, Computing and Technology, Vol. 9, 2005, pp. 199–204.
Ozcan, E., Mohan, C. K., Particle swarm optimization: Surfing the waves., Memorias del IEEE Congress on Evolutionary Computation (CEC1999), 1999.
Passino, K. M., Distributed optimization and control using only a germ of intelligence., Memorias del 2000 IEEE International Symposium on Intelligent Control, 2000, pp. 5–13.
Passino, K. M., Biomimicry of bacterial foraging for distributed optimization and control., IEEE Control Systems Magazine, Vol. 22, No. 3, 2002, pp. 52–67. DOI: https://doi.org/10.1109/MCS.2002.1004010
Pham, D., Afify, A., Koç, E., Manufacturing cell formation using the bee’s algorithm., Memorias del Innovative Production Machines and Systems Virtual Conference, 2007.
Pham, D., Darwish, A. H., Eldukhr, E., Otri, S., Using the bee’s algorithm to tune a fuzzy logic controller for a robot gimnasta., Memorias del Innovative Production Machines and Systems Virtual Conference, 2007.
Pham, D., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., Zaidi, M., The bees algorithm – a novel tool for complex optimisation problems., Memorias del Innovative Production Machines and Systems Virtual Conference, 2006. DOI: https://doi.org/10.1016/B978-008045157-2/50081-X
Pham, D., Ghanbarzadeha, A., Multi–objective optimisation using the bee’s algorithm., Memorias del Innovative Production Machines and Systems Virtual Conference, 2007.
Pham, D., Koç, E., Ghanbarzadeh, A., Otri, S., Optimisation of the weights of multi–layered perceptrons using the bee’s algorithm, Memorias del 5th International Symposium on Intelligent Manufacturing Systems, 2006.
Pham, D., Muhamad, Z., Mahmuddin, M., Ghanbarzadeh, A., Koç, E., Otri, S., Using the bees algorithm to optimise a support vector machine for wood defect classification., Memorias del Innovative Production Machines and Systems Virtual Conference, 2007.
Poli, R., Langdon, W. B., Holland, O., Extending particle swarm optimization via genetic programming., Memorias del 8th European Conference on Genetic Programming, 2005, pp. 291–300. DOI: https://doi.org/10.1007/978-3-540-31989-4_26
Rubinstein, R., Combinatorial optimization, cross-entropy, ants and rare events., Stochastic Optimization: Algorithms and Applications, S. Uryasev y P. M. Pardalos (eds.), Kluwer Academic Publishers, 2001. DOI: https://doi.org/10.1007/978-1-4757-6594-6_14
Rumelhart, D., McClelland, J., Parallel Distributed Processing Vol. 1, MIT Press, 1986, pp. 113–118. Salerno, J., Using the particle swarm optimization technique to train a recurrent neural model., IEEE International Conference on Tools with Artificial Intelligence, 1997, pp. 45–49.
Shi, Y., Eberhart, R. C., Parameter selection in particle swarm optimization., Memorias del Seventh Annual Conference on Evolutionary Programming, 1998, pp. 591–600. DOI: https://doi.org/10.1007/BFb0040810
Stützle, T., Hoos, H., The max–min ant system and local search for the traveling salesman problem., Memorias del IEEE International Conference on Evolutionary Computation and Evolutionary Programming Conference (IEEE–ICEC–EPS’97), T. Baeck, Z. Michalewicz y X., ao (eds), IEEE Press, 1997, pp. 309–314.
Teodorovic, D., Dell’Orco, M., Bee colony optimization – a cooperative learning approach to complex transportation problems., Memorias del 10th EWGT Meeting and 16th Mini-EURO Conference, 2005.
Tillett, J., Rao, T. M., Sahin, F., Rao, R., Darwinian particle swarm optimization., Memorias del 2nd Indian International Conference on Artificial Intelligence, 2005, pp. 1474–1487.
Tsutsui, S., Ant colony optimisation for continuous domains with aggregation pheromones metaphor., Memorias del 5th International Conference on Recent Advances in Soft Computing, 2004, pp. 207–212.
v.E. Conradie, A., Miikkulainen, R., Aldrich, C., Adaptive control utilising neural swarming., Memorias del Genetic and Evolutionary Computation Conference 2002 (GECCO2002), 2002.
Voss, M. S., Feng, X., ARMA model selection using particle swarm optimization and AIC criteria., Memorias del IFAC 15th Triennial World Congress, 2002. DOI: https://doi.org/10.3182/20020721-6-ES-1901.00469
Whitaker, R., Hurley, S., An agent based approach to site selection for wireless networks., Memorias del ACM Symposium on Applied Computing, 2002, pp. 574–577.
Yan-jun, L., Tie-jun, W., An adaptative ant colony system algorithm for continuous-space optimization problems., Journal of Zhejiang University SCIENCE, Vol. 4, No. 1, 2003, pp. 40–46. DOI: https://doi.org/10.1631/jzus.2003.0040
Yisu, J., Knowles, J., Hongmei, L., Yizeng, L., Kell, D. B., The landscape adaptive particle swarm optimizar., Applied Soft Computing, Vol. 8, No. 1, 2007, pp. 295–304. DOI: https://doi.org/10.1016/j.asoc.2007.01.009
Yoshida, H., Kawata, K., Fukuyama, Y., Takayama, S., Nakanishi, Y., A particle swarm optimization for reactive power and voltage control considering voltage security assessment., IEEE Transactions on Power Systems, Vol. 15, No. 4, 2001, pp. 1232–1239. DOI: https://doi.org/10.1109/59.898095
Yuan, R., Guang-yi, C., Xin-jian, Z., Particle swarm optimization based predictive control of proton exchange membrane fuel cell (pemfc)., Journal of Zhejiang University SCIENCE A, Vol. 7, No. 3, 2006, pp. 458–462. DOI: https://doi.org/10.1631/jzus.2006.A0458
Zhang, W., Xie, X., DEPSO: Hybrid particle swarm with differential evolution operador., Memorias del IEEE International Conference on Systems, Man and Cybernetics (SMCC), 2003, pp. 3816–3821.
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