Abstract
In this work, we discuss the synergy between Evolutionary Computation (EC) and Multi-Agent Systems (MAS) when both are used together to enhance the process of solving optimization problems. Evolutionary algorithms are inspired by nature and follow Darwin theory of the fittest. They are usually applied where there is no specific algorithm which can solve optimization problems in a reasonable time. Multi-Agent Systems, in their turn, are collections of autonomous entities, named agents, that sense their environment and execute some actions in the environment to meet their individual or common goals. When these two techniques are applied together, one can create powerful approaches to better solve optimization problems. This paper presents an overview of this combined approach, considering both mono-objective and multi-objective approaches. In particular, we stress the importance of hyper-heuristic approaches, i.e., heuristics that help to choose the best EC algorithm among a candidate set.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Acan, A., Lotfi, N.: A multiagent, dynamic rank-driven multi-deme architecture for real-valued multiobjective optimization. Artif. Intell. Rev. 48(1), 1–29 (2017)
Atallah, M.J.: Algorithms and Theory of Computation Handbook. CRC Press, Boca Raton (1998)
Aydin, M.E., Fogarty, T.C.: Teams of autonomous agents for job-shop scheduling problems: an experimental study. J. Intell. Manuf. 15(4), 455–462 (2004)
Balid, A., Minz, S.: Improving multi-agent evolutionary techniques with local search for job shop scheduling problem. In: 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 2, pp. 516–521, December 2008
Barbucha, D.: A cooperative population learning algorithm for vehicle routing problem with time windows. Neurocomputing 146, 210–229 (2014). Bridging Machine learning and Evolutionary Computation (BMLEC) Computational Collective Intelligence
Belkhelladi, K., Chauvet, P., Schaal, A.: An agent framework with an efficient information exchange model for distributed genetic algorithms. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 848–853, June 2008
Boussaid, I., Lepagnot, J., Siarry, P.: A survey on optimization metaheuristics. Inf. Sci. 237, 82–117 (2013)
Burke, E.K., et al.: Hyper-heuristics: a survey of the state of the art. J. Oper. Res. Soc. 64(12), 1695–1724 (2013)
Cadenas, J.M., Garrido, M.C., Munoz, E.: A cooperative system of metaheuristics. In: 7th International Conference on Hybrid Intelligent Systems (HIS 2007), pp. 120–125, September 2007
de Carvalho, V.R., Sichman, J.S.: Applying copeland voting to design an agent-based hyper-heuristic. In: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, pp. 972–980 (2017)
de Carvalho, V.R., Sichman, J.S.: Solving real-world multi-objective engineering optimization problems with an Election-Based Hyper-Heuristic. In: International Workshop on Optimisation in Multi-agent Systems (OPTMAS 2018) (2018)
de Carvalho, V.R., Sichman, J.S.: Multi-agent election-based hyper-heuristics. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 5779–5780 (2018)
Chalupa, D.: Adaptation of a multiagent evolutionary algorithm to NK landscapes. In: Proceedings of the 15th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO 2013, Companion, pp. 1391–1398. ACM, New York (2013)
Chatzinikolaou, N., Robertson, D.: The use of reputation as noise-resistant selection bias in a co-evolutionary multi-agent system. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, GECCO 2012, pp. 983–990. ACM, New York (2012)
Chira, C., Gog, A., Dumitrescu, D.: Exploring population geometry and multi-agent systems: a new approach to developing evolutionary techniques. In: Proceedings of the 10th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO 2008, pp. 1953–1960. ACM, New York (2008)
Coello, C.: Evolutionary Algorithms for Solving Multi-objective Problems. Springer, New York (2007). https://doi.org/10.1007/978-0-387-36797-2
Denzinger, J., Offermann, T.: On cooperation between evolutionary algorithms and other search paradigms. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol. 3, p. 2324 (1999)
Drezewski, R., Siwik, L.: Agent-based multi-objective evolutionary algorithm with sexual selection. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 3679–3684, June 2008
Eiben, E.A., Schoenauer, M., Laredo, J.L.J., Castillo, P.A., Mora, A.M., Merelo, J.J.: Exploring selection mechanisms for an agent-based distributed evolutionary algorithm. In: Proceedings of the 9th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO 2007, pp. 2801–2808. ACM, New York (2007)
Feigenbaum, E.A., Feldman, J., et al.: Computers and Thought. ACM, New York (1963)
Fu, Y., Wang, H., Tian, G., Li, Z., Hu, H.: Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm. J. Intell. Manuf., 1–16 (2018)
Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)
Huang, J., Liu, J., Yao, X.: A multi-agent evolutionary algorithm for software module clustering problems. Soft Comput. 21(12), 3415–3428 (2017)
Jiang, S., Zhang, J., Ong, Y.S.: A multiagent evolutionary framework based on trust for multiobjective optimization. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2012, vol. 1, pp. 299–306, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2012)
Koza, J.R.: Evolution of subsumption using genetic programming. In: Proceedings of the First European Conference on Artificial Life, pp. 110–119 (1992)
de Lima Corrêa, L., Inostroza-Ponta, M., Dorn, M.: An evolutionary multi-agent algorithm to explore the high degree of selectivity in three-dimensional protein structures. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 1111–1118, June 2017
Maashi, M., Özcan, E., Kendall, G.: A multi-objective hyper-heuristic based on choice function. Expert Syst. Appl. 41(9), 4475–4493 (2014)
Malek, R.: An agent-based hyper-heuristic approach to combinatorial optimization problems. In: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, vol. 3, pp. 428–434, October 2010
Martin, S., Ouelhadj, D., Beullens, P., Ozcan, E., Juan, A.A., Burke, E.K.: A multi-agent based cooperative approach to scheduling and routing. Eur. J. Oper. Res. 254(1), 169–178 (2016)
Meignan, D., Créput, J.C., Koukam, A.: A cooperative and self-adaptive metaheuristic for the facility location problem. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, GECCO 2009, pp. 317–324. ACM, New York (2009)
Meignan, D., Koukam, A., Créput, J.C.: Coalition-based metaheuristic: a self-adaptive metaheuristic using reinforcement learning and mimetism. J. Heuristics 16(6), 859–879 (2010)
Milano, M., Roli, A.: MAGMA: a multiagent architecture for metaheuristics. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(2), 925–941 (2004)
Nouri, H.E., Belkahla Driss, O., Ghédira, K.: Metaheuristics based on clustering in a holonic multiagent model for the flexible job shop problem. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, GECCO Companion 2015, pp. 997–1004. ACM, New York (2015)
Nugraheni, C.E., Abednego, L.: Multi agent hyper-heuristics based framework for production scheduling problem. In: 2016 International Conference on Informatics and Computing (ICIC), pp. 309–313, October 2016
Osman, I.H., Laporte, G.: Metaheuristics: a bibliography. Ann. Oper. Res. 63, 511–623 (1996)
Ouelhadj, D., Petrovic, S.: A cooperative hyper-heuristic search framework. J. Heuristics 16(6), 835–857 (2010)
Pan, X., Chen, H.: A multi-agent social evolutionary algorithm for resource-constrained project scheduling. In: 2010 International Conference on Computational Intelligence and Security, pp. 209–213, December 2010
Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley Longman Publishing Co. Inc., Boston (1984)
Socha, K., Kisiel-Dorohinicki, M.: Agent-based evolutionary multiobjective optimisation. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 1, pp. 109–114, May 2002
Sun, H., Zhou, C.: Context-aware multi-agent model of microgrid optimization using fuzzy preferences evolutionary algorithm. In: 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP), pp. 803–808, June 2013
Talbi, E., Bachelet, V.: COSEARCH: a parallel cooperative metaheuristic. J. Math. Model. Algorithms 5(1), 5–22 (2006)
Talukdar, S., Baerentzen, L., Gove, A., De Souza, P.: Asynchronous teams: cooperation schemes for autonomous agents. J. Heuristics 4(4), 295–321 (1998)
Ullah, A.S.S.M.B., Sarker, R., Lokan, C.: An agent-based memetic algorithm (AMA) for nonlinear optimization with equality constraints. In: 2009 IEEE Congress on Evolutionary Computation, pp. 70–77, May 2009
Wang, S., Wang, L.: A knowledge-based multi-agent evolutionary algorithm for semiconductor final testing scheduling problem. Knowl. Based Syst. 84, 1–9 (2015)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
Wooldridge, M.: An Introduction to Multiagent Systems. Wiley, Chichester (2009)
Yan, Y., Wang, H., Wang, D., Yang, S., Wang, D.: A multi-agent based evolutionary algorithm in non-stationary environments. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 2967–2974, June 2008
Zeng, C., Gu, T., Zhong, Y., Cai, G.: A multi-agent evolutionary algorithm for connector-based assembly sequence planning. Proc. Eng. 15, 3689–3693 (2011). cEIS 2011
Zheng, Y., Xu, X., Chen, S., Wang, W.: Distributed agent based cooperative differential evolution: a master-slave model. In: 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, vol. 01, pp. 376–380, October 2012
Acknowledgements
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Vinicius Renan de Carvalho was also supported by CNPq, Brazil, under grant agreement no. 140974/2016-4.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
de Carvalho, V.R., Sichman, J.S. (2019). Evolutionary Computation Meets Multiagent Systems for Better Solving Optimization Problems. In: Koch, F., Yoshikawa, A., Wang, S., Terano, T. (eds) Evolutionary Computing and Artificial Intelligence. GEAR 2018. Communications in Computer and Information Science, vol 999. Springer, Singapore. https://doi.org/10.1007/978-981-13-6936-0_4
Download citation
DOI: https://doi.org/10.1007/978-981-13-6936-0_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-6935-3
Online ISBN: 978-981-13-6936-0
eBook Packages: Computer ScienceComputer Science (R0)