Evolutionary Computation Meets Multiagent Systems for Better Solving Optimization Problems

  • Vinicius Renan de CarvalhoEmail author
  • Jaime Simão Sichman
Part of the Communications in Computer and Information Science book series (CCIS, volume 999)


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.


Hyper-heuristics Multi-objective Evolutionary Algorithms Voting methods Agent cooperation 



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.


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Laboratório de Técnicas Inteligentes (LTI), Escola Politécnica (EP)University of Sao Paulo (USP)São PauloBrazil

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