Co-evolutionary Multi-agent System with Predator-Prey Mechanism for Multi-objective Optimization

Co-evolutionary techniques for evolutionary algorithms allow for the application of such algorithms to problems for which it is difficult or even impossible to formulate explicit fitness function. These techniques also maintain population diversity, allows for speciation and help overcoming limited adaptive capabilities of evolutionary algorithms. In this paper the idea of co-evolutionary multi-agent system with predator-prey mechanism for multi-objective optimizationis introduced. In presented system the Pareto frontier is located by the population of agents as a result of co-evolutionary interactions between two species: predators and prey. Results from runs of presented system against test problem and comparison to classical multi-objective evolutionary algorithms conclude the paper.

[1]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[2]  Marek Kisiel-Dorohinicki,et al.  Semi-elitist Evolutionary Multi-agent System for Multiobjective Optimization , 2006, International Conference on Computational Science.

[3]  Leszek Siwik,et al.  Multi-objective Optimization Using Co-evolutionary Multi-agent System with Host-Parasite Mechanism , 2006, International Conference on Computational Science.

[4]  Zbigniew Michalewicz,et al.  Handbook of Evolutionary Computation , 1997 .

[5]  Eckart Zitzler,et al.  Evolutionary algorithms for multiobjective optimization: methods and applications , 1999 .

[6]  Rafal Drezewski,et al.  Co-Evolutionary Multi-Agent System with Speciation and Resource Sharing Mechanisms , 2012, Comput. Artif. Intell..

[7]  Thomas Bäck,et al.  Parallel Problem Solving from Nature — PPSN V , 1998, Lecture Notes in Computer Science.

[8]  Marco Laumanns,et al.  A Spatial Predator-Prey Approach to Multi-objective Optimization: A Preliminary Study , 1998, PPSN.

[9]  Stephen Gilmore,et al.  Combining Measurement and Stochastic Modelling to Enhance Scheduling Decisions for a Parallel Mean Value Analysis Algorithm , 2006, International Conference on Computational Science.

[10]  Leszek Siwik,et al.  Co-Evolutionary Multi-Agent System with Sexual Selection Mechanism for Multi-Objective Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.