Efficient Constrained Evolutionary Multi-Agent System for Multi-objective Optimization

Evolutionary multi-agent system approach for optimization (especially for multi-objective optimization) is a very promising computational model. Its computational as well as implemental simplicity causes that approaches based on EMAS model can be widely used for solving optimization tasks. It turns out that introducing some additional mechanisms into basic EMAS - causes that EMAS-based system can be successfully applied for solving constrained multi-objective optimization tasks - and what is important results obtained by proposed approach are better/not worse than results obtained by NSGA-II or SPEA2 algorithms. In the course of this paper some extensions that can be introduced into EMAS in order to constrained multi-objective optimization are presented. What is important - any new additional mechanisms do not have to be introduced into EMAS to solve constrained optimization tasks - the only extensions causing that EMAS-based model becomes an efficient and simple both in conception as well as in implementation - is an appropriate strategy regarding the flow among agents crucial non-renewable resource which is usually called life energy. In this paper, both the idea as well as preliminary results of constrained evolutionary multi-agent system (conEMAS) for multi-objective optimization are presented.