Evolutionary Game Based Optimization Algorithm for Multi-objective Optimization Problems
Used the idea and the selection mechanism called replicator dynamics of evolutionary game theory for reference, and proposed an evolutionary game based optimization algorithm (EGOA) to solve multiobjective optimization problems (MOPs). The frame of algorithm has universal character for MOPs. To evaluate the newly designed approach, solved a group of test functions. The experiment results showed that the evolutionary stable strategy (ESS) set searched by EGOA is very close to the Pareto front of MOPs. Compared with some classic evolutionary algorithm, has a good ability of problem solving.