Multi-Objective Particle Swarm Optimization Algorithm Based on Game Strategies


Particle Swarm Optimization (PSO) is easier to realize and has a better performance than evolutionary algorithm in many fields. This paper proposes a novel multi-objective particle swarm optimization algorithm inspired from Game Strategies (GMOPSO), where those optimized objectives are looked as some independent agents which tend to optimize own objective function. Therefore, a multi- player game model is adopted into the multi-objective particle swarm algorithm, where appropriate game strategies could bring better multi-objective optimization performance. In the algorithm, novel bargain strategy among multiple agents and nondominated solutions archive method are designed for improving optimization performance. Moreover, the algorithm is validated by several simulation experiments and its performance is tested by different benchmark functions.