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.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  R. Garduno-Ramirez,et al.  Multiobjective control of power plants using particle swarm optimization techniques , 2006, IEEE Transactions on Energy Conversion.

[3]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[4]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[5]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[6]  M. N. Vrahatis,et al.  Particle swarm optimization method in multiobjective problems , 2002, SAC '02.

[7]  Günter Rudolph,et al.  A framework of quantum-inspired multi-objective evolutionary algorithms and its convergence condition , 2007, GECCO '07.

[8]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[9]  Stefan Janson,et al.  Molecular docking with multi-objective Particle Swarm Optimization , 2008, Appl. Soft Comput..

[10]  Günter Rudolph,et al.  Convergence properties of some multi-objective evolutionary algorithms , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[11]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[12]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[13]  C.A. Coello Coello,et al.  MOPSO: a proposal for multiple objective particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[14]  Mohammad Ali Abido,et al.  Two-level of nondominated solutions approach to multiobjective particle swarm optimization , 2007, GECCO '07.