Multi-objective PSO Based on Grid Strategy

In multi-objective optimization problem (MOP), keeping solution diversity is key case for solution quality. To improve the MOP quality, the diversity maintenance threshold value (λα) is proposed to keep solutions diversity based on adaptive grid strategy. These strategies can adaptive maintain the non-inferior diversity to improve swarm individual fly to the global optimal. Four test problems are selected to test the proposed strategy compared with other classical methods, and three performance metrics are chosen to explore the algorithm effectiveness.

[1]  Gary G. Yen,et al.  PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local Archives , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

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

[4]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[5]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

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

[7]  Gary G. Yen,et al.  Dynamic Multiple Swarms in Multiobjective Particle Swarm Optimization , 2009, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[8]  M. Janga Reddy,et al.  An efficient multi-objective optimization algorithm based on swarm intelligence for engineering design , 2007 .