Multi-objective Particle Swarm Optimization Algorithm Based on Gaussian-Cauchy Mixture Mutation
Aiming at the defects of poor convergence performance,insufficient global search ability and easy to fall into local optimization in MOPSO optimization algorithm for solving complex multi-objective optimization problems,a multi-objective parti-cle swarm optimization algorithm based on Gaussian-Cauchy mixed mutation(GC-MOPSO)is proposed.The algorithm uses a muta-tion disturbance mechanism of mixed Gaussian mutation and Cauchy mutation to improve the local and global search ability of parti-cles,and uses the tournament selection mechanism to select the global optimal individual in the external file to increase the diversi-ty of the population.The advantages of the algorithm are verified by comparing with six other algorithms in anti-generation distance(IGD).
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