Multiobjective Parallel Chaos Optimization Algorithm with Crossover and Merging Operation

Abstract

Chaos optimization algorithm (COA) usually utilizes chaotic maps to generate the pseudorandom numbers mapped as the decision variables for global optimization problems. Recently, COA has been applied to many single objective optimization problems and simulations results have demonstrated its effectiveness. In this paper, a novel parallel chaos optimization algorithm (PCOA) will be proposed for multiobjective optimization problems (MOOPs). As an improvement to COA, the PCOA is a kind of population-based optimization algorithm which not only detracts the sensitivity of initial values but also adjusts itself suitable for MOOPs. In the proposed PCOA, crossover and merging operation will be applied to exchange information between parallel solutions and produce new potential solutions, which can enhance the global and fast search ability of the proposed algorithm. To test the performance of the PCOA, it is simulated with several benchmark functions for MOOPs and mixed H2/H∞ controller design. The simulation results show that PCOA is an alternative approach for MOOPs.