Differential Evolution Algorithm for Solving Multi-objective Optimization Problems
نویسندگان
چکیده
This paper presents a modified Differential Evolution (DE) algorithm called OCMODE for solving multi-objective optimization problems. First, the initialization phase is improved by using the opposition based learning. Further, a time varying scale factor F employing chaotic sequence is used which helps to get a well distributed Pareto front by the help of non dominated and crowding distance sorting. The performance of the OCMODE algorithm is measured on the set of five ZDT bi-objective benchmark functions and the results are compared with some multi-objective evolutionary algorithms in the literature. The numerical results show the efficiency of the proposed algorithm. Key-Words: Optimization, differential evolution, chaotic sequence, multi-objective optimization
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