Fast Multiobjective Hybrid Evolutionary Algorithm Based on Mixed Sampling Strategy
In this paper, a fast multiobjective hybrid evolutionary algorithm (MOHEA) is proposed to solve the multiobjective optimization problem (MOOP) in achieving a balance between convergence and distribution with computational complexity. The proposed algorithm, MOHEA, improves the vector evaluated genetic algorithm (VEGA) by combing a new sampling strategy according to the Pareto dominating and dominated relationship-based fitness function. VEGA is good at searching the edge region of the Pareto front, but it has neglected the central area of the Pareto front, and the new sampling strategy prefers the center region of the Pareto front. The mixed sampling strategy improves the convergence performance and the distribution performance while reducing the computational time. Simulation experiments on multiobjective test problems show that, compared with NSGA-II and SPEA2, the fast multiobjective hybrid evolutionary algorithm is better in the two aspects of convergence and distribution, and has obvious advantages in the efficiency.
KeywordsEvolutionary algorithm Mixed sampling Multi-objective optimization Styling Benchmark
This research work is supported by the National Natural Science Foundation of China (U1304609), Foundation for Science & Technology Research Project of Henan Province (162102210044, 152102210068, 152102110076), Program for Innovative Research Team (in Science and Technology) in University of Henan Province (17IRTSTHN011), Program for Key Project of Science and Technology in University of Education Department of Henan Province (17A520030), Fundamental Research Funds for the Henan Provincial Colleges and Universities (2014YWQQ12, 2015XTCX03, 2015XTCX04), Research Funds for Key Laboratory of Grain Information Processing and Control (Henan University of Technology) (KFJJ-2015-106), Ministry of Education and the Grant-in-Aid for Scientific Research (C) of Japan Society of Promotion of Science (JSPS): No. 15K00357.
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