A Mixture Crossover Dynamic Constrained Multi-objective Evolutionary Algorithm Based on Self-Adaptive Start-Up Strategy
GENG Huan-Tong1,2, SUN Jia-Qing2, JIA Ting-Ting2
1.Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044 2.School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044
Abstract Aiming at the slow convergence speed by using the cold start only, the poor adaptiveness of a single crossover operator and the poor diversity of the normal mutation, a mixture crossover dynamic constrained multi-objective evolutionary algorithm based on self-adaptive start-up strategy is proposed. Firstly, the hybrid cold-and-hot start-up mode is designed to identify the change degree of dynamic environment and the Cauchy mutation is used to enhance the diversity of evolutionary population. Then, to enhance the adaptiveness of crossover operation to the dynamic environment, three classical crossover operators, BLX_α,SBX and DE, are used simultaneously, and the respective competitiveness are adjusted adaptively according to their contributions. Finally, the cooperation of the elitist population and the evolutionary population balance the global searching ability and the local searching ability. The simulation results on 6 standard testing functions show that the proposed algorithm not only can dynamically identify the change degree in different environments and improve dynamic tracking effect by enhancing the diversity of initial population, but also can choose crossover operators automatically to accelerate the convergence.
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2015, Vol. 28 
