Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 83-88.

• Intelligent Computing • Previous Articles     Next Articles

Multi-objective Grey Wolf Optimization Hybrid Adaptive Differential Evolution Mechanism

ZHAO Yun-tao, CHEN Jing-cheng, LI Wei-gang   

  1. ( Engineering Research Center for Metallurgical Automation and Detecting Technology of Ministry of Education,Wuhan University of Science and Technology,Wuhan 430081,China)
  • Online:2019-11-10 Published:2019-11-20

Abstract: Due to the grey wolf algorithm is easy to fall into local optimum,a multi-objective grey wolf optimization based on adaptive differential evolution mechanism was proposed.Firstly,the external archive is grouped according to the distance of the objective function value to avoid storing similar individuals.Secondly,the selection mechanism of the head wolf is adopted.Finally,differential evolution is introduced into the updating process to select the next generation of grey wolves.At the same time,the parameters of differential evolution are adaptively adjusted according to the objective value of candidate solutions,to balance the local exploitation and the global exploration performance.The experimental results show that the proposed multi-objective grey wolf optimization has better convergence and distribution than the other three algorithms.

Key words: Multi-objective optimization, Grey wolf algorithm, Parametric adaptation, Differential evolution

CLC Number: 

  • TP301.6
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