Computer Science ›› 2017, Vol. 44 ›› Issue (3): 259-263.doi: 10.11896/j.issn.1002-137X.2017.03.053

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Grey Wolf Optimization Algorithm with Self-adaptive Searching Strategy

WEI Zheng-lei, ZHAO Hui, HAN Bang-jie, SUN Chu and LI Mu-dong   

  • Online:2018-11-13 Published:2018-11-13

Abstract: The grey wolf optimization algorithm (GWO) is a new type intelligence optimization algorithm.However,the GWO has the problem of low convergence speed and easily falling into local optimum as the same as other optimization algorithms.Aiming at this problem,an improved GWO with the self-adaptive searching strategy was proposed.Firstly,the search direction is controlled by fitness value and the best learning equation is introduced to improve the convergence speed and the optimization precision.Secondly,in order to maintain the population diversity,the position vector difference is utilized to escape from local optimum.Finally,10 benchmarks and other four popular algorithms were compared to illustrate the superiority of GWO with the self-adaptive searching strategy.The experimental results and the Wilcoxon signed ranks test results show that the improved GWO outperforms the other four algorithms in terms of convergence speed and precision.

Key words: GWO,Self-adaptive,Best learning equation,Wilcoxon signed ranks test,Function optimization

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