Computer Science ›› 2017, Vol. 44 ›› Issue (2): 250-256.doi: 10.11896/j.issn.1002-137X.2017.02.041

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Group Search Optimizer Based on Differential Strategies

XIONG Cong-cong, HAO Lu-meng, WANG Dan and DENG Xue-chen   

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

Abstract: The conventional group search optimizer (GSO) is not free from some drawbacks such as easily falling into local optimum,a relative long computing time and lower convergence accuracy.In this study,we proposed a differential ranking-based group search optimizer (DRGSO) algorithm to alleviate these limitations.There are mainly two improvements in the design of DRGSO.First,the population is initialized according to the ranking of fitness values.With this regard,the population obtains heuristic information and alleviates premature convergence to some extent.Second,four evolutionary operators based on differential strategies are constructed to improve the convergence of the algorithm and enhance the population diversity.To demonstrate the performance,eleven benchmark functions were included to eva-luate the performance of DRGSO.Experimental results indicate that the proposed DRGSO exhibits better performance in comparison with the GA,PSO and GSO in terms of accuracy and speed of convergence.

Key words: Group search optimizer (GSO),Differential mutation,Convergence speed,Convergence accuracy

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