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Simplified group search optimizer algorithm for large scale global optimization

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Abstract

A simplified group search optimizer algorithm denoted as “SGSO” for large scale global optimization is presented in this paper to obtain a simple algorithm with superior performance on high-dimensional problems. The SGSO adopts an improved sharing strategy which shares information of not only the best member but also the other good members, and uses a simpler search method instead of searching by the head angle. Furthermore, the SGSO increases the percentage of scroungers to accelerate convergence speed. Compared with genetic algorithm (GA), particle swarm optimizer (PSO) and group search optimizer (GSO), SGSO is tested on seven benchmark functions with dimensions 30, 100, 500 and 1 000. It can be concluded that the SGSO has a remarkably superior performance to GA, PSO and GSO for large scale global optimization.

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Authors and Affiliations

  1. Department of Computer Science, Xiangnan University, Chenzhou, 423000, Hunan, China

    Wen-fen Zhang  (张雯雰)

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  1. Wen-fen Zhang  (张雯雰)
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Correspondence to Wen-fen Zhang  (张雯雰).

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Foundation item: the Science and Technology Planning Project of Hunan Province (No. 2011TP4016-3), and the Construct Program of the Key Discipline (Technology of Computer Application) in Xiangnan University

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Zhang, Wf. Simplified group search optimizer algorithm for large scale global optimization. J. Shanghai Jiaotong Univ. (Sci.) 20, 38–43 (2015). https://doi.org/10.1007/s12204-015-1585-z

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  • DOI: https://doi.org/10.1007/s12204-015-1585-z

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