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Algorithms 2017, 10(1), 9; doi:10.3390/a10010009

Elite Opposition-Based Social Spider Optimization Algorithm for Global Function Optimization

1
School of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China
2
Key Laboratories of Guangxi High Schools Complex System and Computational Intelligence, Nanning 530006, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yun-Chia Liang, Mehmet Fatih Tasgetiren and Quan-Ke Pan
Received: 27 November 2016 / Revised: 27 December 2016 / Accepted: 4 January 2017 / Published: 8 January 2017
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)

Abstract

The Social Spider Optimization algorithm (SSO) is a novel metaheuristic optimization algorithm. To enhance the convergence speed and computational accuracy of the algorithm, in this paper, an elite opposition-based Social Spider Optimization algorithm (EOSSO) is proposed; we use an elite opposition-based learning strategy to enhance the convergence speed and computational accuracy of the SSO algorithm. The 23 benchmark functions are tested, and the results show that the proposed elite opposition-based Social Spider Optimization algorithm is able to obtain an accurate solution, and it also has a fast convergence speed and a high degree of stability. View Full-Text
Keywords: social spider optimization; elite opposition-based learning; elite opposition-based social spider optimization; function optimization social spider optimization; elite opposition-based learning; elite opposition-based social spider optimization; function optimization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhao, R.; Luo, Q.; Zhou, Y. Elite Opposition-Based Social Spider Optimization Algorithm for Global Function Optimization. Algorithms 2017, 10, 9.

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