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Computational Intelligence and Neuroscience
Volume 2019, Article ID 1589303, 24 pages
https://doi.org/10.1155/2019/1589303
Research Article

An Enhanced Lightning Attachment Procedure Optimization with Quasi-Opposition-Based Learning and Dimensional Search Strategies

School of Civil Engineering, Guangzhou University, Guangzhou, China

Correspondence should be addressed to Weili Luo; nc.ude.uhzg@oullw

Received 15 February 2019; Revised 15 June 2019; Accepted 17 July 2019; Published 1 August 2019

Academic Editor: Bruce J. MacLennan

Copyright © 2019 Tongyi Zheng and Weili Luo. 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.

Abstract

Lightning attachment procedure optimization (LAPO) is a new global optimization algorithm inspired by the attachment procedure of lightning in nature. However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced version of LAPO called ELAPO has been proposed in this paper. A quasi-opposition-based learning strategy is incorporated to improve both exploration and exploitation abilities by considering an estimate and its opposite simultaneously. Moreover, a dimensional search enhancement strategy is proposed to intensify the exploitation ability of the algorithm. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are utilized to test the effectiveness of the proposed algorithm. Numerical results indicate that ELAPO can provide better or competitive performance compared with the basic LAPO and other five state-of-the-art optimization algorithms.