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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 3498363, 25 pages
https://doi.org/10.1155/2017/3498363
Research Article

Elite Opposition-Based Water Wave Optimization Algorithm for Global Optimization

1School of Computer and Electronics Information, Guangxi University, Nanning 530004, China
2College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China
3Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence, Nanning 530006, China

Correspondence should be addressed to Yongquan Zhou

Received 13 August 2016; Revised 4 November 2016; Accepted 15 November 2016; Published 15 January 2017

Academic Editor: Manuel Doblaré

Copyright © 2017 Xiuli Wu et al. 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

Water wave optimization (WWO) is a novel metaheuristic method that is based on shallow water wave theory, which has simple structure, easy realization, and good performance even with a small population. To improve the convergence speed and calculation precision even further, this paper on elite opposition-based strategy water wave optimization (EOBWWO) is proposed, and it has been applied for function optimization and structure engineering design problems. There are three major optimization strategies in the improvement: elite opposition-based (EOB) learning strategy enhances the diversity of population, local neighborhood search strategy is introduced to enhance local search in breaking operation, and improved propagation operator provides the improved algorithm with a better balance between exploration and exploitation. EOBWWO algorithm is verified by using 20 benchmark functions and two structure engineering design problems and the performance of EOBWWO is compared against those of the state-of-the-art algorithms. Experimental results show that the proposed algorithm has faster convergence speed, higher calculation precision, with the exact solution being even obtained on some benchmark functions, and a higher degree of stability than other comparative algorithms.