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Discrete Dynamics in Nature and Society
Volume 2017 (2017), Article ID 8342694, 23 pages
Research Article

A Hybrid Lightning Search Algorithm-Simplex Method for Global Optimization

1School of Computer, Electronics and 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 Intelligent Computing, Nanning 530006, China

Correspondence should be addressed to Yongquan Zhou

Received 11 March 2017; Accepted 1 June 2017; Published 13 July 2017

Academic Editor: Pasquale Candito

Copyright © 2017 Yuting Lu 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.


In this paper, a novel hybrid lightning search algorithm-simplex method (LSA-SM) is proposed to solve the shortcomings of lightning search algorithm (LSA) premature convergence and low computational accuracy and it is applied to function optimization and constrained engineering design optimization problems. The improvement adds two major optimization strategies. Simplex method (SM) iteratively optimizes the current worst step leaders to avoid the population searching at the edge, thus improving the convergence accuracy and rate of the algorithm. Elite opposition-based learning (EOBL) increases the diversity of population to avoid the algorithm falling into local optimum. LSA-SM is tested by 18 benchmark functions and five constrained engineering design problems. The results show that LSA-SM has higher computational accuracy, faster convergence rate, and stronger stability than other algorithms and can effectively solve the problem of constrained nonlinear optimization in reality.