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Mathematical Problems in Engineering
Volume 2017, Article ID 3017608, 13 pages
https://doi.org/10.1155/2017/3017608
Research Article

Improved Backtracking Search Algorithm Based on Population Control Factor and Optimal Learning Strategy

1School of Electronic Information Engineering, Hebei University of Technology, Tianjin 300401, China
2School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
3School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China

Correspondence should be addressed to Lei Chen; nc.ude.ucjt@ielnehc

Received 3 January 2017; Revised 10 May 2017; Accepted 12 June 2017; Published 24 July 2017

Academic Editor: Erik Cuevas

Copyright © 2017 Lei Zhao 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

Backtracking search algorithm (BSA) is a relatively new evolutionary algorithm, which has a good optimization performance just like other population-based algorithms. However, there is also an insufficiency in BSA regarding its convergence speed and convergence precision. For solving the problem shown in BSA, this article proposes an improved BSA named COBSA. Enlightened by particle swarm optimization (PSO) algorithm, population control factor is added to the variation equation aiming to improve the convergence speed of BSA, so as to make algorithm have a better ability of escaping the local optimum. In addition, enlightened by differential evolution (DE) algorithm, this article proposes a novel evolutionary equation based on the fact that the disadvantaged group will search just around the best individual chosen from previous iteration to enhance the ability of local search. Simulation experiments based on a set of 18 benchmark functions show that, in general, COBSA displays obvious superiority in convergence speed and convergence precision when compared with BSA and the comparison algorithms.