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Computational Intelligence and Neuroscience
Volume 2018 (2018), Article ID 9167414, 27 pages
Research Article

Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

1School of Information and Mathematics, Yangtze University, Jingzhou, Hubei 434023, China
2School of Software, East China Jiaotong University, Nanchang, Jiangxi 330013, China

Correspondence should be addressed to Zhongbo Hu

Received 10 October 2017; Accepted 20 December 2017; Published 13 February 2018

Academic Editor: Silvia Conforto

Copyright © 2018 Hailong Wang 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.


The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor () is modified based on the Metropolis criterion in simulated annealing. The redesigned could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive -constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed.