Journal
INFORMATION SCIENCES
Volume 473, Issue -, Pages 202-226Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2018.09.039
Keywords
Backtracking search optimization algorithm(BSA); Adaptive BSA with multiple sub-population(AMBSA); Evolutionary computation (EC); Optimization problem
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Funding
- National Natural Science Foundations of China [61572224, 61304082, 41475017]
- National Science Fund for Distinguished Young Scholars [61425009]
- Anhui Provincial Natural Science Foundation [1708085MF140]
- Major Project of Natural Science Research in Anhui Province [KJ2015ZD36]
- Natural Science Foundation in colleges and universities of Anhui Province [KJ2016A639]
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As a new evolutionary computation method, the structure of backtracking search optimization algorithm (BSA) is simple and the exploration capability of it is strong. However, the global performance of the BSA is significantly affected by mutation strategies and control parameters. Designing appropriate mutation strategies and control parameters is important to improve the global performance of the BSA. In this paper, an adaptive BSA with knowledge learning (KLBSA) is developed to improve the global performance of the BSA. In the method, an adaptive control parameter based on the global and local information of the swarms in the current iteration is designed to adjust the search step length of individuals, which helps to balance the exploration and exploitation abilities of the algorithm. Moreover, a new mutation strategy based on the guidance of different information is designed to improve the optimization ability of the algorithm. In addition, a multi-population strategy is implemented to thoroughly improve the searching ability of the algorithm for different searching areas. To this end, experiments on three groups of benchmark functions and three real-world problems are implemented to verify the performance of the proposed KLBSA algorithm. The results indicate that the proposed algorithm performs competitively and effectively when compared to some other evolutionary algorithms. (C) 2018 Elsevier Inc. All rights reserved.
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