Abstract
Backtracking Search Algorithm (BSA) is a younger population-based evolutionary algorithm and widely researched. Due to the introduction of historical population and no guidance toward to the best individual, BSA does not adequately use the information in the current population, which leads to a slow convergence speed and poor exploitation ability of BSA. To address these drawbacks, a novel backtracking search algorithm with reflection mutation based on sine cosine is proposed, named RSCBSA. The best individual found so far is employed to improve convergence speed, while sine and cosine math models are introduced to enhance population diversity. To sufficiently use the information in the historical population and current population, four individuals are selected from the historical or current population randomly to construct an unit simplex, and the center of the unit simplex can enhance exploitation ability of RSCBSA. Comprehensive experimental results and analyses show that RSCBSA is competitive enough with other state-of-the-art meta-heuristic algorithms.
Highlights
There are many global optimization problems in the real world
Based on the above discussion, in this paper, an enhancing backtracking search optimization algorithm with reflection mutation strategy based on sine cosine, named RSCBSA, is proposed
In RSCBSA, inspired by reflection operation in Nelder–Mead method [41] and Sine Cosine Algorithm (SCA) [42], a new reflection mutation strategy based on sine cosine is developed to address the above-mentioned drawbacks, in which the best solution and sine and cosine math models are introduced to balance exploration and exploitation ability of Backtracking Search Algorithm (BSA)
Summary
There are many global optimization problems in the real world. These problems are characterized by complexity, multimodality, strong-nonlinearity, dynamic change, and non-differentiality. For the drawbacks of slow convergence speed and falling into local optimum, Wang et al [24] proposed an improved BSA fusing optimal solution-guided mutation strategy and niche technology. Based on the above discussion, in this paper, an enhancing backtracking search optimization algorithm with reflection mutation strategy based on sine cosine, named RSCBSA, is proposed. In RSCBSA, inspired by reflection operation in Nelder–Mead method [41] and Sine Cosine Algorithm (SCA) [42], a new reflection mutation strategy based on sine cosine is developed to address the above-mentioned drawbacks, in which the best solution and sine and cosine math models are introduced to balance exploration and exploitation ability of BSA. A novel backtracking search algorithm with reflection mutation strategy based on sine cosine (RSCBSA) is proposed to solve global optimization problems.
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