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<p>The sine cosine algorithm (SCA) is a well-known stochastic optimizer that uses the oscillation characteristic and periodicity of the sine–cosine mathematical model to construct operators to accomplish the search objective iteratively with the merit of a simple structure and easy to understand. However, SCA possesses several limitations, such as low convergence velocity and accuracy, as well as trapping into local stagnation. To overcome these difficulties, this paper combines three mechanisms, namely greedy selection (GS), dimension learning-based hunting (DLH), and terminal replacement mechanism (TRM), to propose a boosted SCA, named GDTSCA. In this research, the GS strategy plays a role in reducing the implementation of invalid position updates by individuals and facilitating individuals to search in a better direction, which enhances the convergence rate and precision of GDTSCA. The DLH strategy uses different methods to build a neighborhood for each searching individual to accomplish information sharing among the individuals. The strategy can mitigate the problem of diversity reduction of searching individuals and boost the global search. The TRM strategy takes the neighbors of the best individual to update the worst individual to keep the individual from falling into local stagnation. To scientifically analyze and verify the performance of GDTSCA, this paper conducts experiments based on the CEC2017 test suite. First, the proposed algorithm is analyzed for the impact of different strategies. Then, this paper studies the search history and balance diversity of GDTSCA utilizing qualitative analysis. Next, to evaluate the ability of GDTSCA, it is compared with nine original meta-heuristics, seven SCA variants, and six other advanced algorithms. The results show that GDTSCA exhibits a stronger optimization effect than the optimizers utilized in most benchmark functions. Finally, the performance of GDTSCA for optimization in real scenarios is explored based on three well-known engineering design tasks. The outcomes show that the optimization ability of GDTSCA is superior to that of many well-known algorithms utilized in the experiment.</p>

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A boosted sine cosine algorithm with dimension learning-based search and terminal replacement mechanism for optimization and engineering applications

  • 期刊论文

  • 发表日期：

    2025年5月28日

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  • Wei Huang,
  • Huiling Chen,
  • Ali Asghar Heidari,
  • Hupeng Xu,
  • Qian Zhang,
  • Yaoyao Lin

摘要

The sine cosine algorithm (SCA) is a well-known stochastic optimizer that uses the oscillation characteristic and periodicity of the sine–cosine mathematical model to construct operators to accomplish the search objective iteratively with the merit of a simple structure and easy to understand. However, SCA possesses several limitations, such as low convergence velocity and accuracy, as well as trapping into local stagnation. To overcome these difficulties, this paper combines three mechanisms, namely greedy selection (GS), dimension learning-based hunting (DLH), and terminal replacement mechanism (TRM), to propose a boosted SCA, named GDTSCA. In this research, the GS strategy plays a role in reducing the implementation of invalid position updates by individuals and facilitating individuals to search in a better direction, which enhances the convergence rate and precision of GDTSCA. The DLH strategy uses different methods to build a neighborhood for each searching individual to accomplish information sharing among the individuals. The strategy can mitigate the problem of diversity reduction of searching individuals and boost the global search. The TRM strategy takes the neighbors of the best individual to update the worst individual to keep the individual from falling into local stagnation. To scientifically analyze and verify the performance of GDTSCA, this paper conducts experiments based on the CEC2017 test suite. First, the proposed algorithm is analyzed for the impact of different strategies. Then, this paper studies the search history and balance diversity of GDTSCA utilizing qualitative analysis. Next, to evaluate the ability of GDTSCA, it is compared with nine original meta-heuristics, seven SCA variants, and six other advanced algorithms. The results show that GDTSCA exhibits a stronger optimization effect than the optimizers utilized in most benchmark functions. Finally, the performance of GDTSCA for optimization in real scenarios is explored based on three well-known engineering design tasks. The outcomes show that the optimization ability of GDTSCA is superior to that of many well-known algorithms utilized in the experiment.