多尺度正余弦优化算法
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作者单位:

安徽工业大学

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中图分类号:

TP301.6

基金项目:

安徽高校自然科学研究项目(KJ2019A0063);安徽省自然科学基金项目(1808085MF196);安徽高校协同创新项目 (GXXT-2019-008)


A Multi-Scale Sine Cosine Algorithm for Optimization Problems
Author:
Affiliation:

Anhui University of Technology

Fund Project:

he University Natural Science Fund project of Anhui Province(KJ2019A0063);Project of Natural Science Foundation of Anhui Province(1808085MF196); The University Synergy Innovation Program of Anhui Province(GXXT-2019-008)

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    摘要:

    针对标准正余弦算法进化后期停滞问题,本文对进化过程中的种群多样性进行了分析,得出正余弦算 法的种群多样性受控制因子直接影响,且种群多样性表达式中控制因子随迭代次数的增加而指数下降。为了改 善正余弦算法进化后期的探索与开采,提出了多尺度正余弦优化算法. 新算法中通过自适应的多尺度控制因子 来调节群体多样性从而实现多层次的搜索,同时设计了协助种群实施局部搜索,用来加快收敛速度和提高解的 质量。将提出的新算法与改进的正余弦算法和多种新型群智能算法进行了对比, 23 个标准函数进行测试的统 计结果表明新算法较好地平衡进化过程中的探索与开采,提高了全局优化能力。

    Abstract:

    In order to address the stagnation problem in the late stage of evolution of standard sine cosine algorithm(SCA), this paper makes the analysis of population diversity. The analysis results show that the control factor affects directly population diversity and is decreased exponentially with increase of iterations in the expression of population diversity. In order to improve the ability of exploration and exploitation in the late stage of evolution of SCA, a multi-scale sine cosine algorithm (MSCA) is presented. In the MSCA, a adaptive multi-scale control factor is designed to regulate population diversity for achieving the search with different layers. Meanwhile, a assisted swarm is developed to coordinate the local search for accelerating the convergence speed and improving calculation accuracy . MSCA was evaluated on 23 benchmark functions and compared with the improved versions of SCA and new swarm intelligence algorithms. The numerical results show that MSCA can better coordinate the exploitation and exploration capabilities and improve the global optimization ability.

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历史
  • 收稿日期:2021-03-29
  • 最后修改日期:2021-07-29
  • 录用日期:2021-07-30
  • 在线发布日期: 2021-09-01
  • 出版日期: