融合多策略改进的黏菌优化算法

doi:10.16451/j.cnki.issn1003-6059.202307006

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摘要

针对黏菌优化算法(Slime Mould Algorithm, SMA)的寻优过程存在收敛效率较低、容易陷入局部最优解等问题,文中提出融合多策略改进的黏菌优化算法(Improved SMA Fused with Multi-strategy, MISMA).引入Halton序列,丰富初始种群的多样性,提升算法寻优的遍历性和收敛精度.融入差分变异思想,改进算法的全局位置更新公式,强化全局探索能力,增强算法的持续寻优性能.糅合改进收敛因子和精英选择机制的局部搜索策略,提升算法的局部开采能力,更好地平衡算法的全局探索与局部开发进程.基于动态边界的透镜成像学习策略改善个体的质量,加强算法反早熟及摆脱局部最优解的能力.在13个基准函数及部分CEC2014测试函数上的数值仿真实验表明,MISMA具有较强的鲁棒性.此外,在光伏电池组件模型参数优化实验上进一步验证MISMA在处理实际工程优化问题时的优越性及适用性.

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	李得恺
	张长胜
	杨雪松

关键词 ：黏菌优化算法(SMA), Halton序列, 差分变异, 收敛因子, 透镜成像学习, 动态边界

Abstract：

The slime mould algorithm(SMA) yields low convergence efficiency and is easily dropped into local shackles. To address these shortcomings, an improved slime mould algorithm fused with multiple strategies(MISMA) is proposed.The Halton sequence is introduced to enrich the diversity of the initial population, and consequently the ergodicity and convergence precision of the algorithm are improved.The differential variation idea is employed to modify the global position update equations and enhance the global exploration capability and the continuous optimization performance.The local search strategy combining convergence factor and elite selection mechanism is ameliorated to improve the local development capacity of the algorithm. Thus, the global search performance and local exploitation capability of the algorithm are well balanced. The lens imaging learning strategy based on dynamic boundary is proposed to improve the individual quality and the capability of the algorithm avoiding prematurity and getting rid of local constraints.Numerical simulation experiments of 13 benchmark functions and some CEC2014 test functions show the strong robustness of MISMA.Moreover, the superiority and applicability of MISMA for dealing with practical engineering optimization problems are verified through parameter optimization experiments of the PV module model.

Key words： Slime Mould Algorithm(SMA) Halton Sequence Differential Mutation Convergence Factor Lens Imaging Learning Dynamic Boundary

收稿日期: 2023-05-12

ZTFLH:

TP301.6

基金资助:

国家自然科学基金项目(No.51665025)资助

通讯作者: 张长胜,硕士,副教授,主要研究方向为复杂工业过程建模、智能优化算法.E-mail:122832170@qq.com.

作者简介: 李得恺,硕士研究生,主要研究方向为智能优化算法、图像处理.E-mail:2531026098@qq.com. 杨雪松,硕士研究生,主要研究方向为图像处理、智能优化算法.E-mail:2305307269@qq.com.

引用本文:

李得恺, 张长胜, 杨雪松. 融合多策略改进的黏菌优化算法[J]. 模式识别与人工智能, 2023, 36(7): 647-660. LI Dekai, ZHANG Changsheng, YANG Xuesong. Improved Slime Mould Algorithm Fused with Multi-strategy. Pattern Recognition and Artificial Intelligence, 2023, 36(7): 647-660.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.202307006 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2023/V36/I7/647

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