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多目标粒子群优化算法用于任务分配和存档指导突变策略

Jianjie Chen¹, Yanmin Liu², Yi Luo³

¹School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, China.

Scientific reports

|May 6, 2025

概括

这项研究介绍了一种新的多目标粒子群优化算法 (TAMOPSO),可以提高搜索效率和公平性. 塔莫普索 (TAMOPSO) 改进了进化搜索和选择过程,以更好地解决多目标问题.

科学领域:

人工智能的人工智能
计算智能是一种计算智能.
优化算法优化算法

背景情况:

传统的粒子群优化 (PSO) 算法面临的挑战是搜索效率低下和偏见的选择机制.
多目标优化问题需要复杂的算法来有效地平衡竞争目标.

研究的目的:

提出一种新的多目标粒子群优化算法 (TAMOPSO),解决传统方法的局限性.
提高进化搜索效率,确保优化过程中的公平性.

主要方法:

塔莫普索采用任务分配策略,划分子群体并根据粒子分布分配任务.
一个自适应的莱维飞行策略可以根据人口的融合或分散动态调整全球和本地搜索.
粒子进化贡献率指数可以过有价值的历史解决方案,而改进的选择机制可以确保公平.

主要成果:

在22个标准测试问题中,TAMOPSO与现有的10个算法相比,表现出更好的性能.
该算法有效地提高了进化搜索效率,并加快了融合速度.
实验结果表明,在粒子选择过程中提高了公平性.

结论:

在解决多目标优化问题方面,TAMOPSO提供了显著的进步.
拟议的任务分配和档案引导的突变策略有助于提高绩效和公平性.
适应式Lévy飞行策略增强了算法平衡全球探索和本地利用的能力.

关键词:

个人的最佳选择选择.莱维飞行战略飞行策略多目标粒子群集优化优化部分人口的分割.任务分配任务分配

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