一个基于受限学习的竞争性群体优化器,用于大规模的多目标优化
IEEE transactions on cybernetics
|October 10, 2025
概括
本研究引入了一个基于约束学习的竞争性群群优化器 (CL-CSO) 来改善大规模的多目标优化. CL-CSO增强了粒子学习和融合速度,以更好地执行复杂的优化任务.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 群集情报 群集情报 群集情报
背景情况:
- 竞争性群体优化器 (CSO) 是大型多目标优化问题 (LMOP) 的关键方法.
- 现有使用双向随机竞争 (PRC) 的民间社会组织面临局限性:赢家质量不佳阻碍了学习,而PRC的随机性减缓了趋同.
研究的目的:
- 解决传统社会组织在解决LMOP方面的局限性.
- 提出一种新的基于受约束学习的CSO (CL-CSO),以提高LMOP的表现.
主要方法:
- CL-CSO使用参考向量将目标空间划分为子区域.
- 采用受约束的学习策略,使失败分子能够从内部或邻近子区域内的获胜者那里学习.
- 一个高斯模型辅助的策略增强了赢家粒子的多样性和质量.
主要成果:
- CL-CSO显著改善了粒子学习效应和整体的融合速度.
- 实验结果表明,与基准LMOP上的现有算法相比,CL-CSO的性能优越.
- 该算法有效处理具有2-3个目标和500-5000个决策变量的问题,包括现实世界的实例选择问题.
结论:
- 拟议的CL-CSO有效地克服了民间社会组织中中国传统机制的局限性.
- CL-CSO提供了一种更有效,更强大的方法来解决大规模的多目标优化问题.
- 该方法在复杂的优化场景中显示出实际应用的前景.
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