使用综合指标和分层粒子群集优化器处理多目标优化问题
Xianzi Zhang1, Yanmin Liu2, Jie Yang2
1School of Data Science and Information Engineering, Guizhou Minzu University, Guiyang 550025, China.
一个新的算法,IMOPSOCE,通过提高融合和多样性来增强多目标粒子群的优化. 它解决了一些常见的问题,比如在复杂问题上过早地趋同.
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:
- 多目标粒子群集优化 (MOPSO) 面临着诸如过早的融合和不充分的多样性等挑战,特别是在高维问题中.
- 这些局限性可能会导致算法陷入局部最佳状态,从而阻碍有效的解决方案.
研究的目的:
- 引入一种新的算法,即IMOPSOCE,旨在克服传统MOPSO固有的缺点.
- 在复杂的优化场景中提高融合速度和解决方案的多样性.
主要方法:
- 制定了外部档案维护策略,包括转折点距离和分布系数.
- 利用综合指标 (CM) 改进非主导解决方案,改善群体的融合和多样性.
- 实施了随机惯性权重策略,以平衡勘探和开发.
- 引入了粒子适应式飞行模式,以提高整体优化能力.
主要成果:
- 与现有的MOPSO算法相比,IMOPSOCE表现出更好的融合和多样性.
- 该算法在复杂的高维测试函数中有效地避免了局部最佳值.
- 对22个测试函数的比较分析显示,IMOPSOCE的性能优于其他10个算法.
结论:
- 拟议的IMOPSOCE算法在解决MOPSO限制方面取得了重大进展.
- 它的创新策略提供了增强的性能,特别是在复杂和高维的优化任务.
- 在大多数测试的功能中,IMOPSOCE表现出强大的竞争力和优异性.
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