经济负载调度问题上的Memetic Salp Swarm算法
Mohammed A Awadallah1,2, Mohammed Azmi Al-Betar3,4, Malik Braik5
1Department of Computer Science, Al-Aqsa University, P.O. Box 4051, Gaza, Palestine.
Scientific reports
|August 20, 2025
概括
本研究介绍了一种新的混合Salp Swarm算法 (SSA) 用于经济负载调度 (ELD) 问题,提高复杂电力系统的优化效率. 新的方法MSSA有效地处理严重的约束,在各种发电机组场景中表现出卓越的性能.
科学领域:
- 电气工程 电气工程
- 计算智能是一种计算智能.
- 优化算法 优化算法
背景情况:
- 经济负载调度 (ELD) 是电气工程中的一个关键的优化问题.
- 传统的方法与ELD的非凸性,多模式性和严重受限制的性质作斗争.
- 现有的算法往往无法有效解决复杂的ELD场景.
研究的目的:
- 介绍一个混合Salp Swarm算法 (SSA) 与自适应β登优化器 (AβHCO) 集成,用于解决经济负载调度 (ELD) 问题.
- 引入一种新型的记忆算法 (MSSA),它结合了全球搜索 (SSA) 和本地改进 (AβHCO) 进行增强的优化.
- 评估拟议的MSSA算法的性能在各种ELD问题实例上,具有不同的约束集.
主要方法:
- 将Salp Swarm算法 (SSA) 与自适应性β登优化器 (AβHCO) 混合,以创建一个模拟算法 (MSSA).
- SSA 起到一般精制剂 (基因编码) 的作用,而 AβHCO 则提供局部精制 (meme).
- 在多个发电机组配置 (3UG-850MW至80UG-21000MW) 中,对各种ELD问题进行MSSA测试,包括负载平衡,输出,受限运行区和坡道速度限制限制等问题.
主要成果:
- 拟议的MSSA算法在解决复杂的ELD问题方面展示了显著的可行性和有用性.
- 对比结果表明,MSSA在处理严重约束方面表现优于现有的算法.
- MSSA有效地解决了各种ELD场景,从小型到大型发电机系统.
结论:
- 混合MSSA算法为经济负载调度问题提供了强大而高效的解决方案.
- 结合全球和本地搜索的memetic方法有效地解决了ELD中非凸度和严重限制的挑战.
- 在电力系统优化方面,MSSA显示出实际应用的前景.
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