具有多个搜索策略和高级内存机制的进化salp群算法,用于解决全球优化和复杂的工程问题
1Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. hoda_zamani@sco.iaun.ac.ir.
Scientific reports
|September 30, 2025
概括
本研究介绍了一种进化的Salp Swarm算法 (ESSA),用于改进复杂的优化任务. 为了应对全球优化和设计挑战,ESSA提高了解决方案质量和融合速度.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 工程应用 工程应用
背景情况:
- 现实世界的优化问题是复杂的,具有许多变量和约束,挑战传统的算法.
- 萨尔普群算法 (SSA) 提供了简单性,但缺乏指导人群向最佳解决方案的准确性.
- 现有的方法与复杂的设计挑战和更清洁的生产系统作斗争.
研究的目的:
- 为复杂的优化问题提出一个增强的Salp Swarm算法 (ESSA).
- 改善人口多样性,适应性搜索和趋同稳定性.
- 在现实应用中解决标准SSA的局限性.
主要方法:
- 开发了ESSA的两种新的进化搜索策略,即多样性和适应性搜索.
- 实施了加强的SSA搜索策略,以实现稳定的趋同.
- 整合了先进的内存机制和存档调节的随机通用选择.
主要成果:
- 在CEC 2017和CEC 2020基准函数上,ESSA表现优于SSA和其他七个领先的算法.
- 实现了高优化效率:84.48% (30D),96.55% (50D) 和89.66% (100D) 的优化效率.
- 在解决方案质量和各个维度的融合速度方面表现优于竞争对手.
结论:
- 欧洲航天局有效解决复杂的优化问题,包括更清洁的生产和设计挑战.
- 拟议的算法显示了优化效率和趋同的显著改进.
- 对于苛刻的优化任务,ESSA提供了强大而高效的解决方案.
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