一个新的元启发优化器,通过人工智能通过GPS实现可靠的经济调度
Mahmoud Ibrahim Mohamed1, Ali M Yousef2, Ahmed A Hafez2
1Electrical Engineering Department, Assiut University, Assiut, Egypt. mahmoudmosaad@eng.aun.edu.eg.
Scientific reports
|June 23, 2025
概括
一个新的混合优化器将一个模两可的优化器与人工智能 (AI) 结合起来,克服了元启发算法的局限性. 这种方法提高了效率,并确保了对经济调度等复杂问题的全球最佳解决方案的趋同.
科学领域:
- 工程和应用科学 工程和应用科学
- 计算智能是一种计算智能.
- 优化技术 优化技术
背景情况:
- 超启发式优化算法被广泛使用,但受到局部最佳,缓慢的融合和高计算需求的影响.
- 现有的优化器经常与工程和科学研究中常见的复杂,多变量问题作斗争.
研究的目的:
- 引入一种新,简单和有效的混合优化器,以解决当前元启发式算法的缺陷.
- 通过一系列元启发式优化器验证拟议的混合方法,从已建立到最近的.
- 证明解决方案对现实世界优化挑战的适用性,特别是经济调度.
主要方法:
- 开发了一个混合优化器,将一个模两可的优化器与人工智能 (AI) 集成在一起.
- 使用遗传算法 (GA),粒子群优化 (PSO),基于教学学习的优化 (TLBO) 和人工大猩猩部队优化 (AGTO) 评估了混合优化器的性能.
- 在IEEE 30 总线系统的经济调度问题上应用了混合解决方案.
主要成果:
- 建议的混合优化器始终趋于全球最佳解决方案.
- 实现了经济调度问题的最低能源成本.
- 与个人优化器相比,与最小的代和计算要求证明了卓越的可靠性和充分性.
结论:
- 这种新的混合优化器有效地克服了传统元启发式算法的局限性.
- 该技术被证明是可靠和高效的,为复杂的优化任务提供了强大的解决方案.
- 在各种元启发式算法和实际工程问题中验证了适用性.
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