应用改进的殖民地优化算法,混合策略的应用,使用调度用于医院的患者管理
Md Limonur Rahman Lingkon1, Md Sazol Ahmmed1
1Department of Industrial & Production Engineering, Rajshahi University of Engineering & Technology (RUET), Rajshahi- 6204, Bangladesh.
Heliyon
|January 3, 2025
概括
这项研究为大规模问题引入了一种改进的群优化 (ICMPACO). ICMPACO算法提高了融合速度和解决方案多样性,在门分配和旅行销售员问题上表现优于以前的方法.
科学领域:
- 人工智能的人工智能
- 优化算法 优化算法
- 计算科学 计算科学
背景情况:
- 大规模的优化问题需要平衡融合速度和解决方案多样性.
- 现有的群优化方法可能会面临局部优化和效率方面的挑战.
研究的目的:
- 介绍一种改进的群优化 (ICMPACO) 技术,以提高大规模优化的性能.
- 为了平衡融合速度和解决方案多样性,以获得更好的优化结果.
主要方法:
- ICMPACO算法集成了共同进化,多种群策略,激素扩散和先进的激素更新.
- 它将种群分为精英和普通群体,并将问题分解为子问题.
- 费洛蒙扩散允许逐渐扩散到附近地区,增强探索.
主要成果:
- ICMPACO成功地解决了门分配问题,并优化了旅行销售员问题 (TSP).
- 证明了改进的优化能力,稳定性和分配效率 (83.5%的门分配).
- 有效地分配了132名患者到20个医院门口,最大限度地减少了处理时间.
结论:
- 与现有方法相比,ICMPACO算法提供了优越的优化性能.
- 它为复杂的现实问题提供了稳定和高效的方法,例如门分配和TSP.
- 该方法有效地平衡了大规模优化挑战的融合和多样性.
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