增强的内核搜索优化器,具有沙猫行为和多策略融合,用于经济排放调度
Ruyi Dong1, Yingxin Tang1, Ali Asghar Heidari2
1College of Information and Control Engineering, Jilin University of Chemical Technology, Jilin, China.
Scientific reports
|November 18, 2025
概括
混沌地图,自适应的t分布突变和带有螺旋搜索 (CSTKSO) 的沙猫行为算法增强了对复杂问题的内核搜索优化 (KSO). CSTKSO在基准测试和真实世界经济排放排放时间表中表现出色.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 超启发式计算 超启发式计算
背景情况:
- 内核搜索优化 (KSO) 在高维空间中面临准确性损失.
- 在复杂的场景中,KSO的映射近似可能无法找到真正的最佳解决方案.
研究的目的:
- 引入一个增强的优化算法,CSTKSO,解决KSO的局限性.
- 在优化中改进人口初始化,解决方案扰动和搜索策略.
主要方法:
- 使用混沌映射进行人口初始化,以防止过早的融合.
- 包含适应性t分布突变,并为勘探开发平衡进行动态参数调整.
- 采用以沙猫行为为灵感的机制,用于强大的搜索优化.
主要成果:
- 在50个IEEE CEC基准函数上,CSTKSO表现出与原始KSO和其他算法相比的卓越性能.
- 该算法在实际的经济排放调度问题中取得了更好的结果.
- CSTKSO有效地解决了KSO的缺点,同时保持了它的优势.
结论:
- 对于复杂,高维度的问题,CSTKSO提供了一个更强大,更有效的优化工具.
- 改进的算法显示了对现实世界应用的巨大潜力.
- CSTKSO代表了metaheuristic优化技术的显著进步.
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