对加权叠加吸引-排斥算法的性能分析,用于解决困难的优化问题
1Faculty of Engineering, Department of Industrial Engineering, Dokuz Eylül University, Izmir, Turkey.
概括
新的加权叠加吸引-排斥算法 (WSAR) 在优化问题上表现强. 它的性能优于其前身 (WSA) 和其他方法,证明了有效的融合和搜索能力.
科学领域:
- 计算智能是一种计算智能.
- 优化算法的优化算法
- 超启发式计算是一种超启发式计算.
背景情况:
- 优化问题在科学和工程中无处不在.
- 现有的元启发算法在平衡探索和开发方面面临着挑战.
- 权重叠加吸引算法 (WSA) 提供了一种新的方法,但需要改进.
研究的目的:
- 为了评估加权叠加吸引力-排斥算法 (WSAR) 的性能.
- 将WSAR与其前身 (WSA) 和其他最先进的元启发算法进行比较.
- 评估WSAR在不受约束和受约束的优化问题上的有效性.
主要方法:
- 实施和测试WSAR算法.
- 在基准数据集上进行了广泛的计算实验,包括CEC2015和CEC2020.
- 统计比较WSAR的结果与WSA和其他领先的元启发算法.
主要成果:
- 在各种基准优化问题上,WSAR表现出强的表现.
- 与WSA相比,该算法取得了具有竞争力的,往往优异的结果.
- WSAR表现出良好的融合速度和有效的搜索能力,参数最小.
结论:
- 在元启发性优化中,WSAR是一个有希望的进步.
- 算法的设计,包括排斥性的运动,增强了其解决问题的能力.
- 对于复杂的优化任务,WSAR提供了一个可行的和有效的替代方案.
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