改进的环境刺激和生物竞争战术 交互式人工生态优化算法用于集群
Wenyan Guo1, Mingfei Wu1, Fang Dai1
1School of Science, Xi'an University of Technology, Xi'an 710054, China.
Biomimetics (Basel, Switzerland)
|June 27, 2023
概括
这项研究引入了一种新的人工生态优化算法 (SIAEO),它在复杂的计算中克服了局部最佳值. 通过环境刺激和竞争,SIAEO增强了勘探和开采,提高了优化性能.
科学领域:
- 人工智能的人工智能
- 优化算法 优化算法
- 计算智能是一种计算智能.
背景情况:
- 复杂的计算通常会在传统的优化算法中遭受局部最佳的损害.
- 在人工生态优化算法 (AEO) 中的顺序执行可以导致次优化解决方案.
研究的目的:
- 开发一个改进的人工生态优化算法 (SIAEO),解决局部最佳问题.
- 提高生态优化算法的探索和利用能力.
主要方法:
- 引入基于人口多样性的环境刺激,用于交互式操作员执行.
- 模拟掠夺作为不同的任务,以成功率为基础执行.
- 整合了一个生物竞争运营商来完善再生战略.
- 集成的随机平均压抑交替用于高级利用.
主要成果:
- 该SIAEO算法证明了越来越好地逃避局部最佳的能力.
- 对CEC2017和CEC2019测试集的比较分析显示,与其他改进的算法相比,性能提高了.
结论:
- 在复杂的优化任务中,SIAEO算法有效地减轻了局部最佳问题.
- 拟议的改进导致在勘探和开采阶段的卓越性能.
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