植被进化与动态成熟战略和多种突变战略解决优化问题
Rui Zhong1, Fei Peng2, Enzhi Zhang1
1Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0808, Japan.
Biomimetics (Basel, Switzerland)
|October 27, 2023
概括
这项研究通过动态成熟度和多样化的突变策略来增强植被进化 (VEGE) 算法. 这些改进加速了趋同,并提高了持续优化问题的准确性.
科学领域:
- 计算智能是一种计算智能.
- 优化算法的优化算法
- 进化计算的演变
背景情况:
- 传统植被进化 (VEGE) 在持续优化中与本地最佳情况作斗争.
- 现有的进化算法 (EA) 需要进一步提高性能.
研究的目的:
- 引入新的搜索策略,以提高VEGE的业绩.
- 为了提高在解决持续优化问题的收速度和准确性.
主要方法:
- 开发了一个动态的成熟度策略,以优惠的种子产生基于健康状况.
- 引入了多种突变方法的多样化突变策略,以增加人口多样性.
- 在基准和工程问题上对标准VEGE和其他七个EA进行了评估.
主要成果:
- 拟议的VEGE带有增强战略,显示了明显加快的趋同.
- 在大多数测试的优化问题中观察到更好的收精度.
- 分析证实了这两种新策略对VEGE业绩的重大贡献.
结论:
- 动态成熟度和多样化的突变策略有效地克服了VEGE的局限性.
- 增强的VEGE为持续优化任务提供了卓越的性能.
- 这项工作为复杂的问题提供了更强大,更有效的进化方法.
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