MOCOVIDOA:一种新的多目标冠状病毒疾病优化算法,用于解决多目标优化问题
Asmaa M Khalid1, Hanaa M Hamza1, Seyedali Mirjalili2
1Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Egypt.
Neural computing & applications
|June 26, 2023
概括
一个新的多目标冠状病毒疾病优化算法 (MOCOVIDOA) 有效地解决了复杂的全球优化问题. 这种新的方法在基准和现实世界的工程测试中显示出与现有方法相比更高的性能.
科学领域:
- 计算智能是一种计算智能.
- 优化算法优化算法
- 生物启发的计算是生物启发的.
背景情况:
- 全球优化问题往往涉及多个相互矛盾的目标.
- 现有的多目标元启发可能在高效处理复杂的搜索空间方面面临挑战.
- 需要以自然过程为灵感的强大算法至关重要.
研究的目的:
- 引入一种新的多目标冠状病毒疾病优化算法 (MOCOVIDOA).
- 用最多三个目标函数解决全球优化问题.
- 通过使用生物启发的机制来增强解决方案选择.
主要方法:
- 开发多目标的冠状病毒疾病优化算法 (MOCOVIDOA).
- 使用档案来存储非主导解决方案.
- 实施一个模拟病毒移的轮盘选择机制.
- 对27个多目标问题的评估 (21个基准,6个工程设计).
主要成果:
- 在6个评估指标 (IGD,GD,MS,SP,HV,delta p) 中,MOCOVIDOA表现出卓越的表现.
- 统计分析 (Wilcoxon排名和值测试) 证实了该算法的优势超过了五种常见的多目标元启发.
- 该算法在解决基准和现实世界的工程设计问题方面都被证明是有效的.
结论:
- 新的MOCOVIDOA算法在多目标优化方面取得了重大进展.
- 它的生物启发方法为解决复杂的优化任务提供了强大而高效的方法.
- 莫科维多亚显示出强大的适用性和解决各种多目标问题的潜力.
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