概括
本研究提出了一种自动化方法来设计混合优化算法 (OA),通过将算法设计视为元优化问题来处理. 这种方法有效地为复杂的连续和组合式问题创建高效的混合式OA.
科学领域:
- 计算智能是一种计算智能.
- 超优化 (Meta-optimization) 是一个非常简单的方法.
- 算法设计 算法设计
背景情况:
- 混合化增强了优化算法 (OA),但是为复杂的问题设计它们是具有挑战性的.
- 现有的方法缺乏针对特定问题实例量身定制的自动化混合OA设计的系统方法.
研究的目的:
- 引入一种新的自动化设计混合式OA的自动化设计的自上而下的方法.
- 为集成多种混合化策略的基于协作的混合开放式应用开发一个通用设计模板.
- 将算法设计定义为一个超优化问题,并有效地解决它.
主要方法:
- 开发了一个基于协作的混合OA的一般设计模板.
- 制定算法设计作为一个数学元优化问题.
- 提出了一种改进的多因素进化算法,用于在多任务环境中解决元优化问题.
主要成果:
- 拟议的方法应用于CEC2017的基准函数和二进制背包问题.
- 数字结果证明了自动化设计方法的可行性和有效性.
- 该方法成功地为各种优化问题生成了高效的混合元启证.
结论:
- 新的自上而下的方法论为设计混合优化算法提供了一种有效的自动化方法.
- 提出的方法是多功能性的,证明了连续和组合优化任务的成功.
- 这项工作在专业和高性能优化算法的自动设计方面取得了重大进展.
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