基于生物地理的多目标离散优化与约束
Leyi Hu1, Xuan Liu1,2, Xiangyu Qu1
1School of Information Engineering, Minzu University of China, Beijing, China.
概括
本研究引入了一种改进的基于生物地理学的优化 (BBO) 算法,以解决具有约束的复杂,多目标离散优化问题. 增强的BBO在寻找最佳解决方案方面表现出有效性和效率.
科学领域:
- 计算智能是一种计算智能.
- 优化算法 优化算法
- 进化计算是一种进化计算.
背景情况:
- 基于生物地理的优化 (BBO) 是一种进化算法,通过自适应迁移增强搜索能力.
- 最初的BBO仅限于连续的,单一目标的问题,不包括离散的和多个目标的优化挑战.
研究的目的:
- 提出一种改进的基于生物地理学的优化 (BBO) 算法,能够解决具有多个约束的多目标离散优化问题.
- 为了提高复杂的优化任务的搜索解决方案的多样性和融合.
主要方法:
- 定义决策矩阵和目标向量,以适应多目标离散优化变量和目标函数.
- 引入了理想点和实用函数来评估候选解决方案.
- 拟议的相似性,可重复性,成本和停滞值,以平衡解决方案的多样性和融合.
主要成果:
- 改进的BBO算法有效地解决了具有多个约束的多目标离散优化问题.
- 对NP-hard复合函数的实验结果验证了该方法的有效性和效率.
- 拟议的门有助于提高搜索多样性和趋同.
结论:
- 开发的BBO算法为复杂的离散,多目标优化问题提供了强大的解决方案.
- 该方法提供了一个框架,用于增强进化算法在处理受约束的优化任务.
- 该研究证实了拟议的BBO增强的实际适用性和效率.
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