A sphere-dominance based preference immune-inspired algorithm for dynamic multi-objective optimization
A sphere-dominance based preference immune-inspired algorithm for dynamic multi-objective optimization
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DOI：
10.1145/1830483.1830565
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发表时间：
2010-07
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影响因子：
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通讯作者：
Ruochen Liu;Wei Zhang;L. Jiao;Fang Liu;Jingjing Ma
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文献类型：
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作者：
Ruochen Liu;Wei Zhang;L. Jiao;Fang Liu;Jingjing Ma
现实世界的优化涉及不断变化的环境中的多个目标，称为动态多目标优化（DMO），是一项具有挑战性的任务，特别是决策者（DM）更喜欢特殊区域。基于一种新的偏好优势概念——领域优势和人工免疫系统（AIS）理论，本文提出了一种针对DMO的领域优势偏好免疫启发算法（SPIA）。 SPIA 的主要贡献是其偏好机制和抽样研究，分别基于新颖的领域优势和概率统计。此外，SPIA还引入了两种分别基于历史信息和高斯突变的超突变策略。在每一代中，通过抽样研究自动确定采用哪种方式进行超突变，以加速搜索过程。此外，SPIA的交互方案使DM能够在不修改算法主要结构的情况下包含他/她的偏好。结果表明，SPIA 可以获得分布良好的解集，有效地收敛到 DM 对 DMO 的首选区域。
Real-world optimization involving multiple objectives in changing environment known as dynamic multi-objective optimization (DMO) is a challenging task, especially special regions are preferred by decision maker (DM). Based on a novel preference dominance concept called sphere-dominance and the theory of artificial immune system (AIS), a sphere-dominance preference immune-inspired algorithm (SPIA) is proposed for DMO in this paper. The main contributions of SPIA are its preference mechanism and its sampling study, which are based on the novel sphere-dominance and probability statistics, respectively. Besides, SPIA introduces two hypermutation strategies based on history information and Gaussian mutation, respectively. In each generation, which way to do hypermutation is automatically determined by a sampling study for accelerating the search process. Furthermore, The interactive scheme of SPIA enables DM to include his/her preference without modifying the main structure of the algorithm. The results show that SPIA can obtain a well distributed solution set efficiently converging into the DM's preferred region for DMO.