An archive-based artificial bee colony optimization algorithm for multi-objective continuous optimization problem
An archive-based artificial bee colony optimization algorithm for multi-objective continuous optimization problem
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DOI：
10.1007/s00521-016-2821-7
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发表时间：
2018-11-01
影响因子：
6
通讯作者：
Zhang, Changsheng
中科院分区：
文献类型：
--
作者：
Ning, Jiaxu;Zhang, Bin;Zhang, Changsheng
多目标优化研究已成为智能计算领域的研究热点之一。提出了一种基于档案库的多目标人工蜂群优化算法(AMOABC)，该算法使用外部档案库保存当前获得的非支配最优解，并设计了一种新颖的Pareto局部搜索机制并将其融入到优化过程中。为了避免搜索过程陷入局部极小，提出了一种新的食物源生成机制，并针对蜜蜂和局部搜索过程设计了不同的搜索策略。对AMOABC算法进行了全面的基准测试，并与目前一些相关的MO算法进行了比较，证明了该算法的有效性。
Research on multi-objective optimization (MO) has become one of the hot points of intelligent computation. In this paper, an archive-based multi-objective artificial bee colony optimization algorithm (AMOABC) is proposed, in which an external archive is used to preserve the current obtained non-dominated best solutions, and a novel Pareto local search mechanism is designed and incorporated into the optimization process. To prevent the searching process from being trapped into local minimum, a novel food source generating mechanism is put forward, and different search strategies are designed for bees and local search process. Comprehensive benchmarking and comparison of AMOABC with the some current-related MO algorithms demonstrate its effectiveness.