A multi-objective artificial bee colony algorithm
A multi-objective artificial bee colony algorithm
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DOI：
10.1016/j.swevo.2011.08.001
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发表时间：
2012-02-01
影响因子：
10
通讯作者：
Hassanizadeh, Bahareh
中科院分区：
文献类型：
--
作者：
Akbari, Reza;Hedayatzadeh, Ramin;Hassanizadeh, Bahareh
针对多目标优化问题，提出了一种基于人工蜂群的多目标优化方法MOABC。MOABC使用基于网格的方法来自适应地评估在外部档案中维护的帕累托前沿。外部档案用于控制个体的飞行行为和构建蜂群。被雇用的蜜蜂基于外部档案中维护的非支配解来调整它们的轨迹。另一方面，工蜂会选择被雇佣的工蜂所宣传的食物来源来更新自己的位置。这些食物来源的质量计算的基础上的帕累托优势的概念。MOABC使用侦察蜂来清除质量差的食物来源。与其他国家的最先进的算法相比，所提出的算法进行了评估一组标准的测试问题。实验结果表明，所提出的方法是有竞争力的相比，在这项工作中考虑的其他算法。(C)2011 Elsevier B.V.保留所有权利。
This work presents a multi-objective optimization method based on the artificial bee colony, called the MOABC, for optimizing problems with multiple objectives. The MOABC uses a grid-based approach to adaptively assess the Pareto front maintained in an external archive. The external archive is used to control the flying behaviours of the individuals and structuring the bee colony. The employed bees adjust their trajectories based on the non-dominated solutions maintained in the external archive. On the other hand, the onlooker bees select the food sources advertised by the employed bees to update their positions. The qualities of these food sources are computed based on the Pareto dominance notion. The scout bees are used by the MOABC to get rid of food sources with poor qualities. The proposed algorithm was evaluated on a set of standard test problems in comparison with other state-of-the-art algorithms. Experimental results indicate that the proposed approach is competitive compared to other algorithms considered in this work. (C) 2011 Elsevier B.V. All rights reserved.