Multi-swarm multi-objective optimization based on a hybrid strategy

被引:14
作者
Sedarous, Shery [1 ]
El-Gokhy, Sherin M. [1 ]
Sallam, Elsayed [1 ]
机构
[1] Tanta Univ, Fac Engn, Comp & Control Engn Dept, Tanta, Egypt
关键词
Multi-objective; Multi-swarm; Decomposition; Dominance; ALGORITHM; DOMINANCE;
D O I
10.1016/j.aej.2017.06.017
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Multi-objective optimization is a very competitive issue that emerges naturally in most real world problems. It is concerned with the optimization of conflicting objectives in multi-objective problems. The multi-objective problem treats with tradeoff solutions in order to satisfy all objectives. An extensive variety of algorithms has been developed to solve multi-objective optimization problems. In this paper, we presents a multi-swarm multiobjective intelligence-based algorithm enhanced with a hybrid strategy between decomposition and dominance (MSMO/2D) to improve convergence and diversity by splitting the primary swarm into a number of sub-swarms. The proposed algorithm is applied to fourteen standard problems and compared with two of the most familiar multi-objective optimization algorithms MOEA/D and (DMOPSO)-M-2. The experimental results give evidence that the multi-swarm armed by the hybrid strategy constitutes a better alternative for multi-objective optimization problems. (C) 2017 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V.
引用
收藏
页码:1619 / 1629
页数:11
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