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

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.