Many multiobjective evolutionary algorithms are based Pareto domination, among them NSGA II and SPEA 2 are two very popular ones. MOEA/D is a very recent multiobjective evolutionary algorithm using decomposition. In this paper, we implement MOEA/D for multi-objective flowshop scheduling problems. We study the replacement strategy of neighboring solutions, the determination of the reference point, and compare different decomposition methods. Experimental results demonstrate that MOEA/D outperforms NSGA II and SPEA 2 significantly for the 2-objective and 3-objective benchmark flowshop-scheduling instances.
Imperialist Competitive Algorithm (ICA) is a new population-based evolutionary algorithm. Previous works have shown that ICA converges quickly but often to a local optimum. To overcome this problem, this work proposed two modifications to ICA: perturbed assimilation move and boundary bouncing. The proposed modifications were applied to ICA and tested using six well-known benchmark functions with 30 dimensions. The experimental results indicate that these two modifications significantly improve the performance of ICA on all six benchmark functions.
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