An enhanced MOEA/D-DE and its application to multiobjective analog cell sizing

Abstract

Recently, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) and its extended version by using differential evolution (DE) as the main search engine (MOEA/D-DE) were proposed, which outperform several widely used multiobjective evolutionary algorithms. MOEA/D decomposes a multiobjective problem into a number of scalar optimization sub-problems with a neighborhood structure and optimizes them simultaneously to approximate the Pareto-optimal set. In this paper, two mechanisms are investigated to enhance the performance of MOEA/D-DE. Firstly, a new replacement mechanism is proposed to call for a balance between the diversity of the population and the employment of good information from neighbors. Secondly, the scaling factor in DE is randomized to enhance the search ability. Comparisons are carried out with MOEA/D-DE on ten benchmark problems, showing that the proposed method exhibits significant improvements. Finally, the enhanced MOEA/D-DE is applied to a real world problem, the sizing of a folded-cascode amplifier with four performance objectives.