Effective Multi-Objective Evolutionary Algorithm for Industrial Applications
Hiroaki Fukumoto, Akira Oyama
Progress in Computing Applications, Vol. 8, No. 1, 2019.
DOI: 
Link: http://www.dline.info/pca/fulltext/v8n1/pcav8n1_1.pdf
Abstract:
Multi-objective evolutionary algorithms (MOEAs) are population based global optimization algorithms and it is said that the performance of the MOEAs depends on the population size. Considering that the recent trends of computer development is in large-scale many-core architectures, and massive parallel computation is getting feasible in more companies and laboratories, the available population size is increasing and the efficiency of MOEA with large population size should be enhanced. This study examines the effect of the population size on MOEAsâ€™ performance on a real-world-derived benchmarking optimization problem, with large population size. In this paper, three mate selection schemes with different degree of elitist strategy are adapted to NSGA-II-M2M. The experimental results show that the elitist strategy can efficiently make use of the effect of the large population size, therefore can reduce the turn-around time.