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Analysis and comparison of multi-objective evolutionary approaches on the multi-objective 1/0 unit commitment problem

Abstract:

In this paper, we analyze the behavior and compare the performance of three state-of-the-art Multi-objective Evolutionary Algorithms (MOEAs) based on three different approaches when solving the Multi-Objective Unit Commitment Problem (MO-UCP). Particularly, we study the performance of representative Pareto-, indicator- and decomposition-based MOEAs (namely NSGA-II, SMS-EMOA and MOEA/D) when solving standard MO-UCP test instances. The MOEAs employed in our comparative study, handle binary representation while lambda-iteration method is probabilistically used for assigning the economic/environmental power real dispatch. In our experiments, each evolutionary approach adopts the window crossover and the window mutation. A detailed study of the impact of these operators is carried out when different crossover and mutation ratios are employed. The comparative study presented here, shows that for low-dimensional instances, the performance of the three evolutionary approaches became very similar. However, when the dimension of the problem (large bit strings) increases, the performance of NSGA-II and SMS-EMOA became better than MOEA/D.