SEMO declines in convergence speed on large-scale MOCOPs compared to NSGA-II, SMS-EMOA and MOEA/D, with absence of crossover as the main cause; adding crossover accelerates convergence at the expense of diversity.
Global vs local search on multi-objective nk-landscapes: Contrasting the impact of problem features
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On Scalability of Multi-Objective Evolutionary Algorithms on Combinatorial Optimisation Problems
SEMO declines in convergence speed on large-scale MOCOPs compared to NSGA-II, SMS-EMOA and MOEA/D, with absence of crossover as the main cause; adding crossover accelerates convergence at the expense of diversity.