Multi-objective evolutionary algorithm based on decomposition with integration strategy

Abstract

To improve the precision in the later stage of population evolution for multi-objective evolutionary algorithm based on decomposition (MOEA/D), a MOEA/D with integration strategy (MOEA/D-IS) is proposed. The proposed algorithm adopts multiple updating strategies, including a novel first-order differential learning strategy, the individual learning strategy, and the binary and polynomial crossover mutation strategy. The penalty-based boundary intersection approach and Chebyshev approach are used to alternately evaluate individuals. The proposed algorithm and five improved MOEA algorithms are tested on 21 functions. Simulation results show that MOEA/D-IS has good performance in diversity and convergence accuracy.