Dual-information-based evolution and dual-selection strategy in evolutionary multiobjective optimization

Abstract

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem into a number of scalar optimization subproblems and optimizes them simultaneously in a collaborative manner in one run. The recently proposed stable matching (STM)-based selection is a variant of MOEA/D that achieves one-to-one STM between subproblems and solutions on the basis of mutual preferences. However, the STM has a high probability of matching a good convergence solution with a subproblem, which results in an imbalance between convergence and diversity of selection result. In this study, we propose a new variant of MOEA/D with dual-information and dual-selection (DS) strategy (MOEA/D-DIDS). Different from other evolutionary operations, we use an adaptive historical and neighboring information in generating new individuals to avoid local optima and accelerate convergence rate. In the selection operation, we use the adaptive limited STM ($$ \beta {\text{LSTM}} $$) strategy, where parameter β is adaptive in accordance with the evolutionary process, as a guideline to select a population from the mixed population that survives as the next parent population. In addition to $$ \beta {\text{LSTM}} $$, we use an STM to select competitive individuals as the members of the next mixed population. This DS strategy not only balances convergence and diversity but also holds the elite solutions. The effectiveness and competitiveness of MOEA/D-DIDS are validated and compared with several state-of-the-art evolutionary multiobjective optimization algorithms on benchmark problems.