A classification tree and decomposition based multi-objective evolutionary algorithm with adaptive operator selection

Abstract

Abstract Adaptive operator selection (AOS) is used to dynamically select the appropriate genic operator for offspring reproduction, which aims to improve the performance of evolutionary algorithms (EAs) by producing high-quality offspring during the evolutionary process. This paper proposes a novel classification tree based adaptive operator selection strategy for multi-objective evolutionary algorithm based on decomposition (MOEA/D-CTAOS). In our proposal, the classification tree is trained by the recorded data set which contains the information on the historical offspring. Before the reproduction at each generation, the classifier is used to predict each possible result obtained by different operators, and only one operator with the best result is selected to generate offspring next. Meanwhile, a novel differential evolution based on search inertia (SiDE) is designed to steer the evolutionary process in a more efficient way. The experimental results demonstrate that proposed MOEA/D-CTAOS outperforms other MOEA/D variants on UF and LZ benchmarks in terms of IGD and HV value. Further investigation also confirms the advantage of direction-guided search strategy in SiDE.

Keywords

• Multi-objective optimization
• Adaptive operator selection
• Classification tree
• Search inertia