A Learning Algorithm Based on Similarity Identification and Knowledge Transfer for Dynamic Multi-Objective Optimization

Abstract

Prediction-based dynamic multi-objective optimization algorithms (DMOEAs) are widely used to explore the relationships of Pareto-optimal solutions (POSs) under continuous time steps, aiming to tackle dynamic multi-objective optimization problems (DMOPs). However, DMOPs with irregular POS shapes pose significant challenges to the quality of predicted solutions owing to the misaligned binding of solutions. To bridge this gap, this paper proposes a learning algorithm based on similarity identification and knowledge transfer, called SIKT-DMOEA, which comprises the following three steps. Firstly, a cluster centers-driven feedforward neural network (CCD-FNN) with global optimal binding assignment is constructed, aiming to learn the regional POS dynamics between adjacent environments. Secondly, a similarity identification technique archives valuable knowledge in historical environments and transfers it to the current environment for evolutionary acceleration. Finally, a population reconstruction strategy is presented for adaptive guidance according to the dominant property of each solution, which approximates the new POS with superior convergence and distribution. Comprehensive experiments demonstrate that SIKT-DMOEA manifests competitiveness when addressing DF test problems and one real-world application problem compared to state-of-the-art DMOEAs. SIKT-DMOEA has corroborated its capability of effectively reducing the loss of population convergence and diversity facing different patterns of environmental changes.