A Population Partition and Prediction Strategy-Based Evolutionary Algorithm for Dynamic Multi-Objective Optimization

Dynamic multi-objective optimization problems (DMOPs) are difficult because the Pareto set (PS) and Pareto front (PF) change over time, requiring algorithms to respond quickly while maintaining convergence and diversity. Existing prediction-based dynamic multi-objective evolutionary algorithms (DMOEAs) often rely on linear assumptions or individual-level modeling, which may limit their ability to respond to non-affine or structurally changing PS trajectories. To address this issue, this paper proposes a dynamic multi-objective evolutionary algorithm based on population partition and prediction strategy, termed PPDMOEA. It employs a cooperative response mechanism in which elite solutions mainly support convergence recovery and regular solutions preserve diversity. First, a two-dimensional evaluation strategy is used to select representative elite solutions for prediction by considering both convergence and distribution. Then, an order-insensitive set-encoded LSTM model is used to describe the temporal evolution of elite solution sets without relying on fixed element positions. Finally, a dynamic solution redistribution strategy is introduced to improve diversity and adaptability in new environments. Experiments on the CEC2018 dynamic multi-objective benchmark against seven representative DMOEAs show that the proposed algorithm achieves competitive MIGD and MHV performance on this benchmark, while its advantages are not uniform across all problem instances.