A Centroid Guided Cluster Transformation for Dynamic Multi-Objective Optimization Algorithm

In recent years, prediction-based algorithms have made significant progress in solving dynamic multi-objective optimization problems (DMOPs). However, most existing algorithms only consider information from several consecutive environments and ignore previous search experiences. This article uses the search experience of the Regularity Model-Based Multiobjective Estimation of Distribution Algorithm (RM-MEDA) to propose a dynamic multiobjective optimization algorithm based on the centroid-guided cluster transformation (CGCT-RM- MEDA). When the environment changes, the information from the statically optimized model in the previous environment is used to estimate a new distribution model through a cluster transformation. At the same time, the location of the population distribution is predicted using a Long Short-Term Memory (LSTM) network, which is used to estimate the cluster centers of the model distribution. The empirical study evaluated the performance of CGCT-RM-MEDA using 14 benchmark functions and one performance metric. The experimental results show that CGCT-RM-MEDA outperforms seven peer algorithms in performance.