Dynamic multi-objective optimization algorithm based on incremental Gaussian mixture model

In recent years, prediction-based algorithms have made significant progress in solving dynamic multi-objective optimization problems (DMOPs). However, most existing methods only consider information from several consecutive environments and ignore past search experiences. To address the issue, this paper proposes a novel dynamic multi-objective evolutionary algorithm (DMOEA) based on an incremental Gaussian mixture model (IGMM). When environmental changes occurred, the initial population in the new environment is composed of two parts. The one part includes a few predicted individuals generated by IGMM aiming to explore the potential correlation between environments. To ensure quality of the individuals generated by IGMM, a feature-based augmentation strategy is employed to generate representative training data before the training process of IGMM. The other part consists of some individuals created via polynomial mutation operator based on randomly selected solutions from the previous environment. Based on the hybrid initial population, IGMM-DMOEA can quickly respond to environmental changes. To testify the performance of IGMM-DMOEA, twenty widely used benchmark functions and three real-world applications are adopted in this study. Extensive experimental results verify that IGMM-DMOEA can exhibit effective response to environmental changes. Comparisons results between it and other seven peer algorithms suggest that IGMM-DMOEA attains more reliable performance, measured by three popular metrics. Moreover, the effectiveness and efficiency of the new proposed strategies are discussed based on extensive experiments.