Knowledge Transfer With Mixture Model in Dynamic Multi-Objective Optimization

Most existing dynamic multiobjective evolutionary algorithms (DMOEAs) have been designed to handle dynamic multiobjective optimization problems (DMOPs) with regular environmental changes. However, they often overlook scenarios where environmental changes are irregular and less predictable. Recently, knowledge transfer has been proposed as a novel paradigm for solving DMOPs. Despite this, most transfer strategies only consider transferring knowledge obtained from the previous environment while ignoring significant differences that may exist between adjacent environments due to irregular changes. To address these issues, this article proposes a novel knowledge transfer strategy based on a Gaussian mixture model (GMM denoted as KTMM) for solving DMOPs with irregular changes. In particular, an adaptive GMM is designed to capture the knowledge of historical environments, which is then transferred to generate an initial population for the new environment. Additionally, a new method for controlling irregular changes is introduced into widely used benchmarks to form the DMOP benchmark with irregular changes. Our proposed KTMM is compared with six state-of-the-art DMOEAs on several benchmark problems with irregular changes. Experimental results demonstrate the superiority of our proposed method in most test instances and in a real-world problem.