Improved Evolutionary Multitasking Optimization Algorithm With Similarity Evaluation of Search Behavior

Task similarity is a major requisite to trigger knowledge sharing in evolutionary multitasking optimization (EMTO). Unfortunately, most of the existing EMTO algorithms only focus on the similarity between population distributions of tasks, but ignore the search behavior of populations, which may degrade the performance of cross-task knowledge sharing. Motivated by this, an improved EMTO algorithm with similarity evaluation of search behavior (SESB-IEMTO), employing the particle swarm optimization (PSO) algorithm as a task solver for each task, is proposed. It comprises three key elements: 1) a dynamic similarity-based evaluation strategy, 2) a cross-task knowledge adaptation method, and 3) a search direction sharing mechanism. Primarily, the source tasks with similar search behavior are discriminated with the dynamic similarity-based evaluation strategy, where individuals can be fully exploited for cross-task evolution. Then, the knowledge derived from these source tasks is regulated by the cross-task knowledge adaption method for alleviating the risk of negative transfer caused by the heterogeneity between tasks. Moreover, to further promote knowledge sharing between tasks, the search direction sharing mechanism is developed to navigate tasks efficiently searching for promising regions. Finally, the convergence of SESB-IEMTO is analyzed, and the effectiveness and superiority are also verified with the experiments on several benchmark tests and a real-world application study.