An Improved Genetic Transmission and Dynamic-Opposite Learning Strategy for Multitasking Optimization

Abstract

Multi-task evolutionary optimization is a new method proposed in recent years to solve optimization problems. Compared with traditional single-task optimization, the multi-task optimization mechanism based on evolutionary algorithms promotes the evolution of population by sharing the potential similarity and complementarity between different problems, so as to improve the performance and efficiency of solving problems. However, with the evolution of the population, the ability of one task to learn from other tasks may decrease, and the efficiency of knowledge transfer will also decrease. To address this issue and improve the quality of knowledge transfer among the tasks, this article proposes a new genetic transmission strategy and mutation strategy of multi task optimization algorithm, namely DOMLMFEA. In particular, in the genetic process, if two parent individuals are specific to different tasks, one parent individual is mapped to the vicinity of the other parent individual through task space mapping, and high-quality offspring individuals are generated through crossover. In addition, a dynamic opposite mutual learning mutation strategy based on wavelet basis function is introduced to generate promising solutions. It can effectively explore and develop in the unified search space and the subspace of each task, and improve the local search ability of the algorithm and the diversity of solutions. In order to evaluate the performance of DOMLMFEA, this paper tests on classical multi-task optimization problems, and compared with several other state-of-the-art MFEA variants. Numerical and simulation results show the effectiveness of the proposed DOMLMFEA.