Search

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.

• A novel evolutionary multi-tasking algorithm MFEA-GHS is proposed. • Genetic transform is introduced to facilitate knowledge transfer. • Hyper-rectangle search is proposed to balance exploration and exploitation. • MFEA-GHS is efficient at solving various multi-factorial optimization problems. Recently, evolutionary multi-tasking (EMT) has surfaced as a new search paradigm in the field of evolutionary computation to solve two or more tasks simultaneously. EMT algorithms accelerate the convergence of multiple optimization tasks by sharing useful knowledge among tasks, i.e., knowledge transfer is the key to the success of EMT algorithms. However, as the evolutionary search proceeds, the learnability of one task to others might decrease and the knowledge transfer becomes less efficient. To address this issue, this paper proposes a novel multi-factorial evolutionary algorithm by hybridizing two complementary strategies, namely genetic transform strategy and hyper-rectangle search strategy (MFEA-GHS). The proposed genetic transform strategy is applied in individual reproduction and aims to strengthen the knowledge transfer efficiency. Particularly, if two parent individuals are specific to different tasks, one parent individual is transformed to fit the other task by task space mapping. As such, higher-quality offspring individuals towards a target task can be generated with the transformed individual. The hyper-rectangle search strategy based on opposition learning is designed to perform efficient exploration and exploitation in both the unified search space and the sub-space of each task, which enables the population to search more unexplored regions. Comprehensive experiments are carried out on both single- and multi-objective EMT optimization problems. The experimental results demonstrate the efficiency of MFEA-GHS and the two proposed strategies. [ABSTRACT FROM AUTHOR]