Multitask Particle Swarm Optimization with Heterogeneous Domain Adaptation

The main goal of multitask optimization (MTO) is the parallel optimization of multiple different tasks. However, since different tasks in the MTO problem usually have heterogeneous characteristics, it is difficult to realize the positive knowledge transfer among tasks, resulting in poor convergence. To cope with this problem, a multi-task particle swarm optimization with a heterogeneous domain adaptation strategy (MTPSO-HDA) is proposed to transfer positive knowledge among heterogeneous tasks. First, a nonlinear mapping between the source task and the target task is constructed based on the adaptive kernel function. Then, source tasks are mapped to the target task space to reduce the differences among heterogeneous tasks. Second, a multi-source domain adaptive strategy based on fitness landscape similarity is designed to implement domain adaptation. Then, the importance of each source domain is quantitatively described to reduce the differences between multiple source domains and a target domain and achieve domain adaptation among heterogeneous tasks. Third, a heterogeneous multitask particle swarm optimization mechanism is introduced to facilitate positive knowledge transfer among heterogeneous tasks. Then, an appropriate evolutionary mechanism is designed according to the fitness landscape similarity to achieve positive knowledge transfer. Finally, to assess the effectiveness of the MTPSO-HDA algorithm, some experiments are designed based on some benchmark problems and real-world application of wastewater treatment process. The results demonstrate that the proposed MTPSO-HDA algorithm can promote positive knowledge transfer among heterogeneous tasks to improve convergence.