Multitask Level-Based Learning Swarm Optimizer

Abstract

Evolutionary multitasking optimization (EMTO) is currently one of the hottest research topics that aims to utilize the correlation between tasks to optimize them simultaneously. Although many evolutionary multitask algorithms (EMTAs) based on traditional differential evolution (DE) and the genetic algorithm (GA) have been proposed, there are relatively few EMTAs based on particle swarm optimization (PSO). Compared with DE and GA, PSO has a faster convergence speed, especially during the later state of the evolutionary process. Therefore, this paper proposes a multitask level-based learning swarm optimizer (MTLLSO). In MTLLSO, multiple populations are maintained and each population corresponds to the optimization of one task separately using LLSO, leveraging high-level individuals with better fitness to guide the evolution of low-level individuals with worse fitness. When information transfer occurs, high-level individuals from a source population are used to guide the evolution of low-level individuals in the target population to facilitate the effectiveness of knowledge transfer. In this way, MTLLSO can obtain the satisfying balance between self-evolution and knowledge transfer. We have illustrated the effectiveness of MTLLSO on the CEC2017 benchmark, where MTLLSO significantly outperformed other compared algorithms in most problems.

Keywords

• evolutionary multitasking optimization (EMTO)
• particle swarm optimization (PSO)
• evolutionary operators