Multitasking Multi-Swarm Optimization

Multi-task optimization (MTO) is a newly emerging research area in the field of optimization, studying on how to solve multiple optimization problems at the same time so that the processes of solving different but relevant problems could help each other via knowledge transfer to improve the overall performance of solving all problems. Evolutionary MTO (EMTO) employs evolutionary algorithms as the optimizer and treats the candidate solutions that perform commonly well on multiple tasks as the transferable knowledge between these tasks. In this work, we propose a multitasking multi-swarm optimization (MTMSO) algorithm which extends a popular dynamic multi-swarm optimization (DMS-PSO) algorithm into the multitasking scenario. In MTMSO, the whole swarm is randomly partitioned into multiple swarms (i.e., task groups) with each being responsible for solving a specific task, and each swarm is further partitioned into multiple sub-swarms. Within each task group, optimization is performed as per the mechanism of DMS-PSO for solving a specific task. Cross-task knowledge transfer is realized via probabilistic crossover of the personal bests of the particles from different task groups. Both task groups and each group's sub-swarms are periodically reformed to maintain search diversity. An adaptive local search process, featuring dynamic allocation of the computational resource for each task, is incorporated in the final stage of optimization to improve the quality of the best solution found for each task. The proposed MTMSO algorithm is compared with a single-task conventional PSO and a popular EMTO algorithm on two test suites composing of 9 simple and 10 complex single-objective MTO problems, respectively, which demonstrates its superiority.