Efficient Search by Improvement of Task Similarity in Multitasking Optimization Problems

Abstract

In recent years, the multitask optimization problem has attracted attention as a new problem class to which evolutionary computation is applied. The multitask optimization problem is an optimization problem that aims to solve multiple tasks in parallel. In conventional optimization problems, a solver is prepared for each task and tuning is required. However, in multitask optimization problems, the tuning of one solver is enough, so the burden on the designer can be reduced. In addition, by solving multiple optimization problems in parallel, it becomes possible to share knowledge among tasks, so that efficient search can be expected. In a single-objective multitask optimization problem, inter task crossover has a positive effect between similar tasks and a negative effect between dissimilar tasks. The conventional multitask optimization methods show good performance in similar multiple task optimization. However, when tasks are not similar, these conventional methods decrease the interaction such as inter task crossover. Therefore, the search performances are not different from that of the single-objective optimization methods independently applied to each task. In this paper, we attempt to improve the similarity between tasks by rearranging the notation of design variables for tasks that are not similar. This paper shows that the search performance can be improved in the widely used two-task benchmark multitask optimization problems.