Multipopulation Optimization for Multitask Optimization

Abstract

Currently, the most of multitask evolutionary algorithms views multiple tasks as factors influencing the evolution of individuals. However, this consideration causes difficulty to assign fitness to individuals, because an individual which performs well on one task can have a bad performance on another task. To avoid this difficulty, this paper proposes a novel multipopulation technique for multitask optimization (MPMTO). The novelty of MPMTO is that it can solve the multiple tasks via a simple and straightforward method by corresponding each population to a task. By this way, the fitness assignment issue can be addressed by just assigning the objective value of the corresponding task to individuals. MPMTO is a general technique so that existing population-based optimization algorithms can be used in each population. This paper uses differential evolutionary algorithm in each population and develops a multipopulation multitask differential evolutionary optimization (mMTDE) based on the proposed multipopulation technique. mMTDE features that each population can use the other populations as the additional knowledge source to create an overlapping population, allowing the populations share information. By this way, the population can improve the efficacy and accuracy of solving multiple tasks. Moreover, the successful inter-task offspring can immigrate back to the corresponding population to fully utilize the inter-task knowledge. We have compared the proposed method with other state-of-the-art methods on benchmark multitask problems. The experimental results show the superiority of the proposed method which could utilizes efficiently the searching knowledge of multiple tasks.