Applying Artificial Bee Colony Algorithm to Interactive Evolutionary Computation

Abstract

In this study, we apply an artificial bee colony (ABC) algorithm to the interactive evolutionary computation (IEC) method for the multimodal retrieval of candidate solutions. Previous works have proposed IEC systems using a parallel interactive tabu search algorithm (PITS) that generates multiple tabu search (TS) retrievals and a hybrid genetic algorithm (GA) involving a global retrieval method and a TS involving a local retrieval method for multimodal retrieval. However, the PITS cannot efficiently retrieve candidate solutions and it has a complicated algorithm. The hybrid GA–TS also finds it hard to retrieve candidate solutions if the user has a more multimodal preference. We propose herein an IEC method with the ABC algorithm for the multimodal and simultaneous retrieval of candidate solutions. We perform a numerical simulation with a pseudo user that imitates multimodal preferences as target candidate solutions instead of a real user. The results show that the proposed method can retrieve multimodal candidate solutions in conditions with limited numbers of candidate solutions and bees.