Bat Algorithm (BA) is a nature-inspired swarm algorithm which has been applied to solve multiple real-world optimisation problems. Due to a lack of balance between exploitation and exploration, multiple researchers ha...
详细信息
ISBN:
(纸本)9781509063673
Bat Algorithm (BA) is a nature-inspired swarm algorithm which has been applied to solve multiple real-world optimisation problems. Due to a lack of balance between exploitation and exploration, multiple researchers have proposed different hybrids of BA. This paper proposes Shuffled multipopulation Bat algorithm (SMPBat), a hybrid between two recently proposed variants of BA:- Enhanced Shuffled Bat algorithm (EShBAT) and Bat algorithm with Ring Master-Slave strategy (BatRM-S). BatRM-S is a multi-population variant of BA which partitions it's population according to a combination of the ring and master-slave strategies. EShBAT incorporates shuffling into BA. The proposed algorithm, SMPBat combines the population partitioning strategies of these two algorithms to enhance the exploitation and exploration capabilities of BA. The evolution strategy of SMPBat also strives to retain the improved solutions. The standard BA replaces a solution with a new solution around the best. However, in this process, the information gained by that solution so far is completely lost. SMPBat changes this exploitation technique used by BA. SMPBat is compared to BatRM-S, EShBAT and BA, over 30 well-known optimisation functions. Results establish SMPBat as a significant improvement over BA, EShBAT and BatRM-S.
暂无评论