Bat Algorithm (BA) is a nature-inspired swarm algorithm which has been applied to solve multiple real-world optimization problems. Due to a lack of balance between exploitation and exploration, multiple researchers have proposed different... more
Bat Algorithm (BA) is a nature-inspired swarm algorithm which has been applied to solve multiple real-world optimization problems. Due to a lack of balance between exploitation and exploration, multiple researchers have proposed different hybrids of BA. This paper proposes Shuffled Multi-Population Bat algorithm (SMPBat), a hybrid between two recently proposed variants of BA:-EShBAT and BatRM-S. BatRM-S – Bat algorithm with Ring Master-Slave strategy, is a multi-population variant of BA which partitions it's population according to a combination of the ring and master-slave strategies. EShBAT – Enhanced Shuffled Bat algorithm, incorporates shuffling into BA. SMPBat combines the population partitioning strategies of these two algorithms to enhance the exploitation and exploitation 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, n 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.
