Bat Algorithm (BA) is a simple and effective global optimization algorithm which has been applied to a wide range of real-world optimisation problems. Various extensions to Bat algorithm have been proposed in the past; prominent amongst them being ShBAT. ShBAT is a hybrid between BA and Shuffled Frog Leaping Algorithm-SFLA; a memetic algorithm based on food search behavior of frogs. ShBAT integrates the shuffling and reorganization technique of SFLA to enhance the exploitation capabilities of BAT. This paper proposes Enhanced Shuffled Bat algorithm (EShBAT) an extension to ShBAT. In ShBAT, different memeplexes evolve independently, with different cultures. EShBAT improves the exploitation capabilities of ShBAT by grouping together the best of each memeplex to form a super-memeplex. This super-memeplex evolves independently to further exploit the best solutions. The performance of EShBAT is verified over 30 well-known benchmark functions. Experimental results indicate a significant improvement of EShBAT over BA and ShBAT.