To resolve the conflict between convergence speed and diversity in Bat Algorithm (BA), we propose a novel improved BA algorithm called local enhanced catfish bat algorithm (LECBA). In LECBA, some inferior bats of initial population are reserved and each bat's historical worst position is updated. While population has been trapped into a local optimum, the initial inferior bats' positions and current bats' historical worst positions can attract population to leap out of the local optimums at a high speed through catfish effect which can improve population's diversity and preserve exploration ability. Furthermore, in each generation, the difference between the best and second-best global solutions is adopted to guide the best one to carry out a local search process called local learning behavior which can improve exploitation ability. The local learning behavior can be executed with population's evolution in parallel, and the local scale factor is dynamically adjusted during evolution. Experimental results show that LECBA has better global search ability and higher convergence speed than other modified bat algorithms.