An Improved Hybrid Bat Algorithm for Traveling Salesman Problem

Abstract

A new metaheuristic, bat algorithm, inspired by echolocation characteristics of micro-bats has been extensively applied to solve various continuous optimization problems. Numerous intelligent techniques are hybridized with bat algorithm to optimize its performance. However, there are only two discrete variants have been proposed to tune the basic bat algorithm to handle combinatorial optimization problems. However, both of them suffer from the inherited drawbacks of the bat algorithm such as slow speed convergence and easy stuck at local optimal. Motivated by this, an improved hybrid variant of discrete bat algorithm, called IHDBA is proposed and applied to solve traveling salesman problem. IHDBA achieves a good balance between intensification and diversification by adding the evolutionary operators, crossover and mutation, which allow performance of both local and global search. In addition, 2-opt and 3-opt local search techniques are introduced to improve searching performance and speed up the convergence. Using extensive evaluations based on TSP benchmark instances taken from TSPLIB, the results show that IHDBA outperforms state-of-the-art discrete bat algorithm i.e. IBA in the most of instances with respect to average and best solutions.