Artificial Bee Colony Algorithm and Bat Algorithm for Solving Travel Salesman Problem

Abstract

There are many algorithms for optimization meta heuristic have developed on swarm intelligence-based that depending in the nature of design. One of them, Bat Algorithm (BA), is based on the "echolocation behaviors" of bats. Micro bat used echolocation to specify the prey, avoid obstacles and locate their roosting crevices in the dark. Another algorithm is Artificial Bee Colony (ABC) is a new optimization algorithm which depend on the "bee behavior" towards colony to search about the food. In this paper, presents the BA and ABC algorithms steps for solving TSP then try to search about best solution depending on the parameters for both algorithms. The results show the ABC is best performance than BA for finding the best tour quickly compared with other by consuming time lesser than by effecting on the convergence speed for searching the solution. Furthermore, The BA required more parameters to achieve each output efficiently and need using improved control strategy to balance between exploitation and exploration that consuming more time for it.