Enhanced Artificial Bee Colony Algorithm for Travelling Salesman Problem using Crossover and Mutation

Abstract

warm intelligence systems are made up of a population of simple agents interacting locally with each another and with their environment. Artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE) etc, are some example of swarm intelligence. In this work, an efficient modified version of ABC algorithm is proposed, where two additional operator crossover and mutation operator is used in the ABC algorithm. Here Crossover operator is used after the employed bee phase and mutation operator is used after scout bee phase of ABC algorithm. Proposed algorithm is applied at standard travelling salesman problem (TSP) for checking the efficiency of proposed algorithm and also simulated results are compared with ABC with uniform mutation algorithm and Basic ABC algorithm. The simulated result showed that the proposed algorithm is better than all the modified version of ABC algorithm. KEYWORDSBee Colony, crossover, Mutation, Genetic Algorithm, Travelling salesman problem.