A Neighbourhood Search for Artificial Bee Colony in Vehicle Routing Problem with Time Windows

Abstract

Artificial bee colony (ABC) is one of the widely used swarm intelligence algorithms in solving combinatorial optimization problems. In this study, the existing Modified ABC (MABC) algorithm is revised to solve the vehicle routing problem with time windows (VRPTW). The reason is that even though MABC is reported to be successful, its exploitation process lacks a selection neighborhood structure during the intensification process. The proposed algorithm, termed as enhanced Modified ABC (E-MABC), offers a neighborhood search that exchanges neighborhood structure between two different routes in the same solution, rather than one route. To evaluate the effectiveness of E-MABC, experiments are performed on 56 instances of VRPTW. The results of E-MABC are compared against the ones produced using MABC and other metaheuristic algorithms. Based on the total travel distance and number of vehicles, the proposed E-MABC is shown to be a solution for VRPTW. The interchange neighborhood search, which is implemented by bees during the exploitation process, improves the solution quality, hence producing an optimal outcome. The proposed E-MABC results are better as compared to MABC in terms of the total travel distance by 71.42 % and the number of vehicles by 35.71%.