ANT COLONY OPTIMIZATION ALGORITHM WITH SEQUENTIAL VARIABLE NEIGHBOURHOOD SEARCH CHANGE STEP IN THE WASTE COLLECTION SYSTEM (Jurnal Teknologi (Sciences & Engineering), 2025)
This study improves the exploration of ant colony optimization (ACO) ability by adapting it with the Sequential Variable Neighbourhood Search Change Step (SVNSCS) algorithm as post-improvements for solving the Capacitated Vehicle Routing Problem (CVRP) in solid waste collection methodology. The aim is to reduce the cost of waste collection by minimizing the route distance and the number of vehicles to serve all containers within the route. Technically, SVNSCS explores the search space depending on the information associated with the demands and coordinates of the nodes. Based on the result, the proposed algorithm demonstrates its superiority over the traditional ACO algorithm by having 66.7%, 81.81%, 62.5%, and 77.77% in terms of the best solution for four CVRP benchmark datasets of A, B, E, and P, respectively. Each dataset has different characteristics, such as the number of containers, vehicle capacity, objective function, weight of each container, and geographical distribution.