Solving Capacitated Vehicle Routing Problem Using Meerkat Clan Algorithm

[摘要] Capacitated Vehicle Routing Problem (CVRP) can be defined as one of the optimization problems where customersare allocated to vehicles to minimize the combined travel distances regarding all vehicles while serving customers. From themany CVRP approaches, clustering or grouping customers into possible individual vehicles' routes and identifyingtheir optimal routes effectively. Sweep is considered a well-studied clustering algorithm to group customers, while variousTraveling Salesman Problem (TSP) solving approaches are mainly applied to generate optimal individual vehicle routes. TheMeerkat Clan Algorithm (MCA) can be defined as a swarm intelligence algorithm derived from carefulobservations regarding Meerkat (Suricata suricatta) in southern Africa's the Kalahari Desert. The animal demonstratestactical organizational skills, excellent intelligence, and significant directional cleverness when searching for food in thedesert. In comparison to the other swarm intelligence, MCA was suggested for solving optimization problems via reaching theoptimal solution effects. MCA demonstrates its ability to resolve CVRP. It divides the solutions into subgroups based onmeerkat behavior, providing a wide range of options for finding the best solution. Compared to present swarm intelligencealgorithms for resolving CVRP, it was demonstrated that the size of the solved issues can be increased by using the algorithmsuggested in this work.

[学科分类] 计算机科学（综合）

[关键词] Capacitated vehicle routing problem;ant colony optimization;genetic algorithm;meerkat clan algorithm;sweepclustering