Solving Capacitated Vehicle Routing Problem Using Meerkat Clan Algorithm

Abstract

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