Multi-population Genetic Algorithm for Rich Vehicle Routing Problems - Agany Manyiel, Joseph Mabor (IRC, 2020)
Genetic Algorithm (GA) is the widely adopted meta-heuristic method for solving Rich Vehicle Routing Problem (RVRP) due to its ability to find optimal solution even for medium to large-scale problem in a reasonable time. However, genetic algorithm is stochastic in nature and does not guarantee optimal solution in an application all the time, a problem referred to as premature convergence in literature. In this paper we present Multi-population Genetic Algorithm for Rich Vehicle Routing Problems (MPGA-RVRP) to provide diversity and delay premature convergence in GA by making use of multiple populations that share potential solutions among each other and evolve independently optimising only one objective. MPGA-RVRP is applied in RVRP with three objectives:- total route distance, total route duration and total route cost. Results from the experiments show that MPGA-RVRP performs better compared to benchmark, Multi-objective Genetic Algorithm (MOGA). A web-based logistic system has also been developed as use case for MPGA-RVRP.