An Adaptive Selection Evolutionary Algorithm for the Capacitated Vehicle Routing Problem

We propose a meta-heuristic based on an evolutionary approach for a Capacitated Vehicle Routing Problem. The modifications concern a selection process and two new heuristics for crossover operators. The numerical results demonstrate the effectiveness of an adaptive selection evolutionary algorithm on the benchmark test problems. The main advantage is the possibility of arranging the proposed selection process and crossover operators in the space of feasible solutions. The presented results are very promising for solving bigger problems. number of customers is large. The largest problems which can be consistently solved by the most effective exact algorithms proposed so far contain about 50 customers, whereas larger instances may be solved only in particular cases. So instances with hundreds of customers, as those arising in practical applications, may only be tackled with heuristic or meta- heuristic methods (12). In meta-heuristics, the emphasis is on performing a deep exploration of the most promising regions of the solution space. The quality of solutions produced by these methods is much better than that obtained by classical heuristic methods. In the paper special attention is paid to meta-heuristics methods with evolutionary mechanisms based on the idea of natural search in biology (5,8,12). Our purpose is to propose an evolutionary search based meta-heuristic idea with modified tournament selection process for CVRP. We will describe an adaptive tournament selection and two new crossover operators for the discussed routing problem.