Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A new hybrid ant colony optimization algorithm for the vehicle routing problem
Pattern Recognition Letters
A hybrid genetic - Particle Swarm Optimization Algorithm for the vehicle routing problem
Expert Systems with Applications: An International Journal
Hybrid particle swarm algorithm for grain logistics vehicle routing problem
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Particle swarm optimization with triggered mutation and its implementation based on GPU
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evaluation of vehicle routing problem with time windows by using metaheuristics algorithm
ACC'11/MMACTEE'11 Proceedings of the 13th IASME/WSEAS international conference on Mathematical Methods and Computational Techniques in Electrical Engineering conference on Applied Computing
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Vehicle routing problem with Time Window (VRPTW) has received much attention by researchers in solving many scheduling applications for transportation and logistics. The objective of VRPTW is to use a fleet of vehicles with specific capacity to serve a number of customers with various demands and time window constraints. As a non-polynomial (NP) hard problem, the VRPTW is complex and time consuming, especially when it involves a large number of customers and constraints. This paper presents a hybrid approach between Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for solving VRPTW. The reason for hybridization is to overcome the problem of premature convergence that exists in standard PSO. Premature convergence often yields partially optimized solutions because of particles stagnation. The proposed hybrid PSO implements a mechanism that automatically trigger swarm condition which will liberate particles from sub-optimal solutions hence enabling progress toward the maximum best solution. A computational experiment has been carried out by running the hybrid PSO with the VRPTW benchmark data set. The results indicate that the algorithm can produce some improvement when compared to the original PSO.