Dynamic parameterization of the particle swarm optimization and genetic algorithm hybrids for vehicle routing problem with time window

2015 ◽  
Vol 12 (1) ◽  
pp. 13-25
Author(s):  
S. Masrom ◽  
Siti Z.Z. Abidin ◽  
N. Omar ◽  
K. Nasir ◽  
A.S. Abd Rahman
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Routing Problem

scholarly journals A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 21033-21053 ◽  
Author(s):  
Sheng-Hua Xu ◽  
Ji-Ping Liu ◽  
Fu-Hao Zhang ◽  
Liang Wang ◽  
Li-Jian Sun
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Routing Problem

Implementation of an H-PSOGA Optimization Model for Vehicle Routing Problem

2021 ◽  
Vol 12 (3) ◽  
pp. 148-162
Author(s):  
Justice Kojo Kangah ◽  
Justice Kwame Appati ◽  
Kwaku F. Darkwah ◽  
Michael Agbo Tettey Soli
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Population Based ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Routing Problem

This work presents an ensemble method which combines both the strengths and weakness of particle swarm optimization (PSO) with genetic algorithm (GA) operators like crossover and mutation to solve the vehicle routing problem. Given that particle swarm optimization and genetic algorithm are both population-based heuristic search evolutionary methods as used in many fields, the standard particle swarm optimization stagnates particles more quickly and converges prematurely to suboptimal solutions which are not guaranteed to be local optimum. Although both PSO and GA are approximation methods to an optimization problem, these algorithms have their limitations and benefits. In this study, modifications are made to the original algorithmic structure of PSO by updating it with some selected GA operators to implement a hybrid algorithm. A computational comparison and analysis of the results from the non-hybrid algorithm and the proposed hybrid algorithm on a MATLAB simulation environment tool show that the hybrid algorithm performs quite well as opposed to using only GA or PSO.

Study on Multi-Depot Vehicle Routing Problem Based on Cloud Adaptive Particle Swarm Optimization

2012 ◽  
Vol 253-255 ◽  
pp. 1369-1373
Author(s):  
Tie Jun Wang ◽  
Kai Jun Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Cloud Model ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Adaptive Particle Swarm Optimization ◽  
Searching Ability

Multi-depots vehicle routing problem (MDVRP) is a kind of NP combination problem which possesses important practical value. In order to overcome PSO’s premature convergence and slow astringe, a Cloud Adaptive Particle Swarm Optimization(CAPSO) is put forward, it uses the randomicity and stable tendentiousness characteristics of cloud model, adopts different inertia weight generating methods in different groups, the searching ability of the algorithm in local and overall situation is balanced effectively. In this paper, the algorithm is used to solve MDVRP, a kind of new particles coding method is constructed and the solution algorithm is developed. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability than GA and the PSO algorithm.

Sign in / Sign up

Export Citation Format

Share Document