scholarly journals Adaptive Cat Swarm Optimization Algorithm and Its Applications in Vehicle Routing Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiao-Fang Ji ◽  
Jeng-Shyang Pan ◽  
Shu-Chuan Chu ◽  
Pei Hu ◽  
Qing-Wei Chai ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Hybrid Algorithm ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Cat Swarm Optimization ◽  
Fixed Dimension ◽  
Speed Up ◽  
Effectiveness And Efficiency ◽  
Experimental Findings

This paper proposes a novel hybrid algorithm named Adaptive Cat Swarm Optimization (ACSO). It combines the benefits of two swarm intelligence algorithms, CSO and APSO, and presents better search results. Firstly, some strategies are implemented to improve the performance of the proposed hybrid algorithm. The tracing radius of the cat group is limited, and the random number parameter r is adaptive adjusted. In addition, a scaling factor update method, called a memory factor y, is introduced into the proposed algorithm. They can be learnt very well so as to jump out of local optimums and speed up the global convergence. Secondly, by comparing the proposed algorithm with PSO, APSO, and CSO, 23 benchmark functions are verified by simulation experiments, which consists of unimodal, multimodal, and fixed-dimension multimodal. The results show the effectiveness and efficiency of the innovative hybrid algorithm. Lastly, the proposed ACSO is utilized to solve the Vehicle Routing Problem (VRP). Experimental findings also reveal the practicability of the ACSO through a comparison with certain existing methods.

A Hybrid Particle Swarm Optimization for Solving Vehicle Routing Problem with Stochastic Demands

2014 ◽  
Vol 971-973 ◽  
pp. 1467-1472 ◽  
Author(s):  
Ning Qiang ◽  
Feng Ju Kang
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Stochastic Demands ◽  
Hybrid Particle Swarm Optimization ◽  
Customer Demands

As one of the most popular supply chain management problems, the Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades, most of these studies focus on deterministic problem where the customer demands are known in advance. But the Vehicle Routing Problem with Stochastic Demands (VRPSD) has not received enough consideration. In the VRPSD, the vehicle does not know the customer demands until the vehicle arrive to them. This paper use a hybrid algorithm for solving VRPSD, the hybrid algorithm based on Particle Swarm Optimization (PSO) Algorithm, combines a Greedy Randomized Adaptive Search Procedure (GRASP) algorithm, and Variable Neighborhood Search (VNS) algorithm. A real number encoding method is designed to build a suitable mapping between solutions of problem and particles in PSO. A number of computational studies, along with comparisons with other existing algorithms, showed that the proposed hybrid algorithm is a feasible and effective approach for Vehicle Routing Problem with Stochastic Demands.

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.

Efficient Heuristics for Large-Scale Vehicle Routing Problems Using Particle Swarm Optimization

2012 ◽  
Vol 3 (2) ◽  
pp. 34-50
Author(s):  
A. Chandramouli ◽  
L. Vivek Srinivasan ◽  
T. T. Narendran
Keyword(s):  
Particle Swarm Optimization ◽  
Vehicle Routing ◽  
Large Scale ◽  
Particle Swarm ◽  
Computational Effort ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Customer Base ◽  
Benchmark Datasets ◽  
Problem Instances

This paper addresses the Capacitated Vehicle Routing Problem (CVRP) with a homogenous fleet of vehicles serving a large customer base. The authors propose a multi-phase heuristic that clusters the nodes based on proximity, orients them along a route, and allots vehicles. For the final phase of determining the routes for each vehicle, they have developed a Particle Swarm Optimization (PSO) approach. Benchmark datasets as well as hypothetical datasets have been used for computational trials. The proposed heuristic is found to perform exceedingly well even for large problem instances, both in terms of quality of solutions and in terms of computational effort.

Sign in / Sign up

Export Citation Format

Share Document