Paper Info
Title
Multi-Constrained Routing Based On Particle Swarm Optimization And Fireworks Algorithm
Abstract
This paper sets up a mathematical model that satisfies the multiconstrained routing optimization problem. By adding a penalty, multiple constraints are mapped to a fitness that satisfies multiple constraints. Then, it uses a heuristic routing algorithm based on particle swarm optimization (PSO) to perform heuristic routing search. Introducing the fireworks algorithm (FWA) based on the PSO search algorithm, our algorithm searches the optimal solution more quickly. Besides, it reduces the defect of PSO falling into the local optimum. Simulation shows the algorithm can effectively solve the multiconstrained routing problem in large-scale networks. While searching for optimal solutions, the success rate of the algorithm is about 5.21% higher than that of the standard PSO algorithm. That is improved by using the ant colony algorithm. The PSO-ACO algorithm is about 2.57% higher than the problem. The average cost of the final search is about 4.36% higher than that of the standard PSO algorithm. It is about 1.34% higher than the PSO-ACO algorithm improved by the ant colony algorithm.
Year
DOI
Venue
2018
10.1109/IECON.2018.8592907
IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
Keywords
Field
DocType
multiple constraints routing, Quality of Service, penalty function, particle swarm optimization algorithm, fireworks algorithm
Ant colony optimization algorithms,Particle swarm optimization,Mathematical optimization,Search algorithm,Control theory,Local optimum,Average cost,Engineering,Optimization problem,Heuristic routing,Penalty method
Conference
ISSN
Citations 
PageRank 
1553-572X
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Youbing Hu100.34
Kun Wang215045.41
Jinjiang Wan300.34
kaidong wang452.33
Xia Hu52411110.07