• Study on hybrid PS-ACO algorit...

Result: Study on hybrid PS-ACO algorithm

Title:
Study on hybrid PS-ACO algorithm
Authors:
BING SHUANG, JIAPIN CHEN, ZHENBO LI
Source:
Neural computing & applications (Print). 20(1):64-73
Publisher Information:
London: Springer, 2011.
Publication Year:
2011
Physical Description:
print,
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Neurology, Neurologie, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Inférence paramétrique, Parametric inference, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme parallèle, Parallel algorithm, Algoritmo paralelo, Calcul neuronal, Neural computation, computación neuronal, Calcul parallèle, Parallel computation, Cálculo paralelo, Comportement, Behavior, Conducta, Convergence, Convergencia, Itération, Iteration, Iteracción, Mise à jour, Updating, Actualización, Méthode heuristique, Heuristic method, Método heurístico, Méthode optimisation, Optimization method, Método optimización, Optimisation, Optimization, Optimización, Performance algorithme, Algorithm performance, Resultado algoritmo, Problème commis voyageur, Travelling salesman problem, Problema viajante comercio, Robustesse estimateur, Estimator robustness, Robustez estimador, Réseau neuronal, Neural network, Red neuronal, 49XX, 62F07, 65K10, 65Kxx, Algorithme hybride, Analyse convergence
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
National Key Laboratory of Nano/Micro Fabrication Technology, Key laboratory for Thin Film and Microfabrication of Ministry of Education, Research Institute of Micro/Nano Science and Technology, Shanghai Jiao Tong University, Shanghai 200030, China
ISSN:
0941-0643
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=23836437
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics
Accession Number:
edscal.23836437
Database:
PASCAL Archive

Further Information

Ant colony optimization (ACO) algorithm is a recent meta-heuristic method inspired by the behavior of real ant colonies. The algorithm uses parallel computation mechanism and performs strong robustness, but it faces the limitations of stagnation and premature convergence. In this paper, a hybrid PS-ACO algorithm, ACO algorithm modified by particle swarm optimization (PSO) algorithm, is presented. The pheromone updating rules of ACO are combined with the local and global search mechanisms of PSO. On one hand, the search space is expanded by the local exploration ; on the other hand, the search process is directed by the global experience. The local and global search mechanisms are combined stochastically to balance the exploration and the exploitation, so that the search efficiency can be improved. The convergence analysis and parameters selection are given through simulations on traveling salesman problems (TSP). The results show that the hybrid PS-ACO algorithm has better convergence performance than genetic algorithm (GA), ACO and MMAS under the condition of limited evolution iterations.