Result: Improved artificial bee colony algorithm for global optimization

Title:
Improved artificial bee colony algorithm for global optimization
Authors:
WEIFENG GAO, SANYANG LIU
Source:
Information processing letters. 111(17):871-882
Publisher Information:
Amsterdam: Elsevier, 2011.
Publication Year:
2011
Physical Description:
print, 29 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, 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, 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, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Divers, Miscellaneous, Algorithme, Algorithm, Algoritmo, Analyse algorithme, Algorithm analysis, Análisis algoritmo, Apprentissage, Learning, Aprendizaje, Benchmark, Benchmarks, Informatique théorique, Computer theory, Informática teórica, Loi probabilité, Probability distribution, Ley probabilidad, Méthode optimisation, Optimization method, Método optimización, Optimisation, Optimization, Optimización, Performance algorithme, Algorithm performance, Resultado algoritmo, Probabilité, Probability, Probabilidad, Résolution problème, Problem solving, Resolución problema, Système chaotique, Chaotic systems, Traitement information, Information processing, Procesamiento información, Vitesse convergence, Convergence speed, Velocidad convergencia, 34C28, 49XX, 65K10, 65Kxx, 68T05, 68T20, 68W20, 68Wxx, Randomized algorithm, Solution équation, Artificial bee colony algorithm, Initial population, Randomized algorithms, Search mechanism, Solution search equation
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Applied Mathematics, Xidian University, Xi'an 710071, China
ISSN:
0020-0190
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=24576298
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.24576298
Database:
PASCAL Archive

Further Information

The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter M, we propose two improved solution search equations, namely ABC/best/1 and ABC/rond/1. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability p to control the frequency of introducing ABC/rond/1 and ABC/best/1 and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms.