Improved artificial bee colony algorithm for global optimization

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, na...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Information processing letters Ročník 111; číslo 17; s. 871 - 882
Hlavní autoři: Gao, Weifeng, Liu, Sanyang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 15.09.2011
Elsevier
Elsevier Sequoia S.A
Témata:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Artificial bee colony algorithm
Calculus of variations and optimal control
Colonies
Computer science; control theory; systems
Convergence
Evolution
Exact sciences and technology
Initial population
Learning
Mathematical analysis
Mathematical models
Mathematics
Miscellaneous
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Numerical methods in optimization and calculus of variations
Optimization
Optimization algorithms
Optimization techniques
Probability distribution
Randomized algorithms
Sciences and techniques of general use
Search mechanism
Searching
Solution search equation
Studies
Theoretical computing
Randomized algorithms
Artificial bee colony algorithm
Solution search equation
Search mechanism
Initial population
Computer theory
Optimization method
Benchmarks
Probability
Probability distribution
Algorithm
Optimization
Convergence speed
Learning
Algorithm performance
Information processing
Problem solving
Algorithm analysis
Chaotic systems
ISSN:0020-0190, 1872-6119
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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/rand/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/rand/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. ► “ ABC/best/1” and “ ABC/rand/1” are proposed. ► A new search mechanism is got by introducing a selective probability p. ► Both opposition-based learning method and chaotic maps are employed. ► The experiment results demonstrate the good performance of the IABC algorithm.
Bibliografie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:0020-0190
1872-6119
DOI:10.1016/j.ipl.2011.06.002