Improved artificial bee colony algorithm for global optimization

• Published in: Information processing letters Vol. 111; no. 17; pp. 871 - 882
• Main Authors: Gao, Weifeng, Liu, Sanyang
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.09.2011; Elsevier; Elsevier Sequoia S.A
• Subjects: 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
• DOI: 10.1016/j.ipl.2011.06.002

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.