Gao, Weifeng; Liu, Sanyang Improved artificial bee colony algorithm for global optimization. (English) Zbl 1260.68457 Inf. Process. Lett. 111, No. 17, 871-882 (2011). Summary: 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. Cited in 18 Documents MSC: 68W20 Randomized algorithms 90C26 Nonconvex programming, global optimization 68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) Keywords:randomized algorithms; artificial bee colony algorithm; initial population; solution search equation; search mechanism Software:JADE; ABC PDFBibTeX XMLCite \textit{W. Gao} and \textit{S. Liu}, Inf. Process. Lett. 111, No. 17, 871--882 (2011; Zbl 1260.68457) Full Text: DOI References: [1] Tang, K. S.; Man, K. F.; Kwong, S.; He, Q., Genetic algorithms and their applications, IEEE Signal Process. Mag., 13, 22-37 (1996) [2] J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proc. IEEE Congr. Evol. Comput. Australia, 1995, pp. 1942-1948.; J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proc. 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