A modified artificial bee colony algorithm

• Published in: Computers & operations research Vol. 39; no. 3; pp. 687 - 697
• Main Authors: Gao, Wei-feng, Liu, San-yang
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2012; Elsevier; Pergamon Press Inc
• Subjects: Algorithmics. Computability. Computer arithmetics; Algorithms; Applied sciences; Artificial bee colony algorithm; Artificial intelligence; Chaos theory; Colonies; Computer science; control theory; systems; Differential evolution; Exact sciences and technology; Exploitation; Exploration; Initial population; Iterative methods; Mathematical analysis; Mathematical models; Operations research; Optimization; Optimization algorithms; Optimization techniques; Probability; Searching; Solution search equation; Studies; Theoretical computing; Differential evolution; Artificial bee colony algorithm; Solution search equation; Initial population; Chaos; Initialization; Probabilistic approach; Evolutionary algorithm; Competitiveness; Modeling; Global solution; Optimization; Swarm intelligence; Numerical convergence; Mathematical programming
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2011.06.007

Artificial bee colony algorithm (ABC) is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. Inspired by differential evolution (DE), we propose an improved solution search equation, which is based on that the bee searches only around the best solution of the previous iteration to improve the exploitation. Then, in order to make full use of and balance the exploration of the solution search equation of ABC and the exploitation of the proposed solution search equation, we introduce a selective probability P and get the new search mechanism. In addition, to enhance the global convergence, when producing the initial population, both chaotic systems and opposition-based learning methods are employed. The new search mechanism together with the proposed initialization makes up the modified ABC (MABC for short), which excludes the probabilistic selection scheme and scout bee phase. Experiments are conducted on a set of 28 benchmark functions. The results demonstrate good performance of MABC in solving complex numerical optimization problems when compared with two ABC-based algorithms.