Journal of Global Optimization
Ant Colony Optimization
A Note on the Extended Rosenbrock Function
Evolutionary Computation
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem
Applied Soft Computing
Structural inverse analysis by hybrid simplex artificial bee colony algorithms
Computers and Structures
Chaotic bee colony algorithms for global numerical optimization
Expert Systems with Applications: An International Journal
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
A global best artificial bee colony algorithm for global optimization
Journal of Computational and Applied Mathematics
Enhanced combination modeling method for combustion efficiency in coal-fired boilers
Applied Soft Computing
Computers and Operations Research
An efficient and robust artificial bee colony algorithm for numerical optimization
Computers and Operations Research
Artificial bee colony algorithm and pattern search hybridized for global optimization
Applied Soft Computing
An artificial bee colony-least square algorithm for solving harmonic estimation problems
Applied Soft Computing
Adaptive artificial bee colony optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Balanced artificial bee colony algorithm
International Journal of Artificial Intelligence and Soft Computing
Enhancing different phases of artificial bee colony for continuous global optimisation problems
International Journal of Advanced Intelligence Paradigms
Artificial bee colony algorithm: a survey
International Journal of Advanced Intelligence Paradigms
A novel artificial bee colony algorithm with Powell's method
Applied Soft Computing
Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm
Computer Methods and Programs in Biomedicine
A hybrid metaheuristic for the cyclic antibandwidth problem
Knowledge-Based Systems
Survey A review of opposition-based learning from 2005 to 2012
Engineering Applications of Artificial Intelligence
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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.