We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimizat...

This paper proposes a γ-Artificial Bee Colony – Feature Selection (γ-ABC-FS) approach to identify the salient feature subset that improves classification accuracy. γ-ABC-FS hybridizes Artificial Colony(ABC) algorithm by employing Rough Set Theory concepts in ABC search process and improved initialization local strategies. The proposed is intended begin evolutionary not excluding attributes cont...

In recent years large number of algorithms based on the swarm intelligence has been proposed by various researchers. The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behavior of honey bees. In short span of time, ABC algorithm has gain wide popularity among researchers due to its simplicity...

Kernel fuzzy C-mean clustering (KFCM) algorithm is effective for high-dimensional data, but this algorithm has some defects of sensitivity to initialization and local optima. Artificial Bee Colony (ABC) algorithm is based on intelligent behaviors of honey bee swarm. It has the properties of strong global optimization and fast convergence speed. A KFCM algorithm based on improved ABC is proposed...

158 Abstract—This paper presents an investigation of selection strategies upon the Artificial Bee Colony (ABC) algorithm in examination timetabling problems. ABC is a global stochastic optimisation algorithm that is based on the behavior of honey bee swarms. Onlooker bees in ABC algorithm choose food source based on the proportional selection strategy. In this paper, three selection strategies ...

The minimum cross entropy thresholding (MCET) has been widely applied in image processing. In this paper, a new multilevel MCET algorithm based on the artificial bee colony (ABC) algorithm is proposed. The proposed thresholding algorithm is called ABC-based MCET algorithm. Four different methods including the exhaustive search, the honey bee mating optimization (HBMO), the particle swarm optimi...

The Artificial Bee Colony Algorithm (ABC) is a heuristic optimization method based on the foraging behavior of honey bees. It has been confirmed that this algorithm has good ability to search for the global optimum, but it suffers from the fact that the global best solution is not directly used, but the ABC stores it at each iteration, unlike the particle swarm optimization (PSO) that can direc...

This paper discusses an approach to generate test data for path coverage based testing using Genetic Algorithms, Differential Evolution and Artificial Bee Colony optimization algorithms. Control flow graph and cyclomatic complexity of the example program has been used to find out the number of feasible paths present in the program and it is compared with the actual no of paths covered by the ev...