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...

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...

The artificial bee colony algorithm (ABC), a population based algorithm, provides solutions with better accuracy compared to other competitive population based algorithms. However, it suffers from slow convergence speed. We suggest modifications in search strategy of ABC to improve overall performance and named this modified algorithm, Efficient ABC algorithm (EABC). The performance of EABC is ...

Artificial bee colony (ABC) algorithm has been introduced for solving numerical optimization problems, inspired collective behavior of honey bee colonies. ABC algorithm has three phases named as employed bee, onlooker bee and scout bee. In the model of ABC, only one design parameter of the optimization problem is updated by the artificial bees at the ABC phases by using interaction in the bees....

A robust image watermarking scheme based on singular value decomposition (SVD) and discrete wavelet transform (DWT) with Artificial Bee Colony Algorithm is proposed in this paper. Previous SVD based watermarking algorithms have a major drawback of false positive detection. For solving this problem, the similarity measure of U matrix for ownership is checked. To achieve the highest possible robu...

In this paper an improved version of the Artificial Bee Colony (ABC) algorithm adjusted for constrained optimization problems is presented. It has been implemented and tested on several engineering benchmarks which contain discrete and continuous variables. Our results were compared to the results obtained by Simple Constrained Particle Swarm optimization algorithm (SiC-PSO) which showed a very...

Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence algorithm that has attracted great deal of attention from researchers in recent years with the advantage of less control parameters and strong global optimization ability. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. This ...

There is a trend in the scientific community to model and solve complex optimization process by employing natural metaphors. In this area, Artificial Bee Colony optimization (ABC) tries to model natural behaviour of real honeybees in food foraging. ABC algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC is used for solving multivariabl...

In order to improve the convergence and diversity of multiobjective optimization algorithms, the harmonic average distance is employed to improve the aggregating function combined L-rank value. Selection model and searching scheme of artificial bee colony algorithm and diversity maintaining scheme are improved in this paper. This novel many objectives optimization method based on improved artif...