The performance of Neural Networks (NN) depends on network structure, activation function and suitable weight values. For finding optimal weight values, freshly, computer scientists show the interest in the study of social insect’s behavior learning algorithms. Chief among these are, Ant Colony Optimzation (ACO), Artificial Bee Colony (ABC) algorithm, Hybrid Ant Bee Colony (HABC) algorithm and ...

Artificial Bee Colony algorithm is a global optimization algorithm which is motivated by the foraging behavior of honey bee swarms. Basic Artificial Bee Colony algorithm (ABC) has the advantages of strong robustness, fast convergence and high flexibility, fewer setting parameters, but it has the disadvantages premature convergence in the later search period and the accuracy of the optimal value...

Artificial Neural Networks (ANN) performance depends on network topology, activation function, behaviors of data, suitable synapse's values and learning algorithms. Researchers used different learning algorithms to train ANN for getting high performance. Artificial Bee Colony (ABC) algorithm is one of the latest successfully Swarm Intelligence based technique for training Multilayer Perceptron ...

This paper introduces a variant of Artificial Bee Colony algorithm and compares its results with a number of swarm intelligence and population based optimization algorithms. The Artificial Bee Colony (ABC) is an optimization algorithm based on the intelligent food foraging behavior of honey bees. The proposed variant, Artificial Bee Colony Algorithm with Balanced Explorations and Exploitations ...

Artificial bee colony (ABC) algorithm, one of the swarm intelligence algorithms, has been proposed for continuous optimization, inspired intelligent behaviors of real honey bee colony. For the optimization problems having binary structured solution space, the basic ABC algorithm should be modified because its basic version is proposed for solving continuous optimization problems. In this study,...

In this paper, a novel hybrid optimization algorithm, which combines a firefly algorithms and Artificial Bee Colony algorithm (FFAABC), is proposed for solving the economic power dispatch (EPD) problem. The hybrid algorithm is structured with two stages. The first stage uses the search by firefly algorithm (FFA) and second stage is a search with Artificial Bee Colony algorithm (ABC). The hybrid...

Job Scheduling plays an important role for efficient utilization of grid resources available across different domains and geographical zones. Scheduling of jobs is challenging and NPcomplete. Evolutionary / Swarm Intelligence algorithms have been extensively used to address the NP problem in grid scheduling. Artificial Bee Colony (ABC) has been proposed for optimization problems based on foragi...

Swarm intelligence is a research field that models the collective intelligence in swarms of insects or animals. Many algorithms that simulates these models have been proposed in order to solve a wide range of problems. The Artificial Bee Colony algorithm is one of the most recent swarm intelligence based algorithms which simulates the foraging behaviour of honey bee colonies. In this work, modi...

This paper presents a new approach to feature selection and classifcation based on support vector machine and hybrid artificial bee colony. The approach consists of two stages. At the first stage, this paper presented a hybrid artificial bee colony-based classifier model that combines artificial bee colony to improve classification accuracy with the most superior model parameter and features we...