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...

A Modified Artificial Bee Colony (ABC) algorithm for Economic Dispatch (ED) problem has been proposed. The Artificial Bee Colony (ABC) algorithm which is inspired by the foraging behavior of honey bee swarm gives a solution procedure for solving economic dispatch problem. It provides solution more effective than Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimizati...

Feature selection plays an important role in data mining and pattern recognition, especially in the case of large scale data. Feature selection is done due to large amount of noise and irrelevant features in the original data set. Hence, the efficiency of learning algorithms will increase incredibly if these irrelevant data are removed by this procedure. A novel approach for feature selection i...

 Numerous problems in engineering and science can be converted into optimization problems. Artificial bee colony (ABC) algorithm is a newly developed stochastic optimization algorithm and has been widely used in many areas. However, due to the stochastic characteristics of its solution search equation, the traditional ABC algorithm often suffers from poor exploitation. Aiming at this weakness o...

Artificial bee colony algorithms belong to the paradigm of bio-inspired, population-based, algorithms that have been widely used to solve optimization problems. These algorithms use population of individuals/particles/bees/ants in order to explore a search space of potential solutions to a given problem and to be able to quickly converge to a global solution, or at least to a good solution. The...

The idea behind this work is to solve the Shortest Common Supersequence by using Artificial Bee Colony (ABC) Algorithm. This algorithm is considered as one of the newest nature-inspired swarm-based optimization algorithms and has a promising performance [6]. Shortest Common Supersequence is a classical problem in the field of strings and it is classified as NP-Hard problem [12]. Many algorithms...

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...

Artificial Neural Network (ANN) has found widespread application in the field of classification. Many domains have benefited with the use of ANN based models over traditional statistical models for their classification and prediction needs. Many techniques have been proposed to arrive at optimal values for parameters of the ANN model to improve its prediction accuracy. This paper compares the i...

Software test suite optimization is one of the most important issue in software testing as testing consumes a lot of time in executing redundant test cases. In this paper, we have proposed and implemented a new approach for test suite optimization, namely, Mutated Artificial Bee Colony. Artificial Bee colony algorithm combines local search carried out by employed and onlooker bees with global s...