Artificial bee colony optimization algorithm is one of the popular swarm intelligence technique anticipated by D. Karaboga in year 2005. Since its inception, this algorithm was modified by a number of researchers and applied in different areas of engineering, science and management to solve very complex problems. This algorithm is very simple to implement and has the least number of control par...

This paper presents an artificial bee colony (ABC) algorithm for structural optimization of planar and space trusses under stress, displacement and buckling constraints. In order to improve the performance of the classic ABC algorithm, modifications in neighborhood searching method, onlooker phase, and scout phase are proposed. Optimization of different typical truss structures is performed usi...

Hybrid Differential Artificial Bee Colony Algorithm Ajith Abraham1 ∗, Ravi Kumar Jatoth2, and A. Rajasekhar3 1Machine Intelligent Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, USA 2Department of Electronics and Communication Engineering, National Institute of Technology Warangal, India 3Department of Electrical and Electronics Engineering, National Institu...

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...

Artificial Bee Colony (ABC) is one of the most recent nature inspired (NIA) algorithms based on swarming metaphor. Proposed by Karaboga in 2005, ABC has proven to be a robust and efficient algorithm for solving global optimization problems over continuous space. However, it has been observed that the structure of ABC is such that it supports exploration more in comparison to exploitation. In or...

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...

http://dx.doi.org/10.1016/j.ins.2014.04.013 0020-0255/ 2014 Elsevier Inc. All rights reserved. ⇑ Corresponding author. Tel.: +86 0791 88126661; fax: +86 0791 88126660. E-mail addresses: [email protected] (H. Wang), [email protected] (Z. Wu), [email protected] (S. Rahn [email protected] (H. Sun), [email protected] (Y. Liu), [email protected] (J.-s. Pan). Hui Wang a,⇑, Zhijian ...

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....

Artificial Bee Colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over test problems as well as real world optimization problems. This paper presents an attempt to modify ABC to make it less susceptible to stick at local optima and...

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...