Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Journal of Global Optimization
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Journal of Global Optimization
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Solving large scale optimization problems by opposition-based differential evolution (ODE)
WSEAS Transactions on Computers
Chaotic bee swarm optimization algorithm for path planning of mobile robots
EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
Scheduling jobs on computational grid using differential evolution algorithm
ICNVS'10 Proceedings of the 12th international conference on Networking, VLSI and signal processing
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Artificial bee colony (ABC) algorithm is a simple but powerful swarm intelligence optimization algorithm which was successfully applied to a number of problems. In this paper we propose a new approach for extending ABC algorithm based on five mutation strategies "borrowed" from differential evolution (DE) algorithm in order to improve the exploitation process. We compared five different strategies with original ABC algorithm on standard benchmark functions for various numbers of problem variables. The experimental results show that the modified ABC algorithms are effective and outperform the original algorithms in most cases.