The week's pick
Reseach Article
Balaning Explorations with Exploitations in the Artificial Bee Colony Algorithm for Numerical Function Optimization
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 9 - Number 1 |
| Year of Publication: 2015 |
| Authors: Syeda Shabnam Hasan, Fareal Ahmed |
10.5120/ijais15-451364
|
Syeda Shabnam Hasan, Fareal Ahmed . Balaning Explorations with Exploitations in the Artificial Bee Colony Algorithm for Numerical Function Optimization. International Journal of Applied Information Systems. 9, 1 ( June 2015), 42-48. DOI=10.5120/ijais15-451364
Abstract
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 (ABC-BEE) makes attempts to dynamically balance the mutation step size with which the artificial bees explore the search space. Mutation with small step size produces small variations of existing solutions which is better for exploitations, while large mutation steps are likely to produce large variations that facilitate better explorations of the search space. ABC-BEE fosters both large and small mutation steps as well as adaptively controls the step lengths based on their effectiveness to produce better solutions. ABC-BEE has been evaluated and compared on a number of benchmark functions with the original ABC algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA). Results indicate that the proposed scheme facilitates more effective mutations and performs better optimization outperforming all the other algorithms in comparison.
References
- D. Karaboga, B. Basturk, "On the performance of artificial bee colony (ABC) algorithm", in Applied Soft Computing, vol. 8, no. 1, pp. 687-697, 2008.
- D. Karaboga, B. Basturk, "Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems", in Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing, June 18-21, 2007, Cancun, Mexico.
- B. Basturk, D. Karaboga, "An artificial bee colony (ABC) algorithm for numeric function optimization", in Proceedings of the IEEE Swarm Intelligence Symposium 2006, Indianapolis, Indiana, USA, 12–14 May 2006.
- D. Karaboga, "An idea based on honey bee swarm for numerical optimization", Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
- P. Lucic, D. Teodorovic, "Vehicle Routing Problem with Uncertain Demand at Nodes: The Bee System and Fuzzy Logic Approach", in Fuzzy Sets in Optimization, Editor J. L. Verdegay, Springer-Verlag, Berlin Heidelbelg, pp. 67-82, 2003.
- P. Lucic, D. Teodorovic, "Bee system: Modeling Combinatorial Optimization Transportation Engineering Problems by Swarm Intelligence", in Preprints of the TRISTAN IV Triennial Symposium on Transportation Analysis, Sao Miguel, Azores Islands, pp. 441-445,2001.
- S. Nakrani, C. Tovey, "On Honey Bees and Dynamic Allocation in an Internet Server Colony", Proceedings of 2nd International Workshop on the Mathematics and Algorithms of Social Insects, Atlanta, Georgia, USA, 2003.
- D. Teodorovic, M. Dell'Orco, "Bee Colony Optimization - A Cooperative Learning Approach to Complex Transportation Problems", in Advanced OR and AI Methods in Transportation, pp. 51-60, 2005.
- H. Drias, S. Sadeg, S. Yahi, "Cooperative Bees Swarm for Solving the Maximum Weighted Satisfiability Problem", IWAAN International Work Conference on Artificial and Natural Neural Networks, Barcelona, Spain, pp. 318-325, 2005.
- X. S. Yang, "Engineering Optimizations via Nature-Inspired Virtual Bee Algorithms", IWINAC2005, LNCS 3562, J. M. Yang and J. R. Alvarez (Eds. ), Springer-Verlag, Berlin Heidelberg, pp. 317-323, 2005.
- D. T. Pham, E. Kog, A. Ghanbarzadeh, S. Otri, S. Rahim, M. Zaidi, "The Bees Algorithm – A Novel Tool for Complex Optimization Problems", IPROMS 2006 Proceeding 2nd International Virtual Conference on Intelligent Production Machines and Systems, Oxford, Elsevier, 2006.
- D. T. Pham, E. Koc, A. Ghanbarzadeh, S. Otri, "Optimization of the Weights of Multi-Layered Perceptions Using the Bees Algorithm", in Proceedings of 5th International Symposium on Intelligent Manufacturing Systems, Sakarya, Turkey, pp. 38-46, 2006.
- H. F. Wedde, M. Faruq, Y. Zhan, "BeeHive: An Efficient Fault-Tolerant Routing Algorithm Inspired by Honey Bee Behavior", Ant Colony, Optimization and Swarm Intelligence, Eds. M. Dorigo, Lecture Notes in Computer Science 3172, Springer Berlin, pp. 83-94, 2004.
- D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Reading: Addison-Wesley Longman, 1989.
- M. Dorigo and T. Stützle. Ant Colony Optimization. MIT Press, Cambridge, 2004.
- R. Eberhart, Y. Shi and J. Kennedy. Swarm Intelligence. Morgan Kaufmann, San Francisco, 2001.
- J. H. Holland. Adaptation in Natural & Artificial Systems. University of Michigan Press, Ann Arbor, MI, 1975.
- M. Mathur, S. B. Karale, S. Priye, V. K. Jayaraman, B. D. Kulkarni, "Ant Colony Approach to Continuous Function Optimization". Ind. Eng. Chem. Res. 39(10), 2000, 3814-3822.
- G. Bilchev and I. C. Parmee, "The Ant Colony Metaphor for Searching Continuous Design Spaces", in Selected Papers from AISB Workshop on Evolutionary Computing, 1995, pp. 25-39.
- M. Dorigo, G. D. Caro, L. M. Gambardella, "Ant algorithms for Discrete optimization", Artificial Life, vol. 5, no. 2, pp. 137-172, 1999.
- D. Srinivasan, T. H. Seow, "Evolutionary Computation", CEC '03, 8–12 Dec. 2003, 4(2003), Canberra, Australia, pp. 2292–2297.
- V. Tereshko, A. Loengarov, "Collective Decision-Making in Honey Bee Foraging Dynamics", Comput. Inf. Sys. J. , vol. 9, no. 3, pp. 1–7, 2005.
- K. Benatchba, L. Admane, M. Koudil, "Using bees to solve a data-mining problem expressed as a max-sat one, artificial intelligence and knowledge engineering applications: a bio-inspired approach". in Proceedings of the First International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2005, Las Palmas, Canary Islands, Spain, 15–18, June 2005.
- L. P. Wong , M. Y. H. Low , C. S. Chong, "A Bee Colony Optimization Algorithm for Traveling Salesman Problem", Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS), pp. 818-823, May 13-15, 2008.
- A. Baykasoglu, L. Ozbakor and P. Tapkan, "Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem", chapter 8 in Swarm Intelligence, Focus on Ant and Particle Swarm Optimization, I-Tech Education and Publishing, Vienna, Austria, 2007, ISBN 978-3-902613-09-7.
- C. S. Chong, A. I. Sivakumar, M. Y. H. Low , K. L. Gay, "A bee colony optimization algorithm to job shop scheduling", Proceedings of the 38th conference on Winter simulation, December 03-06, 2006, Monterey, California.
- X. Yao, Y. Liu, and G. Lin, "Evolutionary Programming Made Faster", IEEE Transactions on Evolutionary Computation, Vol-3, No. 2, 1999.
Index Terms
Keywords