Combination of Adaptive Enlargement and Reduction in the Search Neighbourhood in the Bees Algorithm

A.S. Azfanizam; D.T. Pham; A.A. Faieza

doi:10.4028/www.scientific.net/AMM.564.614

Paper Titles

Function-Based Conceptual Design Expert (CDE) Systems: Development Trend and Gaps Identification
p.590

Reactive Scheduling of a Decentralized Multi-Objective Material Transportation Tasks in a Dynamic Job Shop Manufacturing System
p.597

Communication System Improvement for Air Traffic Service to Provide a Continuous Surveillance over the Oceanic Area
p.604

Higher Desirability in Solving Multiple Response Optimization Problems with Committee Machine
p.608

Combination of Adaptive Enlargement and Reduction in the Search Neighbourhood in the Bees Algorithm
p.614

A Framework of Knowledge Based System for Integrated Maintenance Strategy and Operation
p.619

A Review of the Critical Success Factors in the Adoption of Lean Production System by Small and Medium Sized Enterprises
p.627

A Survey on Lean Manufacturing Practices in Malaysian Food and Beverages Industry
p.632

Alternative Numerical Validation Methodology for Short-Term Development Projects
p.638

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 564Combination of Adaptive Enlargement and Reduction...

Combination of Adaptive Enlargement and Reduction in the Search Neighbourhood in the Bees Algorithm

Abstract:

The Bees Algorithm, a heuristic optimisation procedure that mimics bees foraging behaviour, is becoming more popular among swarm intelligence researchers. The algorithm involves neighbourhood and global search and is able to find promising solutions to complex multimodal optimisation problems. The purpose of neighbourhood search is to intensify the search effort around promising solutions, while global search is to enable avoidance of local optima. Despite numerous studies aimed at enhancing the Bees Algorithm, there have not been many attempts at studying neighbourhood search. In this work, the combination of adaptive enlargement and reduction of the search neighbourhood is presented. Two engineering design problems with constraints which were the pressure vessel and speed reducer were selected to demonstrate the performance of the modified algorithm. The experimental results obtained showed that this combination is beneficial to the proposed algorithm.

You might also be interested in these eBooks

Advances in Mechanical and Manufacturing Engineering

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 564)

Pages:

614-618

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.564.614

Citation:

Cite this paper

Online since:

June 2014

Authors:

A.S. Azfanizam*, D.T. Pham, A.A. Faieza

Keywords:

Adaptive Enlargement, Bees Algorithm, Search Neighbourhood

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] D. E. Goldberg, in: Genetic Algorithms in Search, Optimization, and Machine Learning, by Addison-Wesley Publishing Company, Inc., Massachusetts, USA (1989), in press.

Google Scholar

[2] R. Eberhart and J. Kennedy, in: New optimizer using particle swarm theory. Proceedings of the 1995 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan (1995), p.39.

DOI: 10.1109/mhs.1995.494215

Google Scholar

[3] M. Dorigo, V. Maniezzo and A. Colorni: Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 26(1) (1996), p.29.

DOI: 10.1109/3477.484436

Google Scholar

[4] D. T. Pham, A. Ghanbarzadeh, E. Koç, S. Otri, S. Rahim and M. Zaidi, in: The Bees Algorithm - A Novel Tool for Complex Optimisation Problems. Proceedings 2nd International Virtual Conference on Intelligent Production Machines and Systems (IPROMS)(2006).

DOI: 10.1016/b978-008045157-2/50081-x

Google Scholar

[5] A. Ghanbarzadeh: The Bees Algorithm: A Novel Optimisation Tool. Cardiff University (2007).

Google Scholar

[6] L. C. Cagnina,S. C. Esquivel and C. A. CoelloCoello: Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica (Ljubljana) 32(3)(2008), p.319.

Google Scholar

[7] B. Akay and D. Karaboga: Artificial bee colony algorithm for large-scale problems and engineering design optimization. Journal of Intelligent Manufacturing 23(4)(2010), p.1.

DOI: 10.1007/s10845-010-0393-4

Google Scholar

[8] S. He,E. Prempainand Q. H. Wu: An improved particle swarm optimizer for mechanical design optimization problems. Engineering Optimization 36(5)(2004), p.585.

DOI: 10.1080/03052150410001704854

Google Scholar