The Swarm Intelligence Optimization Algorithm and its Application

Li Shan Li

doi:10.4028/www.scientific.net/AMR.711.659

Paper Titles

A Computer Aided System to Discriminate Enhanced Colon Images by Three Data Mining Methods
p.636

New Digital Predistortion Technique with RLS Method for Wide-Band OFDM Signals
p.641

Research on Cloud Computing Industrial Cluster Innovation Evaluation Model
p.647

3D Reconstruction of Solid Rocket Motor Based on CT Images
p.652

The Swarm Intelligence Optimization Algorithm and its Application
p.659

Laboratory Management Based on Internet of Things
p.665

Human Identification Using One Lead ECG Signal
p.670

Research on the Cost Sharing of Customer Collaborative Product Innovation
p.677

Designing Economic Inspection Sampling Procedure with Bayesian Approach
p.683

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 711The Swarm Intelligence Optimization Algorithm and...

The Swarm Intelligence Optimization Algorithm and its Application

Abstract:

In the article, three kinds of swarm intelligence optimization algorithm are discussed including the ant colony optimization (ACO) algorithm, the particle swarm optimization (PSO) algorithm and the shuffled frog leaping algorithm (SFLA). The principle, development and application of each algorithm is introduced. Finally, an example of TSP is used to test the performance of ACO.

You might also be interested in these eBooks

Manufacture Engineering, Quality and Production System II

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 711)

Pages:

659-664

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.711.659

Citation:

Cite this paper

Online since:

June 2013

Authors:

Li Shan Li

Keywords:

Ant Colony Optimization Algorithm (ACOA), Particle Swarm Optimization (PSO) Algorithm, Shuffled Frog Leaping Algorithm (SFLA), Swarm Intelligence Optimization Algorithm

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] A. Colorni, M. Dorigo et V. Maniezzo, Distributed Optimization by Ant Colonies, actes de la première conférence européenne sur la vie artificielle, Paris, France, Elsevier Publishing, 134-142, 1991.

Google Scholar

[2] M. Dorigo, Optimization, Learning and Natural Algorithms, PhD thesis, Politecnico di Milano, Italie, 1992.

Google Scholar

[3] TSAIC F,TSAI C W, TSENG C C. A New Hybrid Heuristic Approach for Solving Large Traveling Salesman Problem.Information Sciences, 2004, 166: 67-81.

DOI: 10.1016/j.ins.2003.11.008

Google Scholar

[4] TALBIE G,ROUXB O, FONLUPTC, eta.l Parallel Ant Colonies for the Quadratic Assignment Problem. Future Gener-ation ComputerSystems, 2001, 17: 441-449.

Google Scholar

[5] COLORNI A, DORIGO M, et a.l Ant Systems for Job-shop Scheduling. Belgian Journal ofOperationsResearch, Sta-tistics and ComputerScience, 1994, 34: 39-53.

Google Scholar

[6] MAZZEO S,LOISEAU I. An Ant Colony Algorithm for the Capacitated Vehicle Routing. Electronic Notes in Discrete Mathematics, 2004, 18: 181-186.

DOI: 10.1016/j.endm.2004.06.029

Google Scholar

[7] KENNEDY J, EBERHART R C. Particle Swarm Optimization. Proceedings of the IEEE Conference on Neural Networks, IV. Piscataway,NJ, 1995: 1942-1948.

Google Scholar

[8] DONG Y,TANG JF,XU B D, et al. An Application of Swarm Optimization to Nonlinear Programming. Computer and MathmaticswithApplications, 2005, 49: 1655-1668.

Google Scholar

[9] ALLAHVERDIA,AL-ANZI F S.A PSO and a Tabu Search Heuristics for the Assembly Scheduling Problem of the Two-stage Distributed Database Application. Computer & Oper-ationsResearch, 2006, 33: 1056-1080.

DOI: 10.1016/j.cor.2004.09.002

Google Scholar

[10] SWARUP K S,KUMAR P R.A New Evolutionary Computation Technique for Economic Dispatch with Security Constraints. Electrical Power and Energy Systems, 2006, 28:273-283.

DOI: 10.1016/j.ijepes.2006.01.001

Google Scholar

[11] SOUSA T, SILVAA,NEVESA. Particle Swarm Based on Data Mining Algorithms for Classification Tasks. Parallel Computing, 2004, 30: 767-783.

DOI: 10.1016/j.parco.2003.12.015

Google Scholar

[12] EUSUFFM M, LANSEY K E. Optimization of Water Distribution Network Design Using Shuffled Frog Leaping Algorithm. Journal of Water Resources Planning and Management, 2003, 129(3): 210-225.

DOI: 10.1061/(asce)0733-9496(2003)129:3(210)

Google Scholar

[13] EUSUFFMM.Water Resources Decision Making Using Meta-Heuristic Optimization Methods. Tucson: University of Arizona, 2004.

Google Scholar