SERIAL INTEGRATION OF PARTICLE SWARM AND ANT COLONY ALGORITHMS FOR STRUCTURAL OPTIMIZATION

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

GHOLIZADEH S.; FATTAHI F.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: ASIAN JOURNAL OF CIVIL ENGINEERING (BUILDING AND HOUSING) Year:2012 | Volume:13 | Issue:1 Page(s): 127-146

Download Full-Text

View:

303

Download:

175

Cites:

Information Journal Paper

Title

SERIAL INTEGRATION OF PARTICLE SWARM AND ANT COLONY ALGORITHMS FOR STRUCTURAL OPTIMIZATION

Author(s)

GHOLIZADEH S. | FATTAHI F. | Issue Writer Certificate

Keywords

META-HEURISTIC ALGORITHM

SIZE OPTIMIZATION

PARTICLE SWARM OPTIMIZATION

ANT COLONY OPTIMIZATION

EXTERIOR PENALTY FUNCTION

SEQUENTIAL UNCONSTRAINED MINIMIZATION TECHNIQUE

Abstract

The main objective of this study is to hybridize PARTICLE SWARM OPTIMIZATION (PSO) and ANT COLONY OPTIMIZATION (ACO) algorithms to propose an efficient algorithm for optimal designing of truss structures. Two types of serial integration of the algorithms are studied. In the first one, PSO is employed to explore the design space, while ACO is utilized to achieve a local search about the best solution found by PSO. This is denoted as serial particle swarm ant colony algorithm (SPSACA). In the second one, ACO works as the global optimizer while PSO acts as the local one. This is called as serial ant colony particle swarm algorithm (SACPSA). A number of structural optimization benchmark problems are solved by the proposed algorithms. Numerical results indicate that the SPSACA possesses better computational performance compared with the SACPSA and other existing algorithms.

Cites

No record.

References

Cite

APA: Copy

GHOLIZADEH, S., & FATTAHI, F.. (2012). SERIAL INTEGRATION OF PARTICLE SWARM AND ANT COLONY ALGORITHMS FOR STRUCTURAL OPTIMIZATION. ASIAN JOURNAL OF CIVIL ENGINEERING (BUILDING AND HOUSING), 13(1), 127-146. SID. https://sid.ir/paper/298905/en

Vancouver: Copy

GHOLIZADEH S., FATTAHI F.. SERIAL INTEGRATION OF PARTICLE SWARM AND ANT COLONY ALGORITHMS FOR STRUCTURAL OPTIMIZATION. ASIAN JOURNAL OF CIVIL ENGINEERING (BUILDING AND HOUSING)[Internet]. 2012;13(1):127-146. Available from: https://sid.ir/paper/298905/en

IEEE: Copy

S. GHOLIZADEH, and F. FATTAHI, “SERIAL INTEGRATION OF PARTICLE SWARM AND ANT COLONY ALGORITHMS FOR STRUCTURAL OPTIMIZATION,” ASIAN JOURNAL OF CIVIL ENGINEERING (BUILDING AND HOUSING), vol. 13, no. 1, pp. 127–146, 2012, [Online]. Available: https://sid.ir/paper/298905/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops