Abstract
The current work discusses a novel global optimization method called the Multi-Agent Normal Sampling Technique (MANST). MANST is based on systematic sampling of points around agents; each agent in MANST represents a candidate solution of the problem. All agents compete with each other for a larger share of available resources. The performance of all agents is periodically evaluated and a specific number of agents who show no promising achievements are deleted; new agents are generated in the proximity of those promising agents. This process continues until the agents converge to the global optimum. MANST is a standalone global optimization technique. It is benchmarked with six well-known test cases and the results are then compared with those obtained from Matlab™ 7.1 GA Toolbox. The test results showed that MANST outperformed Matlab™ 7.1 GA Toolbox for the benchmark problems in terms of accuracy, number of function evaluations, and CPU time.
| Original language | English |
|---|---|
| Title of host publication | 2007 Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC2007 |
| Pages | 297-306 |
| Number of pages | 10 |
| Volume | 6 PART A |
| DOIs | |
| Publication status | Published - 2008 |
| Event | 33rd Design Automation Conference, presented at - 2007 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2007 - Las Vegas, NV, United States Duration: Sep 4 2007 → Sep 7 2007 |
Other
| Other | 33rd Design Automation Conference, presented at - 2007 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE2007 |
|---|---|
| Country | United States |
| City | Las Vegas, NV |
| Period | 9/4/07 → 9/7/07 |
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ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Science Applications
- Mechanical Engineering
- Modelling and Simulation
Cite this
Multi-agent Normal Sampling Technique (MANST) for global optimization. / Saremi, Alireza; Al-Hinai, Nasr; Wang, G. Gary; ElMekkawy, Tarek.
2007 Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC2007. Vol. 6 PART A 2008. p. 297-306.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multi-agent Normal Sampling Technique (MANST) for global optimization
AU - Saremi, Alireza
AU - Al-Hinai, Nasr
AU - Wang, G. Gary
AU - ElMekkawy, Tarek
PY - 2008
Y1 - 2008
N2 - The current work discusses a novel global optimization method called the Multi-Agent Normal Sampling Technique (MANST). MANST is based on systematic sampling of points around agents; each agent in MANST represents a candidate solution of the problem. All agents compete with each other for a larger share of available resources. The performance of all agents is periodically evaluated and a specific number of agents who show no promising achievements are deleted; new agents are generated in the proximity of those promising agents. This process continues until the agents converge to the global optimum. MANST is a standalone global optimization technique. It is benchmarked with six well-known test cases and the results are then compared with those obtained from Matlab™ 7.1 GA Toolbox. The test results showed that MANST outperformed Matlab™ 7.1 GA Toolbox for the benchmark problems in terms of accuracy, number of function evaluations, and CPU time.
AB - The current work discusses a novel global optimization method called the Multi-Agent Normal Sampling Technique (MANST). MANST is based on systematic sampling of points around agents; each agent in MANST represents a candidate solution of the problem. All agents compete with each other for a larger share of available resources. The performance of all agents is periodically evaluated and a specific number of agents who show no promising achievements are deleted; new agents are generated in the proximity of those promising agents. This process continues until the agents converge to the global optimum. MANST is a standalone global optimization technique. It is benchmarked with six well-known test cases and the results are then compared with those obtained from Matlab™ 7.1 GA Toolbox. The test results showed that MANST outperformed Matlab™ 7.1 GA Toolbox for the benchmark problems in terms of accuracy, number of function evaluations, and CPU time.
UR - http://www.scopus.com/inward/record.url?scp=44849110847&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=44849110847&partnerID=8YFLogxK
U2 - 10.1115/DETC2007-35506
DO - 10.1115/DETC2007-35506
M3 - Conference contribution
SN - 0791848027
SN - 9780791848029
SN - 0791848078
SN - 9780791848074
VL - 6 PART A
SP - 297
EP - 306
BT - 2007 Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC2007
ER -