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 |
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| 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