Abstract
Many optimization methods and practical softwares have been developing for many years and most of them are very effective, especially to solve practical problems. But, non linearity of objective functions and constraint functions, which have frequently seen in practical problems, has caused a difficult situation for optimization. This difficulty mainly lies in the existence of several local optimum solutions. In this study, we have proposed a new global optimization methodology that provides an information exchange mechanism in the nearest neighbour method. We have developed a simple software system, which treated each design point in optimization as an agent. Many agents can search the optima simultaneously exchanging the their information. We have defined two roles of the agents. Local search agents have roles on searching local optima by an existing method like the steepest decent method and so on. Stochastic search agents investigate the design space by making use of the information from other agents. Through simple and several structural optimization problems, we have confirmed the advantages of this method.
| Original language | English |
|---|---|
| Pages (from-to) | 3227-3235 |
| Number of pages | 9 |
| Journal | Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C |
| Volume | 67 |
| Issue number | 662 |
| DOIs | |
| Publication status | Published - 2001 |
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Keywords
- Artifical Intelligence
- Computer Aided Design
- Knowledge Engineering
- Multi Agents System
- Optimum Design
- Structural Analysis
ASJC Scopus subject areas
- Mechanics of Materials
- Mechanical Engineering
- Industrial and Manufacturing Engineering
Cite this
A Study on the Optimum Design Using Search Agents. / Miyashita, Tomoyuki; Yamakawa, Hiroshi.
In: Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C, Vol. 67, No. 662, 2001, p. 3227-3235.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A Study on the Optimum Design Using Search Agents
AU - Miyashita, Tomoyuki
AU - Yamakawa, Hiroshi
PY - 2001
Y1 - 2001
N2 - Many optimization methods and practical softwares have been developing for many years and most of them are very effective, especially to solve practical problems. But, non linearity of objective functions and constraint functions, which have frequently seen in practical problems, has caused a difficult situation for optimization. This difficulty mainly lies in the existence of several local optimum solutions. In this study, we have proposed a new global optimization methodology that provides an information exchange mechanism in the nearest neighbour method. We have developed a simple software system, which treated each design point in optimization as an agent. Many agents can search the optima simultaneously exchanging the their information. We have defined two roles of the agents. Local search agents have roles on searching local optima by an existing method like the steepest decent method and so on. Stochastic search agents investigate the design space by making use of the information from other agents. Through simple and several structural optimization problems, we have confirmed the advantages of this method.
AB - Many optimization methods and practical softwares have been developing for many years and most of them are very effective, especially to solve practical problems. But, non linearity of objective functions and constraint functions, which have frequently seen in practical problems, has caused a difficult situation for optimization. This difficulty mainly lies in the existence of several local optimum solutions. In this study, we have proposed a new global optimization methodology that provides an information exchange mechanism in the nearest neighbour method. We have developed a simple software system, which treated each design point in optimization as an agent. Many agents can search the optima simultaneously exchanging the their information. We have defined two roles of the agents. Local search agents have roles on searching local optima by an existing method like the steepest decent method and so on. Stochastic search agents investigate the design space by making use of the information from other agents. Through simple and several structural optimization problems, we have confirmed the advantages of this method.
KW - Artifical Intelligence
KW - Computer Aided Design
KW - Knowledge Engineering
KW - Multi Agents System
KW - Optimum Design
KW - Structural Analysis
UR - http://www.scopus.com/inward/record.url?scp=85024446438&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85024446438&partnerID=8YFLogxK
U2 - 10.1299/kikaic.67.3227
DO - 10.1299/kikaic.67.3227
M3 - Article
AN - SCOPUS:85024446438
VL - 67
SP - 3227
EP - 3235
JO - Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C
JF - Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C
SN - 0387-5024
IS - 662
ER -