Search

Copyright of Applied Mathematics & Mechanics (1000-0887) is the property of Applied Mathematics & Mechanics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The computation consumption of finite element analysis for structural optimization design of holding poles is large, and it is difficult to determine the feasible region. The response surface method (RSM) was used to simulate the real response of the holding pole, and an improved arithmetic optimization algorithm (IAOA) was proposed to optimize the holding pole. The fractional-order calculus was introduced into the arithmetic optimization algorithm (AOA) to improve the exploitation ability of the AOA. The Latin hypercube sampling was applied to select the test samples of each member of the holding pole, and the least square method was employed to analyze the sample points. Then, the 2nd-order response surface surrogate model for the stress and displacement of the holding pole on the cross-sectional sizes of each member was established. An optimization model was constructed with the minimum mass as the optimization objective and the allowable stress and displacement as constraints, and the IAOA was implemented to solve the model. The results show that, the 2nd-order response surface model can accurately predict the response value of the holding pole. The solution accuracy of the IAOA is significantly improved. The surrogate model can greatly decrease the calculation cost of the finite element analysis. The mass of the holding pole is reduced by 8.2% after optimization. The RSM and the IAOA can be combined to solve the optimization design problem of large spatial truss structures effectively. [ABSTRACT FROM AUTHOR]

Ａｎ ｉｍｐｒｏｖｅｄ ｃｕｃｋｏｏ ｓｅａｒｃｈ （ＩＣＳ） ａｌｇｏｒｉｔｈｍ ｗａｓ ｄｅｖｅｌｏｐｅｄ ｔｏ ｉｄｅｎｔｉｆｙ ｔｈｅ ｔｈｅｒｍａｌ ｄｉｆｆｕｓｉｏｎ ｃｏｅｆｆｉｃｉｅｎｔｓ ｆｏｒ ｉｎｖｅｒｓｅ ｔｒａｎｓｉｅｎｔ ｈｅａｔ ｃｏｎｄｕｃｔｉｏｎ ｐｒｏｂｌｅｍｓ ｗｉｔｈ ｈｅａｔ ｓｏｕｒｃｅｓ． Ｔｈｅ ｈｅａｔ ｃｏｎｄｕｃｔｉｏｎ ｐｒｏｂｌｅｍ ｗｉｔｈ ｈｅａｔ ｓｏｕｒｃｅ ｗａｓ ｔｒａｎｓｆｏｒｍｅｄ ｉｎｔｏ ｏｎｅ ｗｉｔｈｏｕｔ ｈｅａｔ ｓｏｕｒｃｅ． Ｔｈｅ ｄｉｒｅｃｔ ｐｒｏｂｌｅｍ ｗａｓ ｓｏｌｖｅｄ ｗｉｔｈ ｔｈｅ ｂｏｕｎｄａｒｙ ｅｌｅｍｅｎｔ ｍｅｔｈｏｄ． Ｔｈｅ ｔｈｅｒｍａｌ ｄｉｆｆｕｓｉｏｎ ｃｏｅｆｆｉｃｉｅｎｔ ｗａｓ ｔｒｅａｔｅｄ ａｓ ｔｈｅ ｏｐｔｉｍｉｚａｔｉｏｎ ｖａｒｉａｂｌｅ， ａｎｄ ｔｈｅ ｄｉｆｆｅｒｅｎｃｅ ｂｅｔｗｅｅｎ ｔｈｅ ｃａｌｃｕｌａｔｅｄ ｔｅｍｐｅｒａｔｕｒｅ ａｎｄ ｔｈｅ ｍｅａｓｕｒｅｄ ｔｅｍｐｅｒａｔｕｒｅ ｗａｓ ｔａｋｅｎ ａｓ ｔｈｅ ｏｂｊｅｃｔｉｖｅ ｆｕｎｃｔｉｏｎ． Ｔｈｅ ｔｈｅｒｍａｌ ｄｉｆｆｕｓｉｏｎ ｃｏｅｆｆｉｃｉｅｎｔ ｗａｓ ｏｐｔｉｍｉｚｅｄ ｔｈｒｏｕｇｈ ｍｉｎｉｍｉｚａｔｉｏｎ ｏｆ ｔｈｅ ｏｂｊｅｃｔｉｖｅ ｆｕｎｃｔｉｏｎ ｗｉｔｈ ｔｈｅ ＩＣＳ ａｌｇｏｒｉｔｈｍ． Ｃｏｍｐａｒｉｓｏｎ ｂｅｔｗｅｅｎ ｔｈｅ ｒｅｓｕｌｔｓ ｏｆ ｔｈｅ ｃｏｎｊｕｇａｔｅ ｇｒａｄｉｅｎｔ ｍｅｔｈｏｄ （ＣＧＭ）， ｔｈｅ ｃｕｃｋｏｏ ｓｅａｒｃｈ （ＣＳ） ａｌ⁃ ｇｏｒｉｔｈｍ ａｎｄ ｔｈｅ ＩＣＳ ａｌｇｏｒｉｔｈｍ ｉｎｄｉｃａｔｅｓ ｔｈａｔ ｔｈｅ ＩＣＳ ａｌｇｏｒｉｔｈｍ ｉｓ ｌｅｓｓ ｓｅｎｓｉｔｉｖｅ ｔｏ ｉｔｅｒａｔｉｖｅ ｉｎｉｔｉａｌｉｚａｔｉｏｎ ｔｈａｎ ｔｈｅ ＣＧＭ， ａｎｄ ｔｈｅ ＩＣＳ ａｌｇｏｒｉｔｈｍ ｈａｓ ｈｉｇｈｅｒ ｅｆｆｉｃｉｅｎｔ ｃｏｎｖｅｒｇｅｎｃｅ ｔｈａｎ ｔｈｅ ＣＳ ａｌｇｏｒｉｔｈｍ． Ｔｈｅ ｎｕｍｅｒｉｃａｌ ｅｘａｍｐｌｅｓ ｗｅｒｅ ｄｅｖｏｔｅｄ ｔｏ ｔｈｅ ｉｎｆｌｕｅｎｃｅｓ ｏｆ ｔｈｅ ｍｅａｓｕｒｅｄ ｐｏｉｎｔ ｎｕｍｂｅｒ， ｔｈｅ ｎｅｓｔ ｎｕｍｂｅｒ ａｎｄ ｔｈｅ ｍｅａｓｕｒｅｄ ｎｏｉｓｅ． Ｔｈｅ ｒｅｓｕｌｔ ａｃｃｕｒａｃｙ ｄｅｃｒｅａｓｅｓ ｗｉｔｈ ｔｈｅ ｍｅａｓｕｒｅｄ ｐｏｉｎｔ ｎｕｍｂｅｒ， ａｎｄ ｔｈｅ ｉｔｅｒａｔｉｏｎ ｎｕｍｂｅｒ ｄｅｃｒｅａｓｅｓ ｗｉｔｈ ｔｈｅ ｎｅｓｔ ｎｕｍｂｅｒ． Ｍｏｒｅｏｖｅｒ， ｔｈｅ ｈｉｇｈｅｒ ｔｈｅ ｍｅａｓｕｒｅｄ ｎｏｉｓｅ ｇｏｅｓ， ｔｈｅ ｌｏｗｅｒ ｔｈｅ ｒｅｓｕｌｔ ａｃｃｕｒａｃｙ ｗｉｌｌ ｂｅ． Ｔｈｅ ｒｅｓｕｌｔｓ ｓｈｏｗ ｔｈａｔ ｔｈｅ ＩＣＳ ａｌｇｏｒｉｔｈｍ ｉｓ ａｃｃｕｒａｔｅ ａｎｄ ｅｆｆｉｃｉｅｎｔ ｆｏｒ ｔｈｅ ｉｄｅｎｔｉｆｉｃａｔｉｏｎ ｏｆ ｔｈｅｒｍａｌ ｄｉｆｆｕｓｉｏｎ ｃｏｅｆｆｉｃｉｅｎｔｓ. [ABSTRACT FROM AUTHOR]