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A. Suresh, A. Mohammed, "An Improved Genetic Algorithm for the Optimal Design of Large Trusses", in B.H.V. Topping, (Editor), "Advances in Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, pp 97-102, 1998. doi:10.4203/ccp.53.3.2

Abstract

An improved genetic algorithm is presented for the optimal design of large trusses. A method based on violations of normalised constraints with gradually increasing penalty parameters is used to convert the constrained optimisation problem into an unconstrained one. Advanced genetic operators such as two-point cross-over, niche, cloning and fitness scaling are used to obtain an improved genetic algorithm which is very efficient. The use of non-binary coding, which is useful especially in dealing with problems involving a large number of design variables, is also demonstrated. Numerical examples and comparison with known results from literature are presented to illustrate the efficiency and accuracy of the proposed method.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 53 ADVANCES IN ENGINEERING COMPUTATIONAL TECHNOLOGY Edited by: B.H.V. Topping Paper III.2 An Improved Genetic Algorithm for the Optimal Design of Large Trusses A. Suresh and A. Mohammed Department of Civil Engineering, Regional Engineering College, Calicut, India doi:10.4203/ccp.53.3.2 purchase the full-text of this paper Full Bibliographic Reference for this paper A. Suresh, A. Mohammed, "An Improved Genetic Algorithm for the Optimal Design of Large Trusses", in B.H.V. Topping, (Editor), "Advances in Engineering Computational Technology", Civil-Comp Press, Edinburgh, UK, pp 97-102, 1998. doi:10.4203/ccp.53.3.2 Abstract An improved genetic algorithm is presented for the optimal design of large trusses. A method based on violations of normalised constraints with gradually increasing penalty parameters is used to convert the constrained optimisation problem into an unconstrained one. Advanced genetic operators such as two-point cross-over, niche, cloning and fitness scaling are used to obtain an improved genetic algorithm which is very efficient. The use of non-binary coding, which is useful especially in dealing with problems involving a large number of design variables, is also demonstrated. Numerical examples and comparison with known results from literature are presented to illustrate the efficiency and accuracy of the proposed method. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £75 +P&P)
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