NDLI: Optimization of laminated composite plates for maximum fundamental frequency using Elitist-Genetic algorithm and finite strip method

Content Provider	Springer Nature Link
Author	Sadr, M. H. Ghashochi Bargh, H.
Copyright Year	2011
Abstract	In the present paper, fundamental frequency optimization of symmetrically laminated composite plates is studied using the combination of Elitist-Genetic algorithm (E-GA) and finite strip method (FSM). The design variables are the number of layers, the fiber orientation angles, edge conditions and plate length/width ratios. The classical laminated plate theory is used to calculate the natural frequencies and the fitness function is computed with a semi-analytical finite strip method which has been developed on the basis of full energy methods. To improve the speed of the optimization process, the elitist strategy is used in the Genetic algorithm. The performance of the E-GA is also compared with the simple genetic algorithm and shows the good efficiency of the E-GA algorithm. A multi-objective optimization strategy for optimal stacking sequence of laminated box structure is also presented, with respect to the first natural frequency and critical buckling load, using the weighted summation method to demonstrate the effectiveness of the E-GA. Results are corroborated by comparing with other optimum solutions available in the literature, wherever possible.
Starting Page	707
Ending Page	728
Page Count	22
File Format	PDF
ISSN	09255001
Journal	Journal of Global Optimization
Volume Number	54
Issue Number	4
e-ISSN	15732916
Language	English
Publisher	Springer US
Publisher Date	2011-09-24
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Composite plates Elitist-Genetic algorithm Finite strip Optimization Real Functions Operations Research/Decision Theory Computer Science
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Applied Mathematics Management Science and Operations Research Business, Management and Accounting Computer Science Applications

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