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Comparison of stochastic search optimization algorithms for the laminated composites under mechanical and hygrothermal loadings
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The aim of the present study is to design the stacking sequence of the laminated composites that have low coefficient of thermal expansion and high elastic moduli. In design process, multi-objective genetic algorithm optimization of the carbon fiber laminated composite plates is verified by single objective optimization approach using three different stochastic optimization methods: genetic algorithm, generalized pattern search, and simulated annealing. However, both the multi- and single-objective approaches to laminate optimization have been used by considerably few authors. Simplified micromechanics equations, classical lamination theory, and MATLAB Symbolic Math toolbox are used to obtain the fitness functions of the optimization problems. Stress distributions of the optimized composites are presented through the thickness of the laminates subjected to mechanical, thermal, and hygral loadings.