Improved Genetic Algorithm with Two-Level Approximation Method for Laminate Stacking Sequence Optimization by Considering Engineering Requirements

Abstract

Laminated composites have been widely applied in aerospace structures; thus optimization of the corresponding stacking sequences is indispensable. Genetic algorithms have been popularly adopted to cope with the design of stacking sequences which is a combinatorial optimization problem with complicated manufacturing constraints, but they often exhibit high computational costs with many structural analyses. A genetic algorithm using a two-level approximation (GATLA) method was proposed previously by the authors to obtain the optimal stacking sequences, which requires significantly low computational costs. By considering practical engineering requirements, this method possesses low applicability in complicated structures with multiple laminates. What is more, it has relatively high dependence on some genetic algorithm control parameters. To address these problems, now we propose an improved GA with two-level approximation (IGATLA) method which includes improved random initial design, adaptive penalty fitness function, adaptive crossover probability, and variable mutation probability, as well as enhanced validity check criterion for multiple laminates. The efficiency and feasibility of these improvements are verified with numerical applications, including typical numerical examples and industrial applications. It is shown that this method is also able to handle large, real world, industrial analysis models with high efficiency.