Composite laminate design with strength and fatigue life optimization using Elite–Scout Genetic Algorithm (ESGA)

Genetic algorithm (GA)-based optimization is a common approach to deal with problems that have a large solution space. Composite laminate design is one such problem due to the substantial number of candidates that arise from the selection and stacking of lamina. Many authors have successfully demonstrated GA-based approach in stacking sequence optimization. A few of them extending it to as far as selecting the candidate lamina for the candidate laminate. However, a vast majority of the literature is limited to optimizing a single parameter such as Margin of Safety, Buckling Load Factor, etc., while neglecting other parameters or assigning ill-suited weights to obtain a unified fitness function. In this paper, we will be discussing a novel Elite–Scout Genetic Algorithm (ESGA) for multi-objective optimization which will result in the Pareto-optimal solution(s). The algorithm is based on the first multiple elitist selection method, integrated with the concept of a scout operator. ESGA incorporates a population dependent growing crossover rate, and decaying mutation rate. The growth and decay are modeled along the lines of the cooling schedule as found in simulated annealing (SA). This will ensure greater flexibility of the algorithm by accommodating better exploration of the solution space without deterring the convergence of the solution.