Optimizing frequencies of skew composite laminates with metaheuristic algorithms

Abstract

In this article, a high-fidelity structural optimization framework is developed by combining the high-accuracy of finite element method with iterative improvement capability of metaheuristic algorithms. Rotary inertia and transverse shear deformation are included in the finite element model by considering first-order shear deformation theory (FSDT). Three powerful nature-inspired metaheuristic algorithms viz. genetic algorithm (GA) in its classical form, a particle swarm optimization (PSO) variant and a cuckoo search (CS) variant are used. Advanced memetic attributes are incorporated in the PSO and CS to form their respective variants—repulsive particle swarm optimization with local search and chaotic perturbation (RPSOLC) and CHP co-evolutionary host–parasite (CHP). Extensive numerical simulations are carried out to validate these approaches by comparing with existing literature. A comprehensive set of benchmark solutions on certain new problems are also reported. Statistical tests and keen assessment of the predicted results show CHP comprehensively outperforms RPSOLC and GA, while RPSOLC has marginal superiority over GA.