multi-objective optimization of hybrid laminated composites under a frequency constraint by using the particle swarm optimization

improving mechanical properties of composites based on strength-to-weight ratio has been gained much attention in various applications. having the most resistant and at the same time the lightest and the most economical structure is believed as an aim in these applications. these three factors are usually opposed to each other. so, applying optimization algorithms for improving mechanical properties of composites is very important. particle swarm optimization (pso) is used in balanced symmetric hybrid laminated composites for accessing the lowest weight and cost based on the first natural frequency. in this research, the objective function is a combination of the weight and cost which are both functions of the numbers and material of layers, while the natural frequency, in addition to the above factors, is a function of the fibers angle and the stacking sequenc, too. the results obtained from pso algorithm (including optimized stacking sequences and the number of plies reinforced by either glass or graphite fibers) are compared with obtained results from genetic algorithm (ga) and ant colony optimization (aco). the results confirme the advantages of hybrid composites and reveale that pso provide the same results and in some cases even better sequences relative to the mentioned algorithms. this algorithm is so useful and competitive with respect to other heuristic algorithms.