LAUSR

Abstract The main emphasis of this paper is placed on the effectiveness of the proposed optimization method in material identification. The primary motivation of integrating GA, ACO and PSO is to minimize each other’s weaknesses and to promote respective strengths. In the proposed algorithm, the effect of random initialization of GA is subdued by passing the products of GA through the ACO and PSO operators to well organize the exploitative and exploratory search coverage. In return, GA improves the convergence rate and alleviates the strong dependency on the pheromone array in ACO as well as resolves the conflict arisen in identifying the trade-off parameter and further refine the exploitative search of PSO with the introduction of two-point standard mutation and one-point refined mutation. The proposed algorithm has been verified and applied in composite material identification with absolute percentage errors between measured and evaluated natural frequencies not more than 2%.