Discrete hybrid PSO algorithm for design of laminate composites with multiple objectives

Abstract

This article presents a multi-objective discrete hybrid adaptive swarm intelligence algorithm for combinatorial optimization and applied for design optimization of fiber-reinforced composite structures. An approach is presented in this article to integrate a Pareto dominance concept into the adaptive PSO algorithm developed earlier by the first author in order to handle multi-objective optimization problems. In fact, a hybrid version of the adaptive PSO algorithm is now proposed in this article for multi-objective optimization by integrating with discrete as well as continuous neighborhood search algorithms. Further, an external archive technique is also integrated in order to collect the historical Pareto optimal solutions. The design constraints are handled in this article by treating them as additional objectives. Numerical studies have been carried out through the optimization of a hybrid fiber-reinforced composite plate, laminate composite cylindrical shell, and also a pressure vessel problem with varied number of design objectives. Standard performance metrics are employed to evaluate the performance of the proposed multi-objective hybrid PSO algorithm with both discrete as well as continuous neighborhood search algorithms. Studies clearly favor hybrid PSO with variable-depth neighborhood search algorithm. Finally, comparisons have also been made with other popular evolutionary algorithms like NSGA-II, PAES, Micro-GA, and SPEA2 to demonstrate the effectiveness of the proposed hybrid multi-objective PSO algorithm.