Multi-objective optimal structural design of composite superstructure using a novel MONMPSO algorithm - Gholami, Meghdad; Fathi, Alireza; Baghestani, Ali Mohammad (International journal of mechanical sciences, 2021)
•A novel Multi-Objective Niching Memetic Particle Swarm Optimization (MONMPSO) algorithm is proposed for solving constrained multi-objective optimization problems.•Multi-objective optimal structural design of composite sandwich panel is done by considering:üObjective functions: the weight and the cost.üOptimization parameters: the materials of fiber, matrix and core, the laminate arrangements and construction, the amount of fiber reinforcement, the thickness of the core and the thicknesses of the lamina, laminate and panel.üLoads: out of plane pressure and buckling load.üThe base theory : first-order shear deformation laminated plate theory (FSDT).•Pareto front-optimal solutions are obtained for various problem cases. As an application of composite science in the marine industry, the present paper deals with the multi-objective optimal structural design of a superstructure composite sandwich panel based on the first-order shear deformation laminated plate theory (FSDT). Several parameters including the type of fiber, matrix and core material, the amount of reinforcement, the core, lamina and laminate thickness, the laminate arrangement (stacking sequence) and the laminate construction are considered as the design parameters. A novel Multi-Objective Niching Memetic Particle Swarm Optimization (MONMPSO) algorithm is proposed and its performance is evaluated using the well-known non-dominated sorting genetic algorithm (NSGA-II). The results show that the proposed MONMPSO algorithm has a better performance in comparison to the NSGA-II algorithm in extracting the Pareto front pattern. Based on the numerical results, many useful structural rules for designing a composite sandwich panel under the out of plane pressure and buckling load have been deduced. [Display omitted]