The applicability and implementation of the discrete Big Bang-Big Crunch optimisation technique for discontinuous objective function in multi-material laminated composite pressure vessels - Ellul, Brian; Camilleri, Duncan (The International journal of pressure vessels and piping, 2018)
The Big Bang-Big Crunch (BB-BC) algorithm is implemented to optimise lay-up configurations of filament wound laminated cylindrical pressure vessels subject to internal pressure. The vessels are composed of a combination of two different types of lamina, a chopped strand mat and a direct roving lamina with different fibre orientations. These two types of layers are combined into one discrete design variable, creating a complex discontinuous objective function surface. Two optimisation cases are presented, in the first case, the inverse of the Tsai-Wu Strength Ratio is minimised, and in the second case, a cost factor is introduced to shift the global minimum to a singularity. To assess the performance of the BB-BC algorithm, the results of the optimisation runs are compared with the global optimum obtained by a brute-force method. In this study, an analysis of the effect of varying the population size on the efficiency and accuracy of the results, is presented. In addition, the influence of previous best fit candidate on the performance of the algorithm is also investigated. It is shown that the BB-BC algorithm is robust in optimising layup sequences even when the number of layers is high and when two different types of layers are present. In particular, it is shown that higher population sizes increases the accuracy of the results although it lowers the efficiency. In the presence of a singularity in the objective function, it is necessary that the new generations subject to further optimisation represent more the previous best fit candidate. •BB-BC is a robust algorithm to optimise multi-material laminated pressure vessels.•The BB-BC algorithm can be used even when the global optimum is a singularity.•Population size has a significant impact on the accuracy of the results obtained.•Repeated optimisation runs are required to identify premature convergence.