A Double-Distribution Statistical Algorithm for Composite Laminate Optimization

The paper proposes a new evolutionary algorithm for composite laminate optimization, named Double-Distribution Optimization Algorithm (DDOA). The DDOA belongs to the family of Estimation of Distributions Algorithms (EDA) that build a statistical model of promising regions of the design space based on sets of good points, and use it to guide the search. A generic framework for introducing variable dependencies by making use of the physics of the problem is presented. The algorithm uses two distributions simultaneously: a simple distribution for the design variables, complemented by the distribution of auxiliary variables. The combination of the two generates complex distributions at a low computational cost. The paper demonstrates DDOA’s efficiency for two laminate optimization problems for which the design variables are the fiber angles and the auxiliary variables are the lamination parameters. The results show that its reliability in finding the optima is greater than that of a simple EDA and of a standard GA, and that its superiority increases with the problem dimension.