multiobjective shape optimization with constraints based on estimation distribution algorithms and correlated information
A new approach based on Estimation Distribution Algorithms for constrained multiobjective shape optimization is proposed in this article. Pareto dominance and feasibility rules are used to handle constraints. The algorithm uses feasible and infeasible individuals to estimate the probability distribution of evolving designs. Additionally, correlation among problem design variables is used to improve exploration. The design objectives are: minimum weight and minimum nodal displacement. Also, the resulting structures must fulfill three design constraints: a) maximum permissible Von Misses stress, b)connectedness of the structure elements, and c) small holes are not allowed in the structure. The finite element method is used to evaluate the objective functions and stress constraint.
shape optimization correlated information moea constraint handling edas