Gauss-Markov Measure Field Models for Low-Level Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
| Hi-index | 0.00 |
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.