Estimation distribution algorithms on constrained optimization problems

Abstract Estimation distribution algorithm (EDA) is an evolution technique that uses sampling to generate the offspring. Most developed EDAs focus on solving the optimization problems which only have the constraints of variable boundaries. In this paper, EDAs are proposed for solving the constrained optimization problems (COPs) involving various types of constraints. In particular, a modified extreme elitism selection method is designed for EDAs to handle the constraints. This selection extrudes the role of some top best solutions to pull the mean vector of the Gaussian distribution towards these best solutions and makes EDAs form a primary evolutionary direction. The EDAs based on five different Gaussian distribution with this selection are evaluated using a set of benchmark functions and some engineering design problems. It is found that for solving these problems, the EDA that is based on a single multivariate Gaussian distribution model with the modified extreme elitism selection outperforms the other EDAs and some state-of-the-art techniques.

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