Opposition-Based Learning Estimation of Distribution Algorithm with Gaussian Copulas and Its Application to Placement of RFID Readers

Estimation of distribution algorithms are a class of optimization algorithms based on probability distribution model. In this paper, we propose an improved estimation of distribution algorithm using opposition-based learning and Gaussian copulas. The improved algorithm employs multivariate Gaussian copulas to construct probability distribution model and uses opposition-based learning for population initialization and new population generation. By estimating Kendall’s tau and using the relationship of Kendall’s tau and correlation matrix, Gaussian copula parameters are firstly estimated, thus, joint distribution is estimated. Afterwards, the Monte Carte simulation is used to generate new individuals. Then, the opposite numbers have also been utilized to improve the convergence performances. The improved algorithm is applied to some benchmark functions and optimal placement of readers in RFID networks. The relative experimental results show that the improved algorithm has better performance than original version of estimation of distribution algorithm and is effective in the optimal placement of readers in RFID networks.