Pareto-based multi-objective estimation of distribution algorithm with Gaussian copulas and application in RFID network planning

A Pareto-based multi-objective estimation of distribution algorithm with Gaussian copulas is proposed and applied to RFID network planning. The algorithm employs Pareto-based approach and multivariate Gaussian copulas to construct probability distribution model. 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. An archive with maximum capacity is used to maintain the non-dominated solutions. The Pareto optimal solutions are selected from the archive on the basis of the diversity of the solutions, and the crowding-distance measure is used for the diversity measurement. The archive gets updated with the inclusion of the non-dominated solutions from the combined population and current archive, and the archive which exceeds the maximum capacity is cut using the diversity consideration. The proposed algorithm is applied to some benchmark and RFID network planning. The relative experimental results show that the algorithm has better performance and is effective.