A review on probabilistic graphical models in evolutionary computation

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms

Optimal relay placement in microgrids considering critical clearing time

This paper is concerned with the optimal placement of protection devices in a microgrid using particle swarm optimization algorithm. One of the main advantages of Distributed Generation (DG) scheme and microgrids in modern distribution systems is the reduction of number of outages and the associated damages caused by them. This task is accomplished by supplying a feeder from multiple sources. In order to prevent generator instability in DGs connected to utility, it is necessary to improve the protective schemes of traditional distribution systems and also to use proper relaying and setting for DGs. All of the downstream overcurrent (OC) relays of each DG are coordinated together and also should be coordinated with OC relay that is installed on the Point of Common Coupling (PCC) which is set at Critical Clearing Time (CCT) as a definite time, to have a desirable performance on each outage. In this paper, by the use of graph theory, various branches of a feeder are identified and the constraints for using particle swarm optimization algorithm to optimize the location of protective equipment are derived. In the proposed algorithm, the location, type and direction of relays are optimized simultaneously