A new formulation and solution of probabilistic constrained load flow (PCLF) problem
suitable for modern power systems with wind power generation and electric vehicles (EV) …

The algorithm of Continuous Point Location with Adaptive d-ary Search (CPL-AdS) strategy
exhibits its efficiency in solving stochastic point location (SPL) problems. However, there is …

Learning Automaton (LA) is an adaptive self-organized model that improves its action-
selection through interaction with an unknown environment. LA with finite action set can be …

This paper considers the stochastic point location (SPL) problem as a learning mechanism
trying to locate a point on a real line via interacting with a random environment. Compared to …

Learning automata (LA), a powerful tool for reinforcement learning in the field of machine
learning, could explore its optimal state by continuously interacting with an external …

Unlike the field of tutorial systems, where a real-life student interacts and learns from a
software system, our research focuses on a new philosophy in which no entity needs to be a …

Learning Automaton (LA), a branch of reinforcement learning, initially began with the Fixed
Structure Learning Automaton (FSLA) family and was later expanded to include the Variable …

Submodular function maximization is a typical combinatorial optimization problem that
arises in many areas of computer science, such as data summarization, batch mode active …

Wireless data broadcasting is an efficient, bandwidth preserving way of data dissemination.
However, as the amount of data increases, the waiting time of the clients becomes …

One of the difficult challenges facing data miners is that algorithm performance degrades if
the feature space contains redundant or irrelevant features. Therefore, as a critical …