UTF:Upgrade Transfer Function, Feature Selection by UTF-BPSO

This folder contains an implementation for the feature selection problem using UTF-BPSO

In the real world, many optimization problems are discrete and very complex to solve. Some of them are in the class of NP-hard problems and their search spaces grow exponentially with the problem size. As a result, an exhaustive search will be impractical using exact algorithms. In the last decades, meta-heuristic algorithms as approximate algorithms have shown superior performance in solving these problems. The majority of these algorithms have been designed for continuous search spaces and are not able to solve binary optimization problems. Therefore, a transfer function is applied to convert the continuous search space to the binary one. The performance of such binary algorithms depends on their ability of exploration, exploitation and transfer function. Several transfer functions have been introduced so far but they have shown poor exploration and exploitation in solving some problems. UTF (Upgrade Transfer Function) is a novel adaptive transfer function, based on two linear functions and adapts itself during running the algorithm to switch from exploration to exploitation. This capability also covers disadvantages of metaheuristic algorithms in terms of poor exploration and exploitation. The UTF is applied in particle swarm optimization to convert the continuous search space to the binary one. The algorithm named UTF-BPSO is used for feature selection problem.