Paper Title
Development of Binary Moth Flame Optimization Variants Via S-Shape and V-Shape Transfer Functions and Their Performance Analysis on Wind Turbine Placement Problem

Abstract
The world's energy demand is increasing every year. Fossil fuels such as coal and gasoline are mostly used to meet this demand. This causes carbon gas emissions, which in turn leads to global warming. To reduce this problem, it is necessary to turn to environmentally friendly renewable energy sources. One of the most important renewable energy sources is wind energy. Wind energy is produced by machines called wind turbines. Correct positioning of these turbines hugely affects the efficiency of wind energy. In this study, a 2x2 kilometer area is divided into equal-sized cells in a 10x10 grid structure to place wind turbines in the most suitable location. Turbines will be placed by giving each cell a value of 0 (no turbine) or 1 (turbine present). However, there is a possibility of placing 2100 different turbines for a 10x10 grid layout. It is quite difficult to reach the optimum solution in the wind turbine placement (WTP) problem with the brute force technique. Instead, using metaheuristic algorithms produces a much faster and more reasonable solution. In this study, one of the well-known metaheuristics called the moth flame optimization algorithm is utilized to place turbines. Four S-shaped and four V-shaped transfer functions are used to convert the MFO algorithm into a binary structure. The performances of the binary MFO variants are compared regarding fitness function and generated average power values. The obtained results show that BinMFO-S2 and BinMFO-V2 variants have the best performance for fitness function and average power values, respectively. Keywords - Wind Turbine Placement, Moth Flame Optimization, Transfer Functions, Metaheuristic Algorithms.