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. To meet this demand, fossil fuels such as coal and gasoline are mostly used. This causes carbon gas emissions, which in turn leads to global warming. In order 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. One of the most important factors affecting the efficiency of wind energy is the correct positioning of these turbines. 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, the moth flame optimization (MFO) algorithm, a metaheuristic algorithm inspired by nature, is used to ensure the optimum location of wind turbines. 8 different S and V-shaped transfer functions are used to convert the MFO algorithm, which can solve continuous problems in its original form, into a binary structure. The performances of the binary MFO algorithms obtained with each transfer function are compared in terms of various performance criteria such as the energy produced, the number of turbines used, the fitness function value and the convergence performance.