Community detection (CD) is a powerful way to extract meaningful information from networks such as political election networks, biological networks, social networks, technological networks. This study proposes a modified discrete version of Coot bird natural life model (COOT) optimization algorithm to solve CD problem in the networks. The basic COOT method is based on the different collective behaviors of the birds of the coot family. These collective actions of coots are regular and irregular movements on the water surface. The position update rule of the basic COOT method does not provide a balance between exploitation and exploration ability for the problem addressed in this study. Therefore, a new update mechanism is integrated into the basic COOT method to extend the local and global search tendencies of the basic COOT method. In the proposed COOT method (for short MCOOT), in order to create a new position for the current coot individual, first the original update mechanism of COOT method is carried out; then, the proposed update mechanism is executed. Three important modifications have been made in the new update mechanism: (1) Some dimensions of the current coot individual are randomly selected in the range of 1 to the dimension size of the problem; (2) the selected dimensions of the coot individual are updated according to the proposed update rule; (3) a genetic mutation operator is executed on the current coot position according to a mutation probability to improve the exploration ability. Furthermore, in the proposed MCOOT method, the continuous values of the current coot positions are converted to discrete values, because the CD problem is a discrete problem. Based on these modifications, in order to analyze and validate the effectiveness of the proposed MCOOT, it is applied on ten different small-sized or large-sized network problems. Finally, the experimental results of MCOOT method are compared with those of some state-of-the-art optimization methods in terms of solution quality and time evaluation. According to the experiments of our study, the proposed algorithm is obtained the best results for all community detection problems used in this study when compared with 22 other algorithms. As a result, the proposed method achieves superior or comparable performance in terms of solution quality and robustness according to the general results. Therefore, the proposed method can be much more competitive, especially for discrete problems.

Renewable energy resources are natural, clean, economical, and never-ending energy resources. Wind energy is an important clean, cheap, and easy applicable energy sources. On account of this, generation of the energy from wind technology has been raised day by day because of the competition with fossil-fuel power production methods. By depending on increases the number of turbines located in the wind farm, the average power obtains from each wind turbine appreciable reduces due to the existence of wake effects within the wind farm. Therefore, the optimal placement of turbines in a wind farm provides to get optimum wind energy from the wind farm. When the place where the wind turbines are located is considered as NxN grid, a wind turbine can be established to each cell of this grid. Whether a wind turbine is replaced to each cell of the grid or not can be modeled as a binary-based optimization problem. In this study, a Jaya-based binary optimization algorithm is proposed to determine which cells are used for wind turbine replacement. In order to justify the efficiency of the proposed approach, two different test cases are considered, and the solutions produced by the proposed approach are compared with the solutions of the swarm intelligence or evolutionary computation methods. According to the experiments and comparisons the Jaya-based binary approach shows a superior performance than compared approaches in terms of cost and power effectiveness. While the efficiency of the Jaya-based approach is 92.2% with 30 turbines replacement on 10 × 10 grid, the efficiency of the Jaya-based binary method is 95.7% with 43 turbines replacement on 20 × 20 grid.