Binary social group optimization algorithm for solving 0-1 knapsack problem

In this paper, we propose the binary version of the Social Group Optimization (BSGO) algorithm for solving the 0-1 knapsack problem. The standard Social Group Optimization (SGO) is used for continuous optimization problems. So a transformation function is used to convert the continuous values generated from SGO into binary ones. The experiments are carried out using both low-dimensional and high-dimensional knapsack problems. The results obtained by the BSGO algorithm are compared with other binary optimization algorithms. Experimental results reveal the superiority of the BSGO algorithm in achieving a high quality of solutions over different algorithms and prove that it is one of the best finding algorithms especially in high-dimensional cases.

A Novel Approach for Image Denoising and Performance Analysis using SGO and APSO

Image denoising is essential to extract the information contained in an image without errors. A technique of using both wavelets and evolutionary computing tools is proposed to denoise and to improve the image quality. An adaptive thresholding-based wavelet denoising technique in the threshold function is coordinated by novel social group optimization (SGO) and accelerated particle swarm optimization (APSO) is proposed. The simulation oriented experimentation is taken out employing MATLAB and the analysis is carried out using the image property metrics similar to peak signal to noise ratio (PSNR), mean square error (MSE) and other structural similarity index metrics (SSIM).

Optimal Placement of Wind Power Plants in Transmission Power Networks by Applying an Effectively Proposed Metaheuristic Algorithm

In this paper, a modified equilibrium algorithm (MEA) is proposed for optimally determining the position and capacity of wind power plants added in a transmission power network with 30 nodes and effectively selecting operation parameters for other electric components of the network. Two single objectives are separately optimized, including generation cost and active power loss for the case of placing one wind power plant (WPP) and two wind power plants (WPPs) at predetermined nodes and unknown nodes. In addition to the proposed MEA, the conventional equilibrium algorithm (CEA), heap-based optimizer (HBO), forensic-based investigation (FBI), and modified social group optimization (MSGO) are also implemented for the cases. Result comparisons indicate that the generation cost and power loss can be reduced effectively, thanks to the suitable location selection and appropriate power determination for WPPs. In addition, the generation cost and loss of the proposed MEA are also less than those from other compared methods. Thus, it is recommended that WPPs should be placed in power systems to reduce cost and loss, and MEA is a powerful method for the placement of wind power plants in power systems.