Hybridization of two or more variants algorithms is the recent trend research field. With help a hybridized algorithm we try to find out better optimal solution and solve various optimization applications. In this paper, new approach Advancement on Grey Wolf Optimization with Fitness Based Self Adaptive Differential Evolution (AGWO-FSADE) proposed. populations are calculated using self adaptive...

This paper presents the use of a new meta-heuristic technique namely gray wolf optimizer (GWO) which is inspired from gray wolves’ leadership and hunting behaviors to solve optimal reactive power dispatch (ORPD) problem. ORPD problem is a well-known nonlinear optimization problem in power system. GWO is utilized to find the best combination of control variables such as generator voltages, tap c...

Economic load dispatch (ELD) is one of the most important optimization problems in the modern power system. The introduction of non-convex, non-differentiable and non-continuous models like valve point loading (VPL) and prohibited operating zone (POZ) makes the conventional ELD problem to a highly non-linear constrained problem which makes the conventional method to stick to local optima. In th...

Sliding pressure control is a well-known method of controlling supercritical power plants that improves energy efficiency and reduces dynamic stresses. This paper presents novel approach for developing cleaner coal plant’s sliding strategy. First, using Whale Optimizer, nonlinear identified transfer matrix model was created (WO). By comparing simulations errors, the WO clearly outperforms GA Gr...

In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to g...

Received Jun 19, 2017 Revised Aug 15, 2017 Accepted Sep 7, 2017 This manuscript presents a feeder reconfiguration in primary distribution networks with an objective of minimizing the real power loss or maximization of power loss reduction. An optimal switching for the network reconfiguration problem is introduced in this article based on step by step switching and simultaneous switching. This p...

The grey wolf optimization (GWO) algorithm is widely utilized in many global applications. In this paper, a dynamic opposite learning-assisted optimizer (DOLGWO) was proposed to improve the search ability. Herein, learning (DOL) strategy adopted, which has an asymmetric space and can adjust with random point enhance exploitation exploration capabilities. To validate performance of DOLGWO algori...

Abstract In short-term power load forecasting, changes in external multi-dimensional factors will have a certain impact on the accuracy of forecasting. response to this problem, paper proposes prediction model GWO-BILSTM that combines Grey Wolf Optimizer (GWO) and bi-directional long memory (BILSTM) network. Taking real data as set, high correlation parameters are selected input through Pearson...

Grey Wolf Optimizer (GWO) is a nature-inspired swarm intelligence algorithm that mimics the hunting behavior of grey wolves. GWO, in its basic form, real coded needs modifications to deal with binary optimization problems. In this paper, previous work on binarization GWO are reviewed, and classified respect their encoding scheme, updating strategy, transfer function. Then, we propose novel (nam...