A novel Weighted Adaptive Aquila Optimizer technique for solving the Optimal Reactive Power Dispatch problem

Abstract

The major problems in the field of power system engineering can be mostly solved with the help of the ORPD problem. Many recently developed optimization techniques have been implemented in this area of power system to optimize the objective function of minimum power loss, and determine its optimal solution leading to a more efficient and secured system. In this paper, a novel Weighted Adaptive Aquila Optimizer (WAAO) has been proposed to solve the highly complex and non-linear problem of Optimal Reactive Power Dispatch (ORPD). The Aquila optimizer (AO) has been modified with the addition of the unique search feature to develop an algorithm with the capability of optimizing any optimization technique faster and better compared to many other efficient techniques including AO technique. Here, the proposed algorithm has been tested on IEEE 30, 57 and 118 bus systems to minimize the power loss objective function of the ORPD problem, and obtain the optimal solutions to the control variables. The results obtained showed a significant improvement in terms of power saving by minimizing the power loss to a large margin for all the three mentioned test cases, which has not yet been reported earlier in the literature. The detailed study in this work proved that the WAAO has better optimization capability compared to AO and many other well-established techniques in solving the problem of ORPD.

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems

“…The improved versions of AO can handle a large range of difficult real-world optimization problems better than the standard AO. The strategies used in AO are hybridization with NIOAs [22,23], oppositional-based learning [24], chaotic sequence [25], Levy flight-based strategy [26], Gauss map and crisscross operator [27], Niche Thought with Dispersed Chaotic Swarm [28], random learning mechanism and Nelder-Mead Simplex Search [29], wavelet mutation [30], Weighted Adaptive Searching Technique [31], Binay AO [32], and multi-objective AO [33].…”

Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering

“…These algorithms offer the features of self-organization, self-adaptation, and self-learning, and they have been widely applied in various domains, such as biology [17,18], feature selection [19], optimization computing [20], image classification [21], and artificial intelligence [22,23]. map and crisscross operator [51], random learning mechanism and Nelder-Mead simplex search [52], wavelet mutation [53], weighted adaptive searching technique [54], binary AO [55], etc. A fine literature examination of the AO algorithm and its application is offered in reference [56].…”