Finding Solutions for Optimal Reactive Power Dispatch Problem by a Novel Improved Antlion Optimization Algorithm

Abstract

In this paper, a novel improved Antlion optimization algorithm (IALO) has been proposed for solving three different IEEE power systems of optimal reactive power dispatch (ORPD) problem. Such three power systems with a set of constraints in transmission power networks such as voltage limitation of all buses, limitations of tap of all transformers, maximum power transmission limitation of all conductors and limitations of all capacitor banks have given a big challenge for global optimal solution search ability of the proposed method. The proposed IALO method has been developed by modifying new solution generation technique of standard antlion optimization algorithm (ALO). By optimizing three single objective functions of systems with 30, 57 and 118 buses, the proposed method has been demonstrated to be more effective than ALO in terms of the most optimal solution search ability, solution search speed and search stabilization. In addition, the proposed method has also been compared to other existing methods and it has obtained better results than approximately all compared ones. Consequently, the proposed IALO method is deserving of a potential optimization tool for solving ORPD problem and other optimization problems in power system optimization fields.

Application of Shannon Entropy Implementation Into a Novel Fractional Particle Swarm Optimization Gravitational Search Algorithm (FPSOGSA) for Optimal Reactive Power Dispatch Problem

“…The development and the exploitation in metaheuristic approach are further exposed to improve the results while resolving the optimal reactive power dispatch problems (ORPD) such as; particle swarm optimization [11], harmony search algorithm [12], improved harmony search algorithm [13] gray wolf optimizer [14], cuckoo search algorithm [15], backtracking search algorithm [16], gravitational search algorithm [17], seeker optimization algorithm [18], gaussian bare-bones water cycle algorithm [19], colliding bodies optimization algorithm [20], chaotic krill herd algorithm [21], moth-flame algorithm [22], chaotic particle swarm optimization [23], bacterial colony chemotaxis algorithm [24], whale optimization algorithm [25], adaptive chaotic symbiotic organisms search algorithm [26], imperialist competitive algorithm [27], invasive weed optimization [28] and firefly algorithm [29]. Moreover, some other meta-heuristic approaches in support are discussed in [30][31][32][33][34][35].…”

Ant Lion Optimization: Variants, Hybrids, and Applications

“…Also, Li et al [119] proposed a novel version of ALO by replacing roulette wheel with new technique which classify antlions to 2 groups then, some novel equations are used to breakthrough steps to enhance diverse exploration and thorough exploitation in each group. In [120] Yu proposed an improved version of antlion by hybridizing ALO with Nelder-Mead algorithm and apply it to detect structural damage by improving weighted trace lasso regularization.…”

An Improved Lightning Attachment Procedure Optimizer for Optimal Reactive Power Dispatch With Uncertainty in Renewable Energy Resources

“…ORPD problem is a non-convex, complex, and non-linear optimization problem. Thus, many efforts have been introduced for solving the ORPD by applying numerous optimization techniques including the Backtracking Search Optimizer (BSO) [2], Particle Swarm Optimization (PSO) [3], Ant Lion Optimizer (ALO) [4], Improved Ant Lion Optimization algorithm (IALO) [5] , Whale Optimization Algorithm (WOA) [6], Improved Social Spider Optimization Algorithm (ISSO) [7], Differential Evolution (DE) [8], Moth Swarm Algorithm (MSA) [9], Evolutionary Algorithm (EA) [10], Modified Differential Evolution (MDE) [11], Jaya Algorithm (JA) [12], Modified Sine Cosine Algorithm (MSCA) [13], Lightning Attachment Procedure Optimization (LAPO) [14], Gravitational Search Algorithm (GSA) [15], Biogeography-Based Optimization (BBO) [16], Teaching Learning Based Optimization (TLBO) [17], Harmony Search Algorithm (HAS) [17], Grey Wolf Optimizer (GWO) [18], Comprehensive Learning Particle Swarm Optimization (CLPSO) [19], Chemical Reaction Optimization (CRO) [20], Improved Gravitational Search Algorithm (IGSA) [21], Improved Pseudo-Gradient Search Particle Swarm Optimization (IPG-PSO) [22], Firefly Algorithm (FA) [23], Fractional Particle Swarm Optimization Gravitational Search Algorithm [24], hybrid GWO-PSO optimization [25], Oppositional Salp Swarm Algorithm (OSSA) [26], diversity-enhanced particle swarm optimization (DEPSO) [27].…”