Adaptive group search optimization algorithm for multi-objective optimal power flow problem

Daryani, Narges; Hagh, Mehrdad Tarafdar; Teimourzadeh, Saeed

doi:10.1016/j.asoc.2015.10.057

Cited by 143 publications

(69 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This comparison shows the effectiveness and the robustness of the proposed algorithm, and we can say the MFO algorithm also provides very competitive results compared to other algorithms. [23] 801.75 Adaptive Group Search Optimization EP [34] 802.63 Evolutionary Programming TS [17] 802.3 Tabu search PSO [35] 802.205 Particle swarm optimization ABC [36] 800.6600 Artificial bee colony algorithm DE [37] 799.2891 Differential evolution algorithm GA [38] 805.94 Genetic algorithm BBO [19] 799.1116 Biogeography-Based Optimization IGA [26] 800.805 Improved Genetic Algorithms ICA [39] 801.843 Imperialist Competitive Algorithm EADHDE [40] 800.1579 Genetic Evolving Ant Direction HDE SA [41] 799.45 Simulated Annealing SGA (wo-VP) [42] 802.359 Hybrid genetic algorithm GM [43] 804.853 Gradient Method…”

Section: Comparative Studymentioning

confidence: 99%

“…The first solution method for the OPF problem was proposed by Dommel and Tinney [7] in 1968, and since then numerous other methods have been proposed, some of them are: Ant Colony Optimization (ACO) [8], Genetic Algorithm (GA) [9][10], enhanced genetic algorithm (EGA) [11][12], Hybrid Genetic Algorithm (HGA) [13], artificial neural network (ANN) [14], Particle swarm optimization (PSO) [15], fuzzy based hybrid particle swarm optimization (fuzzy HPSO) [16], Tabu Search (TS) [17], Gravitational Search Algorithm (GSA) [18]. Biogeography based optimization algorithm (BBO) [19], harmony search algorithm (HS) [20], krill herd algorithm (KHA) [21], Cuckoo Search (CS) [22], adaptive group search optimization (AGSO) [23], BlackHole-Based Optimization (BHBO) [24]. The reported results were promising and encouraging new research in this direction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Power Flow using the Moth Flam Optimizer: A Case Study of the Algerian Power System

Bentouati¹,

Chaib²,

Chettih³

2016

IJEECS

View full text Add to dashboard Cite

In this paper, a new technique of optimization known as Moth-Flam Optimizer (MFO) IntroductionOptimal power flow (OPF) is a well studied optimization problem in power systems. In 1962, such an issue was first introduced by Carpentier [2]. The problem of the OPF can be defined as a nonlinear programming problem [1]. The main objective of the OPF problem is to optimize chosen objective functions such as piecewise quadratic cost function, fuel cost with valve point effects and voltage profile improvement, by optimal adjusting the power system control variables and satisfying various system operating such as power flow equations and inequality constraints, simultaneously [3][4][5][6].To solve this problem, the researchers proposed a number of optimization algorithms over the years. Therefore optimization is known as one of the most current problem facing research, a good optimization leads to an optimal solution for an efficient system. The first solution method for the OPF problem was proposed by Dommel and Tinney [7] A new method known as the Moth-Flame Optimization MFO algorithm this method has been proposed by Seyedali Mirjalili [25] in 2015 is a nature-inspired method from navigating mechanism of moths in nature called transverse orientation, which has not received yet much attention in the power system. Hence, the first objective of this paper is to apply a new method that is the MFO in order to solve the OPF problem. In what follows, we will briefly give the mathematical model on the proposed algorithm of spiral flying path of moths around artificial lights (flames) [25].In this paper, an approach based on MFO is proposed to solve the OPF problem. This problem has been formulated as a nonlinear optimization problem with equality and inequality constraints. Indeed, different objectives are considered in this work to minimize the cost of fuel, emission, and improve the voltage profile. Moreover, this method is simulated and tested on the Algerian power system network. In addition, the results are compared with other methods reported in any relevant literature dealing with the subject.

show abstract

Section: Comparative Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Power Flow using the Moth Flam Optimizer: A Case Study of the Algerian Power System

Bentouati¹,

Chaib²,

Chettih³

2016

IJEECS

View full text Add to dashboard Cite

show abstract

“…To show the applicability and efficiency of BSA, three different test systems are utilized. In 2016, Daryani et al [40] presented an adaptive group search optimization (AGSO) algorithm for solving optimal power flow (OPF) problem. In 2015, Mahdad and Srairi [41] presented a new approach of power system planning which is based on hybrid firefly algorithm (FFA) and pattern search (PS) algorithm and supported with brainstorming rules to minimize total fuel cost, power losses, and voltage deviation.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

Khan

Singh

2017

Journal of Engineering

View full text Add to dashboard Cite

The exhaustive knowledge of optimal power flow (OPF) methods is critical for proper system operation and planning, since OPF methods are utilized for finding the optimal state of any system under system constraint conditions, such as loss minimization, reactive power limits, thermal limits of transmission lines, and reactive power optimization. Incorporating renewable energy sources optimized the power flow of system under different constraints. This work presents a comprehensive study of optimal power flows methods with conventional and renewable energy constraints. Additionally, this work presents a progress of optimal power flow solution from its beginning to its present form. Authors classify the optimal power flow methods under different constraints condition of conventional and renewable energy sources. The current and future applications of optimal power flow programs in smart system planning, operations, sensitivity calculation, and control are presented. This study will help the engineers and researchers to optimize power flow with conventional and renewable energy sources.

show abstract

“…Some of the most famous of these algorithms are BA [12], GA [13], Harmony Search (HS) [14], ACO [15], Cuckoo Search (CS) [16], Bacterial Foraging Optimization (BFO) [17], PSO [18], Artificial Bee Colony (ABC) [19], Black Hole (BH) [20], One Half Personal Best Position Particle Swarm Optimizations (OHGBPPSO) [21], Half Mean Particle Swarm Optimization algorithm (HMPSO) [22], Personal Best Position Particle Swarm Optimization (PBPPSO) [23], Hybrid Particle Swarm Optimization (HPSO) [24], Hybrid MGBPSO-GSA [25] and MGWO [26], Gravitational Search Algorithm (GSA) [27], Artificial Neural Network (ANN) [28], SCA [29], Adaptive Group Search Optimization (AGSO) [30], Ant Lion Optimizer (ALO) [31], Biogeography Based Optimization (BBO) [32], Moth Flame Optimizer (MFO) [33], Krill Herd Algorithm (KHA) [34], Grasshopper Optimization Algorithm (GOA) [35], Multi-Verse Optimizer (MVO) [36], Black-Hole-Based Optimization (BHBO) [37], Dragonfly Algorithm (DA) [38], HPSOGWO [39], MOSCA [40] and so forth.…”

Section: Related Workmentioning

confidence: 99%

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Singh

Hachimi

2018

MCA

View full text Add to dashboard Cite

Abstract:The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO) and Whale Optimizer Algorithm (WOA). We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO) algorithm: (i) firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii) secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

show abstract

Adaptive group search optimization algorithm for multi-objective optimal power flow problem

Cited by 143 publications

References 46 publications

Optimal Power Flow using the Moth Flam Optimizer: A Case Study of the Algerian Power System

Optimal Power Flow using the Moth Flam Optimizer: A Case Study of the Algerian Power System

Optimal Power Flow Techniques under Characterization of Conventional and Renewable Energy Sources: A Comprehensive Analysis

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Contact Info

Product

Resources

About