NSGA-II/EDA Hybrid Evolutionary Algorithm for Solving Multi-objective Economic/Emission Dispatch Problem

Alawode, Kehinde O.; Adegboyega, Gabriel Adebayo; Muhideen, Jubril Abimbola

doi:10.1080/15325008.2018.1488302

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…Fuzzy theory was also used in this reference to select the best compromise response. Alawode et al [30] proposed a combination of NSGA-II and a modified estimation of distribution algorithm called NSGA-II/EDA to improve convergence and good diversity of CEED problem solutions. In this reference, to reduce the effect of variable interaction on the EDA marginal histogram model, multi-scale principal component analysis was applied on the solutions.…”

Section: Related Workmentioning

confidence: 99%

Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique

Nourianfar

Abdi

2022

Sustainability

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This work tackles a relatively new issue in power system operation, known as the Environmental/Economic Dispatch problem. For this purpose, the combination of two powerful heuristic algorithms, namely, the Exchange Market Algorithm (EMA) and Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO), was employed. Additionally, the Multiple Constraint Ranking (MCR) technique was used to address the system constraints such as prohibited operating zones and ramp rate limits. Furthermore, the mutation operator was used to improve the performance of the global search mechanism. The main purpose of combining these two algorithms was utilizing the EMA’s high performance to explore the global optimum and local exploitation ability of AIWPSO. The algorithm performance was evaluated on six standard benchmark functions and was scrutinized on several different test systems, including 6–40 units. By using the proposed method, the minimum values of the reduction in annual costs, with equal or less emissions, compared to other methods, were USD 17,520, 8760 and 10,801,080, respectively, for the 6-unit, 10-unit, and 40-unit test systems (assuming the same load profile throughout the year). Similarly, in the 14-unit test system for 1750, 2150, and 2650 (MW) load demands, these values were USD 229,879, 148,438, and 4483, respectively.

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Section: Related Workmentioning

confidence: 99%

Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique

Nourianfar

Abdi

2022

Sustainability

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show abstract

“…The literature proves that hybridizing metaheuristic techniques is one of the ways of using the advantages of many techniques. It also aids in developing an efficient technique to solve optimization problems, such as sine-cosine crow algorithm [37], NSGA-II/EDA hybrid EA [38], nondominated sorting moth-flame optimization (NS-MFO) [39], K-means-clustering-based EA [40], and GA-based clustering technique [41].…”

Section: Different Conventional Algorithms Have Been Developed To Solvementioning

confidence: 99%

“…The main objective of EEDP is the minimization of fuel cost and emission of atmospheric pollutants which are two conflicting objective functions while satisfying inequality and equality constraints foist by the power system requirements. The objectives and constraints of EEDP are given below [38]:…”

Section: The Eedp: Case Studymentioning

confidence: 99%

“…The parameter settings which are used for optimizing the EEDP are the following: the size of population Npop = 200 individuals, the maximum number of generations q max = 100 and NFEs = 20,000, the crossover rate Pc = 0.97, and mutation rate P m = 0.09, respectively. The NS-SCGA was run 30 independent runs and it is compared with NPGA [9], a hybrid MO algorithm based on particle swarm optimization and differential evolution (MO-DE/PSO) [31], MOPSO with time variant inertia and acceleration coefficients (TV-MOPSO) [94], SPEA [11], multiobjective harmony search (MOHS) algorithm [95], NSGA-II, hybrid evolutionary algorithm (NSGA-II/EDA) [38], and Multiobjective Collective Decision Optimization Algorithm (MOCDOA) [96] in terms of their best solutions for both minimum emissions and minimum fuel cost (the extreme points on the Pareto front). Moreover, the comparison is also done in terms of the SP metrics.…”

Section: The Eedp: Case Studymentioning

confidence: 99%

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A New Hybrid Metaheuristic Algorithm for Multiobjective Optimization Problems

Farag¹,

El-Shorbagy²,

Mousa³

et al. 2020

IJCIS

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The elitist nondominated sorting genetic algorithm (NSGA-II) is hybridized with the sine-cosine algorithm (SCA) in this paper to solve multiobjective optimization problems. The proposed hybrid algorithm is named nondominated sorting sine-cosine genetic algorithm (NS-SCGA). The main idea of this algorithm is the following: NS-SCGA integrates the merits of exploitation capability of NSGA-II and exploration capability of SCA for a better search ability and speeds up the searching process. The performance of NS-SCGA is tested on the set of benchmark functions provided for CEC09. The NS-SCGA results are compared with other recently developed multiobjective algorithms in terms of convergence, spacing, and spread of the obtained nondominated solutions to the true Pareto front. The statistical analysis of the results obtained is performed by nonparametric Friedman and Wilcoxon signed-rank tests. The results prove that NS-SCGA is superior to or competitive with other multiobjective optimization algorithms considered in the comparison. Furthermore, the economic emission dispatch problem (EEDP) is solved by NS-SCGA. The operating cost (fuel cost) and pollutant emission of the standard IEEE 30-bus network with six generating units are minimized simultaneously by the NS-SCGA considering the losses. The results show the superiority of NS-SCGA and confirm its ability in solving EEDP. Finally, TOPSIS technique is applied to choose the best compromise solution from the obtained Pareto-optimal solutions of EEDP according to the decision-maker's preference.

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Multi Objective Optimization of Dimension Accuracy and Surface Roughness in High-Speed Finishing Milling for the Hard Steel Alloy After Heat-Treatment

Bien

Duong²,

Lap

et al. 2021

Advances in Asian Mechanism and Machine Science

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NSGA-II/EDA Hybrid Evolutionary Algorithm for Solving Multi-objective Economic/Emission Dispatch Problem

Cited by 12 publications

References 32 publications

Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique

Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique

A New Hybrid Metaheuristic Algorithm for Multiobjective Optimization Problems

Multi Objective Optimization of Dimension Accuracy and Surface Roughness in High-Speed Finishing Milling for the Hard Steel Alloy After Heat-Treatment

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