A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch

Abido, M. A.

doi:10.1016/s0142-0615(02)00027-3

Cited by 293 publications

(147 citation statements)

References 22 publications

Supporting

Mentioning

143

Contrasting

Unclassified

Order By: Relevance

“…is random number in the range of [0, 1]; denotes adjustment coefficient which uses the arc tangent function to make it changeable dynamically; and are initial and final value of which are evaluated with 0.9 and 0.4, respectively; represents controlling factor in the range of [0.4, 0.7]; , , , and are defined in (20). To maintain fast convergence characteristic of IR-SFLA and reduce uncertainty of random number, a relative lower is used in the later iteration stage.…”

Section: The Adaptive Frog Activation Mechanismmentioning

confidence: 99%

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

Yang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The short-term hydro generation scheduling (STHGS) decomposed into unit commitment (UC) and economic load dispatch (ELD) subproblems is complicated problem with integer optimization, which has characteristics of high dimension, nonlinear and complex hydraulic and electrical constraints. In this study, the improved binary-real coded shuffled frog leaping algorithm (IBR-SFLA) is proposed to effectively solve UC and ELD subproblems, respectively. For IB-SFLA, the new grouping strategy is applied to overcome the grouping shortage of SFLA, and modified search strategies for each type of frog subpopulation based on normal cloud model (NCM) and chaotic theory are introduced to enhance search performance. The initialization strategy with chaos theory and adaptive frog activation mechanism are presented to strengthen performance of IR-SFLA on ELD subproblem. Furthermore, to solve ELD subproblem, the optimal economic operation table is formed using IR-SFLA and invoked from database. Moreover, reserve capacity supplement and repair, and minimum on and off time repairing strategies are applied to handle complex constraints in STHGS. Finally, the coupled external and internal model corresponding to UC and ELD subproblems is established and applied to solve STHGS problem in Three Gorges hydropower station. Simulation results obtained from IBR-SFLA are better than other compared algorithms with less water consumption. In conclusion, to solve STHGS optimization problem, the proposed IBR-SFLA presents outstanding performance on solution precision and convergence speed compared to traditional SFLA effectively and outperforms the rivals to get higher precision solution with improving the utilization rate of waterpower resources.

show abstract

Section: The Adaptive Frog Activation Mechanismmentioning

confidence: 99%

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

Yang

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Sharing-Based Niche Method NGA has been proved that when the number of chromosomes within the population is large enough and the niche radius is properly set, a sharing function provides as many niches in the population as the number of peaks in the fitness landscape [44,45]. However, there are several problems such as stability and maintainability [45].…”

Section: Algorithm 2 Niches Partitioning Based On Densitymentioning

confidence: 99%

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Zhou

Shen

et al. 2017

IJGI

View full text Add to dashboard Cite

Rapidly growing Global Positioning System (GPS) data plays an important role in trajectory and their applications (e.g., GPS-enabled smart devices). In order to employ K-means to mine the better origins and destinations (OD) behind the GPS data and overcome its shortcomings including slowness of convergence, sensitivity to initial seeds selection, and getting stuck in a local optimum, this paper proposes and focuses on a novel niche genetic algorithm (NGA) with density and noise for K-means clustering (NoiseClust). In NoiseClust, an improved noise method and K-means++ are proposed to produce the initial population and capture higher quality seeds that can automatically determine the proper number of clusters, and also handle the different sizes and shapes of genes. A density-based method is presented to divide the number of niches, with its aim to maintain population diversity. Adaptive probabilities of crossover and mutation are also employed to prevent the convergence to a local optimum. Finally, the centers (the best chromosome) are obtained and then fed into the K-means as initial seeds to generate even higher quality clustering results by allowing the initial seeds to readjust as needed. Experimental results based on taxi GPS data sets demonstrate that NoiseClust has high performance and effectiveness, and easily mine the city's situations in four taxi GPS data sets.

show abstract

“…Again, the multi-objective problem mentioned above could be solved after converting EELD problem to a solo objective optimization problem using cost penalty factors (CPF) [20].The whole objective function may be put together by using PPF and given as: Minimize FC = ∑ wC P + (1 − w)jE (P ) (7) Here 'j' is the cost penalty factor that combines the emission costs with the usual fuel costs and 'w' is the bargaining parameter varying between [0, 1]. The above equation is reduced subject to power balance and generating limits constraints as mentioned in ©IJRASET (UGC Approved Journal): All Rights are Reserved (2) and (4). When the value of w is 1 the objective function represents fuel cost of generation function and when w is equal to 0, the objective function represents emission function only.…”

Section: Multiobjective Economic Emission Load Dispatch (Eeld) Promentioning

confidence: 99%

Best Compromise Solution of Economic Emission Load Dispatch Using Ant Lion Optimization

Rathore¹

2017

IJRASET

View full text Add to dashboard Cite

I.INTRODUCTION The main goal of economic emission load dispatch problem is to get optimum output of thermal generators in power system subjected to several constraints to minimize the operating cost. The thermal power plant operation is dependent upon combustion of fossil fuel which produces SOx, NOx and COx emissions. The increasing pollution is a matter of environmental concern worldwide which has led to formation of international standards for emissions from industries and power plants. Different acts have been made which forces the industries to modify their principles to follow the environment-emission standards strictly. Thus it becomes significant to do emission dispatch or consider emission constraints in economic emission load dispatch. The economic & emission scheduling are contradictory in character and both must be considered together to find optimal scheduling. The problem is put together as a multi objective economic emission load dispatch problem (EELD) problem in which both objectives (emission and economy) have to be minimized. Earlier traditional methods like Newton's method, gradient approach and linear programming [1] were used for solving economic load scheduling problem. In the last years different techniques have been used for solving EELD. Nanda et al [2] applied goal programming techniques to solve economic emission load dispatch. Song et.al [3] solved environmental/economic dispatch with genetic algorithm controlled by fuzzy logic. Abido [4] used genetic algorithm for the economic emission load dispatch (EELD) to find out pareto-optimal solutions. Ah King [5, 6] applied improved non-dominated sorting genetic algorithm (NSGA-II) for creating pareto-optimal front for EELD. Then mozhi [7] solved EELD using hybrid genetic algorithm. Perez [8] solved environmental/economic dispatch using differential evolution. Hong [9] applied immune genetic algorithm for EELD. Hazra [10] proposed bacteria foraging algorithm for emission constrained economic dispatch. Hemamalini [11] solved non convex EELD by applying particle swarm optimization. Sudhakaran applied refined genetic algorithm [12]

show abstract

A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch

Cited by 293 publications

References 22 publications

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density

Best Compromise Solution of Economic Emission Load Dispatch Using Ant Lion Optimization

Contact Info

Product

Resources

About