Enhancement of Self-adaptive real-coded genetic algorithm using Taguchi method for Economic dispatch problem

Subbaraj, P.; Rengaraj, R.; Salivahanan, S.

doi:10.1016/j.asoc.2009.10.019

Cited by 132 publications

(52 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Last but not least, the Taguchi method was collaborated with response surface method and GA in parameters optimization was applied on a nano-particle wet milling process (Hou et al, 2007). Using the Taguchi SelfAdaptive Real-Coded Genetic Algorithm (TSARCGA), economic dispatch problem with valve-point loading was elucidated (Subbaraj et al, 2011).…”

Section: Taguchi Approachmentioning

confidence: 99%

“…The orthogonal arrays are represented as Ln(K n-1 ), where n=2 m is the number of experimental runs, m is a positive integer, K is the number of level for each parameter and n-1 is the number of columns in an orthogonal array chart (Subbaraj et al, 2011). For example, in a classical experimental design of three parameters-five levels, the total number of possible experiments is 5 3 or 125.…”

Section: Capturing Data For Orthogonal Arrays and Signalto-noise Ratiomentioning

confidence: 99%

See 1 more Smart Citation

Parameter Settings for New Generational Genetic Algorithms for Solving Global Optimization Problems

Lim¹,

Sulaiman²,

Mustapha³

et al. 2015

Journal of Computer Science

View full text Add to dashboard Cite

This study operates within experimental design with two main tools of Taguchi method namely orthogonal array and signal to noise ratio to discover the optimal parameter settings for newly proposed generational genetic algorithms; they are Laplace Crossover-Scale Truncated Pareto Mutation (LX-STPM) and Rayleigh Crossover-Scale Truncated Pareto Mutation (RX-STPM). It concluded that GA parameter settings are algorithms and problems dependent.

show abstract

Section: Taguchi Approachmentioning

confidence: 99%

Section: Capturing Data For Orthogonal Arrays and Signalto-noise Ratiomentioning

confidence: 99%

Parameter Settings for New Generational Genetic Algorithms for Solving Global Optimization Problems

Lim¹,

Sulaiman²,

Mustapha³

et al. 2015

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…One disadvantage of Taguchi method is that it does not give the mathematical model of the relations between parameters so predicting the intermediate values is impossible. Subbaraj et al (2011) proposed a new optimisation algorithm, namely Taguchi selfadaptive real-coded genetic algorithm (TSARGA) and implemented to solve economic dispatch (ED) problem with valve-point loading. Chang (2011) used Taguchi method, in order to determine the important parameters in genetic algorithm neural networks (GANN) with the goal of reducing the estimation error.…”

Section: International Journal Of Production Researchmentioning

confidence: 99%

“…Signal to noise (S/N) ratios for smaller is better criteria. Subbaraj et al (2011) Taguchi self-adaptive real-coded genetic algorithm (TSARGA)…”

Section: Appendicesmentioning

confidence: 99%

Using response surface design to determine the optimal parameters of genetic algorithm and a case study

Küçükkoç

Karaoğlan

Yaman

2013

International Journal of Production Research

View full text Add to dashboard Cite

Article title: Using response surface design to determine the optimal parameters of genetic algorithm and a case study Usage guidelinesThis version is made available online in accordance with publisher policies. To see the final version of this paper, please visit the publisher's website (a subscription may be required to access the full text).Before reusing this item please check the rights under which it has been made available. Some items are restricted to non-commercial use. Please cite the published version where applicable. Using response surface design to determine the optimal parameters of genetic algorithm and a case study Ibrahim Abstract: Genetic algorithms are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP hard problems. This algorithm includes a number of parameters whose different levels affect the performance of the algorithm strictly. The general approach to determine the appropriate parameter combination of genetic algorithm depends on too many trials of different combinations and the best one of the combinations that produces good results is selected for the program that would be used for problem solving. A few researchers studied on parameter optimisation of genetic algorithm. In this paper, response surface depended parameter optimisation is proposed to determine the optimal parameters of genetic algorithm. Results are tested for benchmark problems that is most common in mixed-model assembly line balancing problems of type-I (MMALBP-I). Using response surface design to determine the optimal parameters of genetic algorithm and a case studyGenetic algorithms are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP hard problems. This algorithm includes a number of parameters whose different levels affect the performance of the algorithm strictly. The general approach to determine the appropriate parameter combination of genetic algorithm depends on too many trials of different combinations and the best one of the combinations that produces good results is selected for the program that would be used for problem solving. A few researchers studied on parameter optimisation of genetic algorithm. In this paper, response surface depended parameter optimisation is proposed to determine the optimal parameters of genetic algorithm. Results are tested for benchmark problems that is most common in mixed-model assembly line balancing problems of type-I (MMALBP-I).

show abstract

“…Numerous variations of GA have already been utilized with great outcomes to take care of ELD issues (Abido, 2003;Subbaraj, 2011;Amjady, 2010). GA has an advantage of using a chromosome coding technique concerned to the defined problem and the two basic disadvantages are very long execution time and the global optimum solution has no guarantee of convergence.…”

Section: Introductionmentioning

confidence: 99%

Solving unit commitment and economic load dispatch problems using modern optimization algorithms

Hassan¹,

Ezzat²,

Abdelaziz³

2017

Int. J. Eng. Sci. Tech

View full text Add to dashboard Cite

Economic Load Dispatch (ELD) and Unit Commitment (UC) are very important applications to predict the optimized cost of load in a power system. UC determines working states for existing generating units under some operational constraints and then optimizing the operation cost for all running units w.r.t. load demand using economic dispatch. This paper introduces Genetic Algorithm (GA) or Dynamic Programming (DP) to solve UC and then Shuffled BAT (BAT) technique as an evolutionary based approach is presented to solve the constrained ELD problem of thermal plants depending on the results obtained from UC solution. The IEEE 30 bus system is used to test the demonstration of the solution quality, computation efficiency and the feasibility of the application of BAT algorithm for ELD problem.

show abstract

Enhancement of Self-adaptive real-coded genetic algorithm using Taguchi method for Economic dispatch problem

Cited by 132 publications

References 32 publications

Parameter Settings for New Generational Genetic Algorithms for Solving Global Optimization Problems

Parameter Settings for New Generational Genetic Algorithms for Solving Global Optimization Problems

Using response surface design to determine the optimal parameters of genetic algorithm and a case study

Solving unit commitment and economic load dispatch problems using modern optimization algorithms

Contact Info

Product

Resources

About