Genetic Algorithms Connected Simulation with Smoothing Function for Searching Rule Curves

Kangrang, Anongrit; Chaleeraktrakoon, Chavalit

doi:10.3844/ajassp.2007.73.79

Cited by 19 publications

(21 citation statements)

References 8 publications

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“…Simulation model: The developed simulation model in the previous study [10] was adopted to modify in this study. This simulation model had been constructed on the concept of HEC-3 [14] and it can be used to simulate the reservoir operation.…”

Section: Methodsmentioning

confidence: 99%

“…However, the appropriate objective function for searching the curves is average water shortage. Also, a smoothing function constraint is required to include into the proposed GAs for fitting the rule curves [10] . However, GAs is complex to optimization technique because this method is complicacy for creating multi-computation to analysis the optimal reservoir rule curves.…”

Section: Introductionmentioning

confidence: 99%

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Heuristic Algorithm with Simulation Model for Searching Optimal Reservoir Rule Curves

Kangrang¹,

Compliew²,

Chaiyapoom³

2009

American J. of Applied Sciences

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This study proposes a heuristic algorithm to connect with simulation model for searching the optimal reservoir rule curves. The proposed model was applied to determine the optimal rule curves of the Ubolratana reservoir (the Chi River Basin, Thailand). The results showed that the pattern of the obtained rule curves similar to the existing rule curve. Then the obtained rule curves were used to simulate the Ubolratana reservoir system with the synthetic inflows. The results indicated that the frequency of water shortage and the average water shortage are reduced to 44.31 and 43.75% respectively, the frequency of excess release and the average excess release are reduced to 24.08% and 22.81%.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heuristic Algorithm with Simulation Model for Searching Optimal Reservoir Rule Curves

Kangrang¹,

Compliew²,

Chaiyapoom³

2009

American J. of Applied Sciences

Self Cite

View full text Add to dashboard Cite

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“…The GAs connected simulation model developed in the previous study [12] was adopted in this study. This simulation model had been constructed on the concept of HEC-3 [17] and it can be used to simulate the reservoir operation.…”

Section: Genetic Algorithms Embedded Stochastic Simulation Modelmentioning

confidence: 99%

“…However, the appropriate objective function for searching the curves is average water shortage. Also, a smoothing function constraint is required to include into the proposed GAs for fitting the rule curves [12] . The reservoir simulation models which mentioned above require the same necessary input data including inflow record, physical characteristic of the reservoir and conditional rule curves [2,3,13] .…”

Section: Introductionmentioning

confidence: 99%

Stochastic Inflow Simulation for Searching Rule Curves

Kangrang¹,

Phumphan²,

Chaiyapoom³

2008

American J. of Applied Sciences

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Rule curves are basic monthly guidelines for long term reservoir operation. Generally, the optimal rule curves are searched by reservoir simulation model and optimization techniques. A traditional reservoir simulation does not consider the risk of reservoir operation caused by natural uncertainty from inflow. A stochastic simulation model embedded genetic algorithm model is developed for searching the optimal rule curves in this study. Synthetic inflows are used in the developed model for assessing the risk reservoir operation. Single and multi-reservoir systems are applied to assess the efficiency of the proposed technique. The developed model has been applied to determine the optimal rule curves of the Bhumibol and Sirikit Reservoirs (the Chao Phraya River Basin, Thailand) for multi-reservoir system and the Ubolratana Reservoir (the Chi River Basin, Thailand) for single system. The optimal rule curves of each system were used to assess by a Monte Carlo simulation. The results show that the situations of water shortage and excess release of the obtained rule curves are not significantly different from the situation of the curves searching by tradition simulation. It can be concluded that the stochastic simulation model embedded genetic algorithm provided the optimal rule curves as considering the risk of reservoir operation. Furthermore, the proposed model is applicable for both single and multi-reservoir systems.

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“…The "Generalized Chromosome Genetic Algorithm (or GCGA)" was developed by Yang et al (2008) to improve the encoding process in which the chromosome is divided into 2 parts, head and body, to solve the TSP problem. Kangrang and Chaleeraktrakoon (2007) developed the encoding schemes that transform the monthly level of rule curves of reservoirs. The new encoding method was also presented by Carter and Ragsdale (2006) for the Multiple traveling Salesperson Problem (or mTSP).…”

Section: Introductionmentioning

confidence: 99%

An Improved Genetic Algorithm for the Traveling Salesman Problem with Multi-Relations

Patvichaichod¹

2011

Journal of Computer Science

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Problem statement:The aim of this research is to investigate the Traveling Salesman Problems with Multi-Relations (TSPMR) in which each of the vertices has several edges and different weights. The study concerns the weights fluctuated by time in which the problem has more complexity than general TSP problem. However, this type of problem closes to the real-world situation in engineering aspect. Approach: The new genetic algorithm was developed specially for the TSPMR. With the new genetic algorithm, the new coding system is a mixer between the integer numbers (represents the vertices) and the binary (represents the edges). The experiments were done to investigate the suitable input variables, which are a population size, generation, crossover rate and mutation rate. Results: The suitable generation between 300 and 400, the suitable crossover rate between 20 and 60% and the suitable mutation rate between 30 and 80%. Conclusion: The new developed method in this research is found to efficiently solve the problem with less than 100 vertices, which can reduce 50% of the transportation cost. In case of 100 -1,000 vertices problem, the transportation cost is reduced approximately by 20%. If the vertices are more than 1,000, the transportation cost can be reduced less than 20%. For the large size of problem as 30,000 vertices, the transportation cost is reduced for 6.07%.

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Genetic Algorithms Connected Simulation with Smoothing Function for Searching Rule Curves

Cited by 19 publications

References 8 publications

Heuristic Algorithm with Simulation Model for Searching Optimal Reservoir Rule Curves

Heuristic Algorithm with Simulation Model for Searching Optimal Reservoir Rule Curves

Stochastic Inflow Simulation for Searching Rule Curves

An Improved Genetic Algorithm for the Traveling Salesman Problem with Multi-Relations

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