Abstract
In this paper, a nonlinear reservoir release optimization problem has been solved by using four optimization tools with various combinations of input parameters that are generally used in this research field. A comparison has been made between evolutionary methods [genetic algorithm (GA)] and swarm intelligences [particle swarm optimization (PSO) and artificial bee colony (ABC) optimization] in searching the optimum reservoir release policy. From the historical recorded data, the monthly inflow was categorized into three states: high, medium and low. As a guideline for the decision maker, an optimum release curve was generated for each month showing the release options with a variety of different storage conditions. GA (real and binary), ABC optimization and PSO algorithm have been used as optimization tools with the same formulation and objective function for all the methods. For verification of the models, a simulation is done by using 264 monthly historical inflow data. Different indices such as reliability, vulnerability and resiliency were calculated in order to check the performance and risk analysis purposes. The results show that the most recently developed ABC optimization technique provides the best results in meeting demands, avoiding wastage of water and in handling critical period of low flows.
| Original language | English |
|---|---|
| Pages (from-to) | 1583-1594 |
| Number of pages | 12 |
| Journal | Neural Computing and Applications |
| Volume | 24 |
| Issue number | 7-8 |
| DOIs | |
| Publication status | Published - 2014 |
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Keywords
- Artificial bee colony optimization
- Genetic algorithms
- Optimal reservoir release policy
- Particle swarm optimization
ASJC Scopus subject areas
- Artificial Intelligence
- Software
Cite this
Evolutionary techniques versus swarm intelligences : Application in reservoir release optimization. / Hossain, M. S.; El-Shafie, A.
In: Neural Computing and Applications, Vol. 24, No. 7-8, 2014, p. 1583-1594.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Evolutionary techniques versus swarm intelligences
T2 - Application in reservoir release optimization
AU - Hossain, M. S.
AU - El-Shafie, A.
PY - 2014
Y1 - 2014
N2 - In this paper, a nonlinear reservoir release optimization problem has been solved by using four optimization tools with various combinations of input parameters that are generally used in this research field. A comparison has been made between evolutionary methods [genetic algorithm (GA)] and swarm intelligences [particle swarm optimization (PSO) and artificial bee colony (ABC) optimization] in searching the optimum reservoir release policy. From the historical recorded data, the monthly inflow was categorized into three states: high, medium and low. As a guideline for the decision maker, an optimum release curve was generated for each month showing the release options with a variety of different storage conditions. GA (real and binary), ABC optimization and PSO algorithm have been used as optimization tools with the same formulation and objective function for all the methods. For verification of the models, a simulation is done by using 264 monthly historical inflow data. Different indices such as reliability, vulnerability and resiliency were calculated in order to check the performance and risk analysis purposes. The results show that the most recently developed ABC optimization technique provides the best results in meeting demands, avoiding wastage of water and in handling critical period of low flows.
AB - In this paper, a nonlinear reservoir release optimization problem has been solved by using four optimization tools with various combinations of input parameters that are generally used in this research field. A comparison has been made between evolutionary methods [genetic algorithm (GA)] and swarm intelligences [particle swarm optimization (PSO) and artificial bee colony (ABC) optimization] in searching the optimum reservoir release policy. From the historical recorded data, the monthly inflow was categorized into three states: high, medium and low. As a guideline for the decision maker, an optimum release curve was generated for each month showing the release options with a variety of different storage conditions. GA (real and binary), ABC optimization and PSO algorithm have been used as optimization tools with the same formulation and objective function for all the methods. For verification of the models, a simulation is done by using 264 monthly historical inflow data. Different indices such as reliability, vulnerability and resiliency were calculated in order to check the performance and risk analysis purposes. The results show that the most recently developed ABC optimization technique provides the best results in meeting demands, avoiding wastage of water and in handling critical period of low flows.
KW - Artificial bee colony optimization
KW - Genetic algorithms
KW - Optimal reservoir release policy
KW - Particle swarm optimization
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U2 - 10.1007/s00521-013-1389-8
DO - 10.1007/s00521-013-1389-8
M3 - Article
VL - 24
SP - 1583
EP - 1594
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
IS - 7-8
ER -