Updated Particle Swarm Optimization (Pso) Algorithm In Calibrating Reservoir Release Policy

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Abstract

Particle swarm optimization is a very well-known method as it has a strong background in optimization filed to
solve different non-linear, complex problems. This study made a fine tuning in the particle updating process of
standard PSO algorithm. The updated algorithm is used to develop and optimize a reservoir release policy for
monthly basis. The historical data of inflow to the dam/reservoir has categorized in three different category
(High, medium and low). The problem formation has done on the basis of release and storage constraints. The
objective function which was aimed to be minimized has considered as water deficit from the release. Monthly
releases are taken as the main objective variables and are essentially control the water deficit of the process.
The standard form of PSO then compared with the updated version and the results is analyzed by adopting
different performance measuring indicators such as reliability, vulnerability and resilience. These performance
measuring indices are calculated from the outcome of the simulation process by feeding the optimization model
with the actual historical data of inflow. From the results of the simulation and the value of the indicators, the
study shows updated PSO algorithm performs significantly better in optimizing reservoir releases policy.
Original languageEnglish
Publication statusPublished - 14 Aug 2017

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Particle swarm optimization (PSO)
inflow
simulation
vulnerability
dam
Dams
Water
water
Tuning
particle
policy
indicator
index
measuring
method

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)
  • Computer Science(all)

Cite this

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title = "Updated Particle Swarm Optimization (Pso) Algorithm In Calibrating Reservoir Release Policy",
abstract = "Particle swarm optimization is a very well-known method as it has a strong background in optimization filed tosolve different non-linear, complex problems. This study made a fine tuning in the particle updating process ofstandard PSO algorithm. The updated algorithm is used to develop and optimize a reservoir release policy formonthly basis. The historical data of inflow to the dam/reservoir has categorized in three different category(High, medium and low). The problem formation has done on the basis of release and storage constraints. Theobjective function which was aimed to be minimized has considered as water deficit from the release. Monthlyreleases are taken as the main objective variables and are essentially control the water deficit of the process.The standard form of PSO then compared with the updated version and the results is analyzed by adoptingdifferent performance measuring indicators such as reliability, vulnerability and resilience. These performancemeasuring indices are calculated from the outcome of the simulation process by feeding the optimization modelwith the actual historical data of inflow. From the results of the simulation and the value of the indicators, thestudy shows updated PSO algorithm performs significantly better in optimizing reservoir releases policy.",
author = "{Mohd Sidek}, Lariyah and Hossain, {Md Shabbir}",
year = "2017",
month = "8",
day = "14",
language = "English",

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T1 - Updated Particle Swarm Optimization (Pso) Algorithm In Calibrating Reservoir Release Policy

AU - Mohd Sidek, Lariyah

AU - Hossain, Md Shabbir

PY - 2017/8/14

Y1 - 2017/8/14

N2 - Particle swarm optimization is a very well-known method as it has a strong background in optimization filed tosolve different non-linear, complex problems. This study made a fine tuning in the particle updating process ofstandard PSO algorithm. The updated algorithm is used to develop and optimize a reservoir release policy formonthly basis. The historical data of inflow to the dam/reservoir has categorized in three different category(High, medium and low). The problem formation has done on the basis of release and storage constraints. Theobjective function which was aimed to be minimized has considered as water deficit from the release. Monthlyreleases are taken as the main objective variables and are essentially control the water deficit of the process.The standard form of PSO then compared with the updated version and the results is analyzed by adoptingdifferent performance measuring indicators such as reliability, vulnerability and resilience. These performancemeasuring indices are calculated from the outcome of the simulation process by feeding the optimization modelwith the actual historical data of inflow. From the results of the simulation and the value of the indicators, thestudy shows updated PSO algorithm performs significantly better in optimizing reservoir releases policy.

AB - Particle swarm optimization is a very well-known method as it has a strong background in optimization filed tosolve different non-linear, complex problems. This study made a fine tuning in the particle updating process ofstandard PSO algorithm. The updated algorithm is used to develop and optimize a reservoir release policy formonthly basis. The historical data of inflow to the dam/reservoir has categorized in three different category(High, medium and low). The problem formation has done on the basis of release and storage constraints. Theobjective function which was aimed to be minimized has considered as water deficit from the release. Monthlyreleases are taken as the main objective variables and are essentially control the water deficit of the process.The standard form of PSO then compared with the updated version and the results is analyzed by adoptingdifferent performance measuring indicators such as reliability, vulnerability and resilience. These performancemeasuring indices are calculated from the outcome of the simulation process by feeding the optimization modelwith the actual historical data of inflow. From the results of the simulation and the value of the indicators, thestudy shows updated PSO algorithm performs significantly better in optimizing reservoir releases policy.

M3 - Paper

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