@article{MAKHILLJEAS2016111013935,
    title = {A Reservoir Release Optimization-Simulation Model
Using Particle Swarm Optimization (PSO) Algorithm},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {11},
    number = {10},
    pages = {2186-2192},
    year = {2016},
    issn = {1816-949x},
    doi = {jeasci.2016.2186.2192},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2016.2186.2192},
    author = {S.},
    keywords = {Reservoir release optimization,risk analysis,PSO,GA,high},
    abstract = {In a reservoir operation model, it is very important to check the efficiency by means of some
performances measuring indexes. Risk analysis of this kind of optimization-simulation model may consist -
reliability, vulnerability and resiliency of the model. These basic performance measuring indices are analyzed
in this study. A Particle Swarm Optimization (PSO) algorithm is used to minimize the water deficit of a reservoir
system. Also another well-established optimization technique, Genetic Algorithm (GA) has used to compare
the results. Inflow patterns are categorized into three different situations (high, medium and low) to construct
optimum release curves for every month. The release curves, constructed for a particular month indicates the
amount of water release for a known storage condition. After constructing the release policy, simulation has
done with historical inflow data. The simulation results showed that the PSO provide better results in terms of
reliability analysis of the model. Also, it can handle the critical situation of low inflow more efficiently than GA
optimization technique.}
    }