Evolutionary techniques versus swarm intelligences: Application in reservoir release optimization

M. S. Hossain, A. El-Shafie

    Research output: Contribution to journalArticle

    12 Citations (Scopus)

    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 languageEnglish
    Pages (from-to)1583-1594
    Number of pages12
    JournalNeural Computing and Applications
    Volume24
    Issue number7-8
    DOIs
    Publication statusPublished - 2014

    Fingerprint

    Particle swarm optimization (PSO)
    Genetic algorithms
    Risk analysis
    Swarm intelligence
    Water

    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 journalArticle

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