System performances analysis of reservoir optimization–simulation model in application of artificial bee colony algorithm

M. S. Hossain, A. El-Shafie, M. S. Mahzabin, M. H. Zawawi

Research output: Contribution to journalArticle

5 Citations (Scopus)


In reservoir system operation, optimization is very much essential and the compatibility of different optimization techniques is essential to be checked by some performance checking indices. In this study, various types of performance-measuring index are used and compared to provide a complete knowledge on adopting different approaches. Here, the considered performance-measuring indicators will check the operation policy in terms of three different scenarios—how the method is efficient in achieving best results (reliability); how vulnerable the method is for different critical situation (vulnerability); and how capable it is to handle a failure of the model (resiliency). Therefore, the study proposed the artificial bee colony (ABC) optimization technique to develop an optimal water release policy for the well-known Aswan High Dam, Egypt. Particle swarm optimization, genetic algorithm and neural network-based stochastic dynamic programming are also used in a view of comparing model performances. A release curve is developed for every month as a guidance to the decision maker. Simulation has been done for each method using historical actual inflow data, and reliability, resiliency and vulnerability are measured. All model indicators proved that the release policy provided by ABC optimization outperforms in terms of achieving minimum water deficit, less waste of water and handling critical situations.

Original languageEnglish
Pages (from-to)2101-2112
Number of pages12
JournalNeural Computing and Applications
Issue number7
Publication statusPublished - 01 Oct 2018


All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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