Assessment of water resources management strategy under different evolutionary optimization techniques

Jafar Y. Al-Jawad, Robert M. Kalin

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
25 Downloads (Pure)


Competitive optimization techniques have been developed to address the complexity of integrated water resources management (IWRM) modelling; however, model adaptation due to changing environments is still a challenge. In this paper we employ multi-variable techniques to increase confidence in model-driven decision-making scenarios. Here, water reservoir management was assessed using two evolutionary algorithm (EA) techniques, the epsilon-dominance-driven self-adaptive evolutionary algorithm (∈-DSEA) and the Borg multi-objective evolutionary algorithm (MOEA). Many objective scenarios were evaluated to manage flood risk, hydropower generation, water supply, and release sequences over three decades. Computationally, the ∈-DSEA's results are generally reliable, robust, effective and efficient when compared directly with the Borg MOEA but both provide decision support model outputs of value.

Original languageEnglish
Article number2021
Number of pages23
Issue number10
Publication statusPublished - 28 Sep 2019


  • self-adaptive technique
  • many-objective
  • multi-variable
  • reservoir operation strategy


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