Evaluation of Whale, Fruit Fly and Cuckoo Search Algorithms in Optimizing Multi-Objective Operation of Golestan Dam Reservoir Based on Multi-Criteria Decision-Making Method

Document Type : Research Paper

Authors

1 Civil Engineering Department, Roudehen Branch, Islamic Azad University, Roudehen, Iran

2 Department of Civil Engineering, Roudehen Branch, Islamic Azad university, Roudehen, Iran

3 Department of civil engineering, islamic azad university, roudehen branch, roudehen, tehran

Abstract

In this research, after introducing the Whale, Cuckoo search and fruit fly Multi-Objective Optimization Algorithms, their performance individually and compared to each other was evaluated to optimize the Golestan Dam reservoir operation policies with an approach to reduce downstream water demand shortages and flood storage volume management based on reliability, resilience, vulnerability and sustainability criteria. The results showed that the proposed algorithms act differently in optimizing the objective functions. So that the whale multi-objective optimization algorithm leads to better values of objective functions. Regarding to the optimization time, although, there are no significant differences between the cuckoo search and whale algorithms, both perform far better than the fruit fly algorithm and achieve much more convergence over time. When comparing the whale algorithm, as the most efficient algorithm, with the fruit fly algorithm, as the most inefficient algorithm, the model reliability and resilience indices increased by 44% and 52%, respectively, and its vulnerability decreased by 23%, indicating a better performance compared to the other algorithms. Therefore, Whale multi-objective optimization algorithm performs better and converges better than the other algorithms, and cuckoo search and fruit fly multi-objective algorithms ranked second and third.

Keywords

References

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Water Resources Engineering
Volume 13, Issue 47
January 2021
Pages 85-100

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History

  • Receive Date: 28 December 2019
  • Revise Date: 19 August 2020
  • Accept Date: 06 January 2021

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