Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies

Mahdi Valikhan-Anaraki,Md Shabbir Hossain,Mohammad Ehteram,Huzaifa Bin Hashim,Gasim Hayder,Chow Ming Fai,Sai Hin Lai,Hojat Karami,Amr H El-Shafie,Haitham Abdulmohsin Afan,Sayed-Farhad Mousavi,Ali Najah Ahmed,Saeed Farzin,Ahmed El-Shafie,Ozgur Kisi

doi:10.3390/su11082337

Abstract

One of the most important issues in the field of water resource management is the optimal utilization of dam reservoirs. In the current study, the optimal utilization of the Aydoghmoush Dam Reservoir is examined based on a hybrid of the bat algorithm (BA) and particle swarm optimization algorithm (PSOA) by increasing the convergence rate of the new hybrid algorithm (HA) without being trapped in the local optima. The main goal of the study was to reduce irrigation deficiencies downstream of this reservoir. The results showed that the HA reduced the computational time and increased the convergence rate. The average downstream irrigation demand over a 10-year period (1991–2000) was 25.12 × 106 m3, while the amount of water release based on the HA was 24.48 × 106 m3. Therefore, the HA was able to meet the irrigation demands better than some other evolutionary algorithms. Moreover, lower indices of root mean square error (RMSE) and mean absolute error (MAE) were obtained for the HA. In addition, a multicriteria decision-making model based on the vulnerability, reliability, and reversibility indices and the objective function performed better with the new HA than with the BA, PSOA, genetic algorithm (GA), and shark algorithm (SA) in terms of providing for downstream irrigation demands.

Highlights

Continuous droughts and climate change make optimal operations of existing water resource systems important, especially when there is a shortage of resources and increased demands [1]
The results showed that bat algorithm (BA) could increase the profit from energy production by 20%, 25%, and 30% compared to the genetic algorithm (GA), particle swarm optimization algorithm (PSOA), and harmonic search algorithm (HSA), respectively
A new hybrid algorithm (HA) combining the BA and PSOA is introduced for optimal operation of the Aydoghmoush Dam Reservoir to meet downstream irrigation demands and reduce irrigation deficiencies

Summary

Introduction

Continuous droughts and climate change make optimal operations of existing water resource systems important, especially when there is a shortage of resources and increased demands [1]. Boluri-Yazdeli et al [4] used the genetic algorithm (GA) along with operating rule curves for planning and managing reservoir water resources, with the aim to reduce downstream irrigation deficiencies. Bozorg-Haddad et al [5] used a biogeography-based algorithm to reduce the hydrologic deficiencies of a power plant. This algorithm performs based on the immigration of biological species. Genetic programming (GP) was used to plan and manage water resources and increase the energy production of a power plant [6]. The results showed that BA could increase the profit from energy production by 20%, 25%, and 30% compared to the GA, PSOA, and harmonic search algorithm (HSA), respectively. The initial location of bats was considered to be a decision variable, and the sound ability of bats was used for the study

Methods

Results

Conclusion