Pareto Front Analysis in Multi-Objective Optimal Water Resources Allocation Using the MOHO Algorithm

Document Type : Research Paper

Authors

Department of Civil Engineering, University of Qom, Qom, Iran.

10.22059/jwim.2026.401208.1254

Abstract

Water resources management in semi-arid regions is challenged by climate variability, supply–demand imbalances, and environmental constraints. In this study, the Multi-Objective Horse Optimization (MOHO) algorithm was developed to optimize the operation of the Garanqo Reservoir in northwestern Iran. Two main objectives were considered: (1) minimizing vulnerability caused by water shortages and (2) maximizing reliability in meeting downstream water demands. The performance of MOHO was compared with the reference Multi-Objective Genetic Algorithm (MOGA) using the Fonseca–Fleming test function. Results showed that MOHO achieved a well-distributed Pareto front and maintained solution diversity effectively. Analysis of the base period (1971–2000) revealed that vulnerability ranged from 15% to 36%, while reliability ranged from 29% to 70% across the set of Pareto-optimal solutions. The compromise zone was identified within vulnerability levels of 31–33% and reliability levels of 40–55%, highlighting the trade-off between the two conflicting objectives. Reservoir performance analysis also showed a significant temporal mismatch between natural inflows and human water demand, emphasizing the need for accurate release planning and storage management. Finally, several management strategies were proposed to reduce shortages, prevent spill losses, and enhance system resilience under variable hydrological conditions.

Keywords

Main Subjects

References

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Water and Irrigation Management
Volume 16, Issue 1
July 2026
Pages 163-177

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