Severe water scarcity in recent years has magnified the economic, social, and environmental significance of water stress globally, making optimal planning in water resources necessary for sustainable socio-economic development. One of the regions that is most affected by this is the Sistan region and its Hamoun wetland, located in south-east Iran. Water policies are essential to sustain current basin ecosystem services, maintaining a balance between conflicting demands from agriculture and the protection of wetland ecosystems. In the present study, a multi-objective optimization model is linked with the Water Evaluation and Planning (WEAP) software to optimize water allocation decisions over multiple years. We formulate and parameterize a multi-objective optimization problem where the net economic benefit from agriculture and the supply of environmental requirements were maximized, to analyze the trade-off between different stakeholders. This problem is modeled and solved for the study area with detailed agricultural, socio-economic, and environmental data for 30 years and quantification of ecosystem services. By plotting Pareto sets, we investigate the trade-offs between the two conflicting objectives and evaluate a possible compromise. The results are analyzed by comparing purely economic versus multi-objective scenarios on the Pareto front. Finally, the disadvantages and advantages of these scenarios are also qualitatively described to help the decision process for water resources managers.

This study investigates the optimal and safe operation of pumping stations in water distribution systems (WDSs) with the aim of reducing the environmental footprint of water conveyance processes. We introduced the nonlinear chaotic honey badger algorithm (NCHBA), a novel and robust optimization method. The proposed method utilizes chaotic maps to enhance exploration and convergence speed, incorporating a nonlinear control parameter to effectively balance local and global search dynamics. Single-objective optimization results on a WDS show that NCHBA outperforms other algorithms in solution accuracy and convergence speed. The application of the proposed approach on a water network with two variable-speed pumps demonstrated a significant 27% reduction in energy consumption. Expanding our focus to the multi-objective optimization of pump scheduling programs in large-scale water distribution systems (WDSs), we employ the non-dominated sorting nonlinear chaotic honey badger algorithm (MONCHBA). The findings reveal that the use of variable-speed pumps not only enhances energy efficiency but also bolsters WDS reliability compared to the use of single-speed pumps. The results showcase the potential and robustness of the proposed multi-objective NCHBA in achieving an optimal Pareto front that effectively balances energy consumption, pressure levels, and water quality risk, facilitating carbon footprint reduction and sustainable management of WDSs.