Reservoir systems serve as a prevalent mechanism for the control and management of water resources. Given the constraints of limited resources and the escalating demands for water, it is imperative that these systems are operated optimally to enhance the efficiency of water utilization. Despite advancements in addressing real-world challenges, classical optimization methods frequently fall short of delivering optimal solutions due to the structural complexity and the multitude of variables involved. As a result, there exists an urgent need for more effective and robust methodologies to address these challenges. Meta-heuristic algorithms, particularly those inspired by biological evolution and referred to as evolutionary computation, represent reliable and straightforward approaches for tackling complex optimization problems, positioning themselves as viable alternatives to traditional optimization techniques. Evolutionary computation can be classified into two primary categories: evolution strategies and swarm intelligence. While meta-heuristic algorithms based on swarm intelligence are characterized as multi-agent systems that emulate individual behaviors, those grounded in evolution strategies employ adaptive search mechanisms derived from evolutionary processes. This research aims to quantify the uncertainty associated with meta-heuristic algorithms and to evaluate their efficacy in the planning and management of water resources, specifically for the optimal operation of a single reservoir. The study assesses 101 evolutionary algorithms, categorized into eight groups, with a focus on their application in optimizing reservoir system operations to enhance efficiency. The case study centers on the Gheshlagh Reservoir located in Kurdistan, Iran. A comparative analysis of the performance of these algorithms revealed that the SHADE algorithm outperformed its counterparts, achieving a minimum objective function value of 9.59 × 10−10 and demonstrating superior computational speed. Notably, SHADE attained a demand deficit of zero million cubic meters for the reservoir, whereas the FOA algorithm recorded the highest deficit of 10.74 million cubic meters. Furthermore, DE class algorithms exhibited the highest overall performance in the operation of the Gheshlagh Reservoir, showcasing reduced computation times, enhanced robustness, and improved decision-making capabilities. The study underscores the significance of algorithmic structure and problem type in determining performance outcomes, recommending the adoption of SHADE or DE class algorithms for the formulation of operational policies in complex reservoir systems. These findings provide valuable insights for researchers seeking to introduce new or modified algorithms and offer guidance to administrators in selecting the most appropriate algorithm based on specific operational requirements.
扫码关注我们
求助内容:
应助结果提醒方式:
