Authors:
Ghasaq Saadoon Mutar,Lariyah Bte Mohd Sidek,Saad T. Y. Alfalahi,Hidayah Bte Basri,Mahmoud Saleh,Jamal O. Sameer,DOI NO:
https://doi.org/10.26782/jmcms.2025.10.00010Keywords:
Charged System Search (CSS),Reservoir Management,Optimization Algorithms,Sediment Accumulation,Multi-objective Modeling,Abstract
The growing complexity of reservoir management driven by hydrological uncertainty and sedimentation has led to increased reliance on advanced optimization techniques. This review critically examines the recent application of the Charged System Search (CSS) algorithm in addressing nonlinear, multi-constraint challenges within water resource systems. Across the literature, CSS is recognized as an effective method for optimizing operations related to hydropower, water supply, and sediment management. Most studies adopt scenario-based modeling with stochastic hydrological inputs to enhance system resilience under climate variability. A key distinction among studies lies in algorithm customization. While some apply standard CSS, others hybridize it with techniques like Particle Swarm Optimization (PSO), Sequential Quadratic Programming (SQP), and Genetic Algorithms (GA) to improve convergence and solution quality. Comparisons with other metaheuristics such as Differential Evolution (DE), Ant Colony Optimization (ACO), and NSGA-II further contextualize CSS’s relative performance. The reviewed works vary in modeling scale and objectives: some aim to maximize water or energy yield, others to minimize sedimentation or manage operational trade-offs. Models span single- and multi-reservoir systems, with temporal scopes ranging from short-term operations to long-term sediment dynamics. Implementation environments include MATLAB, Python, and specialized hydrological platforms, reflecting methodological diversity. Additionally, researchers employ both single- and multi-objective optimization, often utilizing Pareto fronts for trade-off analysis. By synthesizing these methodological trends and algorithmic adaptations, the review underscores CSS’s flexibility and effectiveness within metaheuristic-based reservoir optimization. However, it also identifies key limitations, including a lack of standardization, minimal real-world application, and weak integration with real-time forecasting tools. The paper concludes with suggestions for future research aimed at enhancing computational efficiency, operational relevance, and decision-support capability in the context of increasing water resource challenges under climate change.Refference:
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