Optimal Flood-Control Operation of Cascade Reservoirs Using an Improved Particle Swarm Optimization Algorithm

Abstract

Optimal reservoir operation is an important measure for ensuring flood-control safety and reducing disaster losses. The standard particle swarm optimization (PSO) algorithm can find the optimal solution of the problem by updating its position and speed, but it is easy to fall into a local optimum. In order to prevent the problem of precocious convergence, a novel simulated annealing particle swarm optimization (SAPSO) algorithm was proposed in this study, in which the Boltzmann equation from the simulated annealing algorithm was incorporated into the iterative process of the PSO algorithm. Within the maximum flood peak reduction criterion, the SAPSO algorithm was used into two floods in the Tianzhuang–Bashan cascade reservoir system. The results shown that: (1) There are lower maximum outflows. The maximum outflows of Tianzhuang reservoir using SAPSO algorithm decreased by 9.3% and 8.6%, respectively, compared with the measured values, and those of Bashan reservoir decreased by 18.5% and 13.5%, respectively; (2) there are also lower maximum water levels. The maximum water levels of Tianzhuang reservoir were 0.39 m and 0.45 m lower than the measured values, respectively, and those of Bashan reservoir were 0.06 m and 0.46 m lower, respectively; and (3) from the convergence processes, the SAPSO algorithm reduced the convergence speed in the early stage of convergence and provided a superior objective function value than PSO algorithm. At the same time, by comparing with GA algorithm, the performance and applicability of SAPSO algorithm in flood operation are discussed further. Thus, the optimal operation model and SAPSO algorithm proposed in this study provide a new approach to realizing the optimal flood-control operation of cascade reservoir systems.

On the Optimized Management of Activated Distributed Storage Systems: A Novel Approach to Flood Mitigation

“…Like GAs, the relative simplicity of PSO makes it ideal for use in multi-objective optimization. As a result, it has been used widely in both reservoir operation and design applications [32][33][34]. In a comparative analysis between GAs and PSO at a single dam, PSO outperformed the GAs in terms of solution accuracy, convergence rate, and run time to reach global optima [34].…”

A Fast-Convergent Hyperbolic Tangent PSO Algorithm for UAVs Path Planning

“…The second aspect is the use of the PSO algorithm in different areas, such as in the PSO is a problem-dependent algorithm with a probabilistic optimization method. In the last two decades, PSO has been used in numerous applications because of its potential for various practical optimization problems such as machine learning [10], power electronics [11], image processing [12], numerical problems, training neural networks [13], electrical engineering to solve optimization problems [14], communication theory [15], control system [16], multi-robot searching [17], UAVs swarm [18]- [19], localization in wireless sensor networks (WSNs) and tracking [20], energy storage optimization [14], evolving artificial neural networks [21], vehicle routing problems [22], medical image segmentation, intelligent diagnosis in health care [23], flood control monitoring [24], smart-grid and micro-grid application in industrial field [25] and traffic monitoring and scheduling and flexible and rapid deployment of unmanned aerial vehicles [26]. In general, the optimization algorithm has two stages: exploration and exploitation.…”

Utilizing the Sobol’ Sensitivity Analysis Method to Address the Multi-Objective Operation Model of Reservoirs

“…Some of these include linear programming [19], dynamic programming [20], network-based programming [21], the genetic algorithm (GA) [22], and particle swarm optimization (PSO) [23], among others. Despite their success across various domains, the broad adoption of these methods for intricate reservoir operations is hindered by issues like the "curse of dimensionality" [24], extensive computational demands [25], and the problem of premature convergence [26]. Current methods for modeling solutions lack efficiency in delivering top-notch ecological operation results.…”