An Overview of the Applications of Particle Swarm in Water Resources Optimization

Cyriac, Rosemary; Rastogi, A. K.

doi:10.1007/978-81-322-1041-2_4

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…Adjustable parameters affect the performance in solving optimization problems with the PSO method [35,47], especially the parameter's inertial weight, which has a profound impact on both global search ability and local search ability [48,49]. This study tested this performance by changing the value of ω in Equation (11) (Figure 11).…”

Section: Discussionmentioning

confidence: 99%

Reservoir Operation Sequence- and Equity Principle-Based Multi-Objective Ecological Operation of Reservoir Group: A Case Study in a Basin of Northeast China

Shen

Wei

et al. 2022

Sustainability

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The sequence of reservoir operations has a profound influence on the regulation and storage capacity of reservoir groups to effectively utilise the natural water inflow and external water transfer in the basin, especially for reservoir groups with water supply tasks. This study establishes the reservoir operation sequence (ROS) of four reservoir group modes, aiming at national economic and ecological water consumption, constructs a model of ROS-based multi-objective ecological operation of the reservoir group, and uses the particle swarm optimisation (PSO) method to optimise the solution. Analysing the results of the three schemes in two scenarios at the Yinma River Basin (YRB) indicates that after the Central Jilin Water Supply Project is put into operation, not only will the production and living water be effectively improved, but also the ecological water in the basin. Then, we compared the optimisation results of different water supply sequences in series and parallel reservoirs, which illustrates that the ROS of the four modes formulated in this research is the optimal water supply sequence.

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Section: Discussionmentioning

confidence: 99%

Reservoir Operation Sequence- and Equity Principle-Based Multi-Objective Ecological Operation of Reservoir Group: A Case Study in a Basin of Northeast China

Shen

Wei

et al. 2022

Sustainability

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“…One study [4] proposed a particle swarm algorithm combined with the mutation operation method, and the particle swarm intelligence factor was added according to the actual problem constraints. One study [5] presented the basic concepts of Particle Swarm Optimization (PSO) and its successful application in the different areas of water resources optimization. Self-adapting inertia can obtain better Convergent effect with the change of Iterative algebra was proved in the study [6].…”

Section: Introductionmentioning

confidence: 99%

PID Controller Parameter Tuning Based on Improved Particle Swarm Optimization Algorithm

Miao¹,

Liu²,

Chen³

et al. 2015

Proceedings of the 2015 International Conference on Electrical, Electronics and Mechatronics

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Improved PSO algorithms combined with Simulated Annealing are proposed in this paper to solve the problems of the original PSO algorithm. After that three further improvements are proposed respectively from the parameters adjustment, the organization structure and evolution, and the topology structure. Four common test functions are used to test the optimizing performances of the improved PSO algorithms in the paper. The test results indicate that all the improved PSO algorithms have better performances compared with former PSO algorithms. Finally, the improved PSO algorithm is applied to PID parameter tuning, and the simulation experiment results prove that the improved PSO algorithm proposed in this paper is more feasible than other intelligent algorithms.

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Application of PSO algorithm in short-term optimization of reservoir operation

SaberChenari

Abghari

Tabari

2016

Environ Monit Assess

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Aiming at the problem that the conventional logging data processing method has low accuracy and large error for complex reservoirs, this paper presents a PSO-ELM algorithm based on particle swarm optimization for reservoir porosity prediction. The prediction model is established by the limit learning machine (ELM). The output weight of ELM is optimized by particle swarm optimization algorithm, and the upper and lower limits of the optimal prediction interval are obtained, and the advantages of ELM learning speed and generalization ability are fully utilized to forecast the reservoir porosity. The PSO-ELM algorithm is used to test the tuff sandstone reservoirs in a certain area, and the results are in good agreement with the core data.

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An Overview of the Applications of Particle Swarm in Water Resources Optimization

Cited by 7 publications

References 12 publications

Reservoir Operation Sequence- and Equity Principle-Based Multi-Objective Ecological Operation of Reservoir Group: A Case Study in a Basin of Northeast China

Reservoir Operation Sequence- and Equity Principle-Based Multi-Objective Ecological Operation of Reservoir Group: A Case Study in a Basin of Northeast China

PID Controller Parameter Tuning Based on Improved Particle Swarm Optimization Algorithm

Application of PSO algorithm in short-term optimization of reservoir operation

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