Optimal allocation of regional water resources based on simulated annealing particle swarm optimization algorithm

Wang, Zhanping; Tian, Juncang; Kong, Feng

doi:10.1016/j.egyr.2022.07.033

Cited by 32 publications

(15 citation statements)

References 17 publications

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“…The simulated annealing (SA) algorithm is a versatile global optimization algorithm that works by exploring the solution space through random walks to find the best possible solution [ 22 , 23 ]. Two main processes are involved in the SA algorithm: the Metropolis algorithm and annealing process.…”

Section: Methodsmentioning

confidence: 99%

Multi-Parameter Auto-Tuning Algorithm for Mass Spectrometer Based on Improved Particle Swarm Optimization

Jia,

Li,

Xiong

et al. 2023

Bioengineering

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Quadrupole mass spectrometers (QMS) are widely used for clinical diagnosis and chemical analysis. To obtain the best experimental results, mass spectrometers must be calibrated to an ideal setting before use. However, tuning the current QMS is challenging. Traditional tuning techniques possess low automation levels and rely primarily on skilled engineers. Therefore, in this study, we propose an innovative auto-tuning algorithm for QMS based on the improved particle swarm optimization (PSO) algorithm to automatically find the optimal solution of QMS parameters and make the QMS reach the optimal state. The improved PSO algorithm is combined with simulated annealing, multiple inertia weights, dynamic boundaries, and other methods to prevent the traditional PSO algorithm from the issue of a local optimal solution and premature convergence. According to the characteristics of the mass spectrum peaks, a termination function is proposed to simplify the termination conditions of the PSO algorithm and further improve the automation level of the mass spectrometer. The results of auto-calibration testing of resolution and mass axis show that both resolution and mass axis calibration could effectively meet the requirements of mass spectrometry experiments. By the experiment of auto-optimization testing of lens and ion source parameters, these parameters were all in the vicinity of the optimal solution, which achieved the expected performance. Through numerous experiments, the reproducibility of the algorithm was established as meeting the auto-tuning function of the QMS. The proposed method can automatically tune the mass spectrometer from its non-optimal condition to the optimal one, which can effectively reduce the tuning difficulty of QMS.

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Section: Methodsmentioning

confidence: 99%

Multi-Parameter Auto-Tuning Algorithm for Mass Spectrometer Based on Improved Particle Swarm Optimization

Jia,

Li,

Xiong

et al. 2023

Bioengineering

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show abstract

“…For constructed buildings, Liu et al employed the simulated annealing approach to optimize the component assembly order [22]. In order to achieve the sustainable use of regional water resources, Wang et al employed the simulated annealing approach to optimize the distribution of water resources in the area [23]. The simulated annealing approach, on the other hand, can leave the local optimum by obtaining the likelihood of deteriorating solutions.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Quality Evaluation Approach for Prefabricated Buildings Using Ant Colony Algorithm and Simulated Annealing Algorithm to Optimize the Projection Pursuit Model

Wang,

Xu,

Ding

et al. 2023

Buildings

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There are problems with an inadequate quality assurance system and non-standard construction organization and administration while creating prefabricated buildings. There are currently fewer quality assessments employing prefabricated component combinations as the research focus, and the quality evaluation methodology is more subjective. We propose a method for evaluating the quality of prefabricated buildings using an ant colony algorithm and a simulated annealing algorithm to optimize the projection pursuit model: firstly, create a prefabricated building quality index system; secondly, questionnaires were distributed, tested for reliability and validity to avoid the influence of questionnaire subjectivity on the results, and structural equation modeling was used to calculate the weights of the quality influencing factors; thirdly, quantify the quality factors of prefabricated components by using the quality function development method, and construct a quality optimization model for the prefabricated component combinations; fourthly, use the ant colony algorithm to solve the quality optimization model to obtain a set of prefabricated component combinations to satisfy the quality requirements; and lastly, use a simulated annealing to optimize the projected pursuit method for evaluating the quality of prefabricated component combination solutions. The results show that (1) The use of optimization algorithms can successfully avoid the issue of a more subjective evaluation approach and increase the efficiency and accuracy of evaluation. (2) Residential Comfort (RC), Usage Durability (UD) and Structural Reliability (SR) have a substantially negative association, but Residential Comfort (RC) and Installation Stability (IS) have strong positive correlations. (3) Based on the magnitude of the vector of the ideal projection direction of the quality indicators, it was determined that the Installation Stability (IS) indicator had the greatest influence on the evaluation of the program, and the Structural Reliability (SR) indicator had the least influence on the program.

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“…The optimal control solution based on the PSO algorithm was proposed, which can be applied to save electrical energy. Wang et al (2022) used the PSO algorithm to optimize regional water resource allocation in Yinchuan city of Ningxia, China. The allocation schemes of water resources are simulated in three different precipitation scenarios in 2025.…”

Section: Introductionmentioning

confidence: 99%

Comparison of improved relevance vector machines for streamflow predictions

Adnan,

Mostafa,

Dai

et al. 2023

Journal of Forecasting

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This study investigates the feasibility of relevance vector machine tuned with dwarf mongoose optimization algorithm in modeling monthly streamflow. The proposed method is compared with relevance vector machines tuned by particle swarm optimization, whale optimization, marine predators algorithms, and single relevance vector machine methods. Various lagged values of hydroclimatic data (e.g., precipitation, temperature, and streamflow) are used as inputs to the models. The relevance vector machine tuned with dwarf mongoose optimization algorithm improved the efficiency of single method in monthly streamflow prediction. It is found that the integrating metaheuristic algorithms into single relevance vector machine improves the prediction efficiency, and among the input combinations, the lagged streamflow data are found to be the most effective variable on current streamflow whereas precipitation has the least effect.

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Optimal allocation of regional water resources based on simulated annealing particle swarm optimization algorithm

Cited by 32 publications

References 17 publications

Multi-Parameter Auto-Tuning Algorithm for Mass Spectrometer Based on Improved Particle Swarm Optimization

Multi-Parameter Auto-Tuning Algorithm for Mass Spectrometer Based on Improved Particle Swarm Optimization

Quality Evaluation Approach for Prefabricated Buildings Using Ant Colony Algorithm and Simulated Annealing Algorithm to Optimize the Projection Pursuit Model

Comparison of improved relevance vector machines for streamflow predictions

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