Multiple of Hybrid Lambda Iteration and Simulated Annealing Algorithm to Solve Economic Dispatch Problem with Ramp Rate Limit and Prohibited Operating Zones

Takeang, Chiraphon; Aurasopon, Apinan

doi:10.1007/s42835-018-00001-z

Cited by 26 publications

(10 citation statements)

References 19 publications

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“…e simulated annealing algorithm can accept the deteriorating solution to a certain extent and accept the trial points that make the objective function value worse. e simulated annealing algorithm uses implicit parallelism algorithm, which is suitable for searching complex regions [16]. Only using the value of the objective function for optimization calculation can avoid the limitation of continuous differentiability.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Application of Artificial Intelligence in the Process of Ecological Water Environment Governance and Its Impact on Economic Growth

Wei

2021

Mathematical Problems in Engineering

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With the increasing pollution of the ecological water environment, the treatment of the ecological water environment has become the focus of everyone’s attention. At present, there are many research results on water environment governance, but the effect is not ideal. In order to effectively control the ecological water environment and promote sustainable economic growth, this research combines artificial intelligence algorithms and applies them to the governance process to explore its application effects and its impact on economic growth. First, the environmental sensor of the corresponding module is designed according to the water environment factor, and the data of dissolved oxygen content, water temperature, turbidity, temperature and humidity, and smoke concentration in the water environment are collected. Then the dynamic time-varying exponential smoothing prediction method is used to predict water quality, and a water quality prediction model is established. Then use support vector machine (SVM) to train the collected data samples, use the decision tree-based SVM classification method to classify the data samples, establish a water quality evaluation model, and use particle swarm optimization algorithm to optimize the evaluation model. Put the sensors and predictive evaluation models established in this research design into the governance of a certain river reach, and collect relevant data from 7 : 00 to 18 : 00 on October 11, 2019. And predict and evaluate its water quality. The experimental results show that the average absolute error of predicting dissolved oxygen content is 0.97%, and the average absolute error of predicting phosphorus content is 2.27%. This shows that the application of artificial intelligence algorithms in the process of ecological water environmental governance can effectively help collect effective information and make more accurate predictions and evaluations of water quality, thereby improving governance efficiency and promoting sustainable economic growth.

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Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Application of Artificial Intelligence in the Process of Ecological Water Environment Governance and Its Impact on Economic Growth

Wei

2021

Mathematical Problems in Engineering

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“…It makes up for the defect that the forward search algorithm and backward search algorithm cannot jump out of the local optimal value. e random algorithms commonly used in subset search are randomly generated sequence selection algorithm [18], simulated annealing algorithm [19], and genetic algorithm [20].…”

Section: Feature Selection Of Financialmentioning

confidence: 99%

Intelligent Prediction Mathematical Model of Industrial Financial Fraud Based on Data Mining

Geng

2021

Mathematical Problems in Engineering

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The essence of enterprise financial modeling is to use mathematical models to classify and sort out all kinds of enterprise information according to the main line of value creation and on this basis to complete the analysis, prediction, and value evaluation of enterprise financial situation. A reasonable financial model is also an effective means to reduce financial fraud. In this paper, a financial fraud identification model is constructed based on empirical data. In the process of model construction, the primary feature set is selected according to the financial fraud motivation theory, and then, the original feature set is obtained by Mann–Whitney test on the primary feature set, and the final fraud identification feature set is selected from the original feature set by using Relief and Boruta algorithms. Finally, based on the final fraud identification feature set, the data algorithms such as decision tree, logistic regression, support vector machine, and random forest are used to identify financial fraud. The experimental results show that the combination of financial fraud identification features constructed by the Relief algorithm and random forest model has the best recognition effect. The evaluation indexes of the G mean value and the F value were 75.86% and 78.33%, respectively.

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“…Several classical approaches in ELD optimization have been proposed by several researchers. Takeang and Aurasopon [6] conducted a study using a hybrid method combining lambda iteration and Int J Artif Intell ISSN: 2252-8938  simulation annealing method (MHLSA) to solve the economic delivery (ED) problem with the characteristics of a smooth cost function. Chauhan [7] used the lambda iteration method to solve the economical load dispatch problem for 6 generating unit frames with and without transmission loss.…”

Section: Introductionmentioning

confidence: 99%

A hunger game search algorithm for economic load dispatch

Aribowo

Muslim

Suprianto

2022

IJ-AI

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This work proposes a new approach to solve the economic load dispatch (ELD) issue in power systems by metaheuristic algorithms inspired by natural life. The problem to be resolved is to optimize the power system network with various constraints by considering the cutting in the cost of the resulting in the transmission of the electric system. The method used in this study is the hunger games search (HGS). This method duplicates the hungerdriven activity and the animal's choice of behavior. The proposed method is to add the concept of starvation as a process structure. Adaptive weights based on the concept of hunger are designed and used to simulate the effects of hunger on each trace process. To get the performance of the proposed method, this research uses mathematical methods, particle swarm optimization (PSO), differential evolution (DE), giza pyramids construction (GPC), and sine tree-seed algorithm (STSA) as a comparison. This study uses 2 case studies. In case study 1, the proposed method has a 0.16% better cost of generation than the mathematical method. Comparison of the HGS method with the PSO method in the second case study, it was found that the HGS method was 0.018% better than the PSO. From the research, it was found that the HGS method was superior.

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Multiple of Hybrid Lambda Iteration and Simulated Annealing Algorithm to Solve Economic Dispatch Problem with Ramp Rate Limit and Prohibited Operating Zones

Cited by 26 publications

References 19 publications

Application of Artificial Intelligence in the Process of Ecological Water Environment Governance and Its Impact on Economic Growth

Application of Artificial Intelligence in the Process of Ecological Water Environment Governance and Its Impact on Economic Growth

Intelligent Prediction Mathematical Model of Industrial Financial Fraud Based on Data Mining

A hunger game search algorithm for economic load dispatch

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