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Performance evaluation and comparison of commonly used optimization algorithms for natural gas liquefaction processes

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ENERGY REPORTS 2022年 8卷 4787-4800页

作者： Sun, Heng Geng, Jinliang Na, Fengyi Rong, Guangxin Wang, Chao China Univ Petr Natl Engn Lab Oil & Gas Pipeline Transmiss Safety Beijing Key Lab Urban Oil & Gas Transmiss & Distr Beijing 102249 Peoples R China

The genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and BOX algorithm have been used in natural gas liquefaction process optimization. Three algorithms all can find a solution by adopting different strategies and computational efforts. Therefore, it is necessary to compare their performance. This article presents a performance comparison of the GA, PSO, and BOX algorithms for the optimization of four natural gas mixed refrigerant liquefaction processes. The results show that PSO has the best optimization performance, reducing the specific energy consumption (SEC) to 0.3233 kWh/kg, 0.2351 kWh/kg, 0.2489 kWh/kg, and 0.2427 kWh/kg for single mixed refrigerant (SMR), dual mixed refrigerant (DMR), propane pre-cooling mixed refrigerant (C3MR), and mixed fluid cascade (MFC), respectively. Furthermore, PSO also improved the exergy efficiency of the four processes to 35.34%, 48.59%, 45.90%, and 47.07%. The composite curve analysis shows that the heat transfer efficiency of the heat exchanger optimized by PSO is more efficient. The study also discovered that the total optimization performance of PSO and GA is better than BOX algorithm, and the GA optimization performance is second only to PSO. This research would greatly assist process engineers in making the right decision on process optimization to overcome energy efficiency challenges. (c) 2022 The Authors. Published by Elsevier Ltd.

关键词： Mixed refrigerant process Specific energy consumption optimization algorithm algorithm comparison

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An efficient spread-based evolutionary algorithm for solving dynamic multi-objective optimization problems

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JOURNAL OF COMBINATORIAL optimization 2022年第1期44卷 794-849页

作者： Falahiazar, Alireza Sharifi, Arash Seydi, Vahid Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Comp Engn South Tehran Branch Tehran Iran

Dynamic multi-objective optimization algorithms are used as powerful methods for solving many problems worldwide. Diversity, convergence, and adaptation to environment changes are three of the most important factors that dynamic multi-objective optimization algorithms try to improve. These factors are functions of exploration, exploitation, selection and adaptation operators. Thus, effective operators should be employed to achieve a robust dynamic optimization algorithm. The algorithm presented in this study is known as spread-based dynamic multi-objective algorithm (SBDMOA) that uses bi-directional mutation and convex crossover operators to exploit and explore the search space. The selection operator of the proposed algorithm is inspired by the spread metric to maximize diversity. When the environment changed, the proposed algorithm removes the dominated solutions and mutated all the non-dominated solutions for adaptation to the new environment. Then the selection operator is used to select desirable solutions from the population of non-dominated and mutated solutions. Generational distance, spread, and hypervolume metrics are employed to evaluate the convergence and diversity of solutions. The overall performance of the proposed algorithm is evaluated and investigated on FDA, DMOP, JY, and the heating optimization problem, by comparing it with the DNSGAII, MOEA/D-SV, DBOEA, KPEA, D-MOPSO, KT-DMOEA, Tr-DMOEA and PBDMO algorithms. Empirical results demonstrate the superiority of the proposed algorithm in comparison to other state-of-the-art algorithms.

关键词： Dynamic multi-objective optimization problems Evolutionary algorithm Heating optimization optimization algorithm Spread-based

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optimization algorithm for Home Energy Management System Based on Artificial Bee Colony in Smart Grid

Optimization Algorithm for Home Energy Management System Bas...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Yanyu Zhang Peng Zeng Chuanzhi Zang Key Lab. of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479987313

To implement demand response in residential sector and facilitate the integration of renewable resources and plug-in electric vehicles in future smart grid, this paper proposes a framework of home energy management system (HEMS) and a optimization algorithm for it based on improved artificial bee colony. The algorithm schedules the operations of schedulable home appliances according to electricity price, forecasted outdoor temperature and renewable power output, and user preferences to minimize user's electricity cost. The effectiveness of the algorithm is verified by simulations, and the electricity cost can be reduced by 47.76%.

关键词： Smart grid Demand response Home energy management system Artificial bee colony optimization algorithm optimization algorithms Smart grids Demand-Responsive bee colonies Energy management systems Domestic architecture electricity costs Bee User preferences

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Transport Models and optimization algorithms for Rechargeable Batteries

Transport Models and Optimization Algorithms for Rechargeabl...

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作者： Deng, Changyu University of Michigan

学位级别：Ph.D., Doctor of Philosophy

Rechargeable batteries, such as lithium-ion and sodium-ion batteries, are gaining much attention due to high demand in electronics, electric vehicles, and energy storage. An essential task of battery research and development is to describe the mechanisms by models and optimize the parameters for best performance. In the dissertation, we focus on improving battery models and developing optimization algorithms. Batteries are complicated systems with numerous properties and parameters. We improved model accuracy via obtaining appropriate parameters. Special effort is made to achieve consistency among different measurement techniques when obtaining the electrode particle diffusivity. It has puzzled the community for a long time that two common techniques yield quite different results. We thoroughly analyzed the error of these methods to explain the discrepancy and provide the conditions for accurate measurement. In contrast to lithium-ion batteries, the research of solid-state battery models is relatively sparse. The physics in solid-state electrolyte is different from liquid electrolyte, thus a continuum model is formulated to describe the transport of lithium ions and electrons in solid-state electrolyte. Our model can directly correlate experiment measurement with physical properties. Battery optimization is challenging due to the high cost and long cycling time to evaluate the performance of a battery. It is then vital to design efficient optimization algorithms targeting those features. Two types of algorithms have been investigated to find the optimal parameters in the models. The first algorithm tries to deal with the long cycling issue. A battery cycling optimization framework leveraging recent advances in machine learning is introduced. It allows parallel cyclers, stops unpromising cycles, and automatically yields new configurations of parameters. We show 75% time reduction in our test. The second algorithm tries to search for electrode patterns with higher ene

关键词： Lithium-ion battery Modeling Transport optimization algorithm

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Enhanced slime mould algorithm with multiple mutation strategy and restart mechanism for global optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期42卷 5069-5083页

作者： Zheng, Rong Jia, Heming Wang, Shuang Liu, Qingxin Sanming Univ Coll Informat Engn Sanming Fujian Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou Hainan Peoples R China

Slime mould algorithm (SMA) is a new metaheuristic algorithm proposed in 2020, which has attracted extensive attention from scholars. Similar to other optimization algorithms, SMA also has the drawbacks of slow convergence rate and being trapped in local optimum at times. Therefore, the enhanced SMA named as ESMA is presented in this paper for solving global optimization problems. Two effective methods composed of multiple mutation strategy (MMS) and restart mechanism (RM) are embedded into the original SMA. MMS is utilized to increase the population diversity, and the RM is used to avoid the local optimum. To verify the ESMA's performance, twenty-three classical benchmark functions are employed, as well as three well-known engineering design problems, including welded beam design, pressure vessel design and speed reducer design. Several famous optimization algorithms are also chosen for comparison. Experimental results show that the ESMA outperforms other optimization algorithms in most of the test functions with faster convergence speed and higher solution accuracy, which indicates the merits of proposed ESMA. The results of Wilcoxon signed-rank test also reveal that ESMA is significant superior to other comparative optimization algorithms. Moreover, the results of three constrained engineering design problems demonstrate that ESMA is better than comparative algorithms.

关键词： Slime mould algorithm multiple mutation strategy restart mechanism global optimization optimization algorithm

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Three-learning strategy particle swarm algorithm for global optimization problems

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INFORMATION SCIENCES 2022年 593卷 289-313页

作者： Zhang, Xinming Lin, Qiuying Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Engn Lab Intelligence Business & Internet Things Xinxiang 453007 Henan Peoples R China

Social Learning Particle Swarm optimization (SL-PSO) greatly improves the optimization performance of PSO. In solving complex optimization problems, however, it still has some deficiencies, such as poor search ability and low search efficiency. Hence, an improved SL-PSO, namely, Three-Learning Strategy PSO (TLS-PSO) is proposed in this paper. Firstly, a med-point-example learning strategy and a random learning strategy are proposed to replace the imitation component and social influence component of SL-PSO to enhance the exploitation and exploration, respectively. Secondly, the two learning strategies are combined cleverly into an updating equation to balance exploration and exploitation. Finally, a worst-best example learning strategy is merged skillfully to construct TLS-PSO with hybrid learning mechanism and further enhance the search ability. The experimental results on the complex functions from CEC2013 and CEC2017 test sets indicate that TLS-PSO has better performance compared with state-of-the-art PSO variants and other algorithms. For example, TLS-PSO has an advantage over SL-PSO on 50 of the 56 functions from CEC2013, its running time is less than SL-PSO's and it has higher search efficiency. Simulation results on the 10 engineering problems also show that TLS-PSO outperforms 7 excellent algorithms, such as IUDE and iLSHADE,. It is expected to solve practical problems better. (C) 2022 Elsevier Inc. All rights reserved.

关键词： optimization algorithm Swarm intelligence optimization algorithm Particle swarm optimization Learning strategy Global optimization problems

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Evaluation of the Efficiency of the optimization algorithms for Transfer Learning on the Rice Leaf Disease Dataset

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第10期13卷 83-91页

作者： Quach, Luyl-Da Quoc, Khang Nguyen Quynh, Anh Nguyen Ngoc, Hoang Tran FPT Univ Cantho City Vietnam

To improve the model's efficiency, people use many different methods, including the Transfer Learning algorithm, to improve the efficiency of recognition and classification of image data. The study was carried out to combine optimization algorithms with the Transfer Learning model with MobileNet, MobileNetV2, InceptionV3, Xception, ResNet50V2, DenseNet201 models. Then, testing on rice disease data set with 13.186 images, background removed. The result obtained with high accuracy is the RMSprop algorithm, with an accuracy of 88% when combined with the Xception model, similar to the F1, Xception model, and ResNet50V2 score of 87% when combined with the Adam algorithm. This shows the effect of gradients on the Transition learning model. Research, evaluate and draw the optimal model to build a website to identify diseases on rice leaves, with the main functions including images and recording of disease identification points for better management purposes on diseased areas of rice.

关键词： optimization algorithm transfer learning RMSprop rice leaf disease Adam

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Particle Swarm optimization-Extreme Learning Machine Model Combined With the Gradient Boost algorithm for Short-Term Wind Power Prediction

Particle Swarm Optimization-Extreme Learning Machine Model C...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Ponkumar, G. Jayaprakash, S. Hemkumar, P. School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India Panimalar Engineering College Chennai India

ISBN: (纸本)9798350325188

Accurate wind power assesses are fundamental to maximizing the use of wind power and defend a safe and reliable electricity grid. Gradient boosting algorithm with a limit learning machine optimized using particle swarm optimization (PSO-ELM), taking into account the inherent unpredictability and variability of wind power as well as the limitations of existing forecasting models. A Gradient Boost technique is then used to generate a large number of PSO-ELM weak predictors. Input weights and thresholds of each weak predictor, which each includes a unique hidden layer node. The results from each weak predictor are combined and weighed to get the final forecast result using a robust wind power forecast model. We use measured data from Turkish wind turbines to verify the efficacy of our suggested strategy. The findings demonstrate that the Gradient-PSO-ELM wind power prediction model has improved generalizability and accuracy than the Random forest. © 2023 IEEE.

关键词： and Wind power prediction Extreme Learning Machines Gradient Boost algorithm optimization algorithm

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optimization Study of BP Neural Network Based on Genetic algorithm

Optimization Study of BP Neural Network Based on Genetic Alg...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Cai, Yongheng Zhou, Zhuye Li, Zehui School of Computing Zhuhai College of Science and Technology Zhuhai China School of Data Sciences Zhejiang University of Finance & Economics Hangzhou China School of Automation and Software Engineering Shanxi University Taiyuan China

ISBN: (纸本)9798350309614

In today's era of big data, neural networks play a pivotal role in fields such as image recognition and natural language processing. However, traditional Backpropagation (BP) neural networks often encounter challenges when dealing with complex problems, including slow convergence and getting stuck in local optima. To address these issues, this study employs a genetic algorithm as an optimization technique to optimize the weights and thresholds of BP neural networks. Training is conducted on a set of highly nonlinear mapping datasets to obtain the optimal weight and threshold configurations for the GA-BP model. Experimental results demonstrate that compared to traditional BP neural networks, the GA-BP model exhibits a 19.8% improvement in prediction accuracy, an approximately 9.20% increase in the coefficient of determination (R2), a reduction of approximately 62.50% in mean squared error (MSE), and a decrease of about 38.76% in root mean squared error (RMSE). Furthermore, the GA-BP model displays significantly accelerated convergence. The contribution of this research lies in proposing an optimization method that combines genetic algorithms with BP neural networks. This model not only enhances prediction accuracy compared to the standalone BP neural network but also significantly strengthens its generalization capabilities. As a result, it demonstrates substantial practical application potential across diverse datasets and application scenarios. © 2023 IEEE.

关键词： BP neural network genetic algorithm optimization algorithm

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The optimization Clearing Model and algorithm Research of Electric Heat Storage Participates in Electricity Spot Market to Improve Renewable Energy Absorption Capacity 8

The Optimization Clearing Model and Algorithm Research of El...

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8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Liu, Gang Wang, Miao Zhao, Qingsong Wang, Zhonghui Zhang, Meishan Li, Dianyang State Grid LiaoNing Electric Power Supply Co. LTD Shenyang China State Grid LiaoNing Electric Power Supply Co. LTD Electric Power Research Insitute Shenyang China State Grid LiaoNing Electric Power Supply Co. LTD Economic Research Institute Shenyang China

ISBN: (纸本)9798350328813

With the construction of new power system and electricity market, the adjustable resources and market entities in the power system are constantly enriched, which puts forward higher requirements for system flexibility and market mechanism. Energy storage is an important means to improve the system regulation capacity, and low-cost, low-constraint electric heat storage technology is the best way to solve the problem of surplus renewable energy consumption and winter heating pollution. Therefore, this paper analyzes the operation characteristics of electric heat storage, proposes a method and mechanism to participate in the electricity spot market, and establishes an optimized clearing model including renewable energy consumption, electric heat storage load, cross-sectional constraint and node electricity price, on this basis, optimizes the operation mode of electric heat storage, improves the renewable energy absorption capacity, and reduces the overall operating cost and energy efficiency of the system. An example verifies the effectiveness of the proposed model and algorithm. © 2023 IEEE.

关键词： absorptive capacity clearing model electric heat storage electricity spot market optimization algorithm

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