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Optimal Energy Scheduling of Load Demand with Two-Level multi-objective functions in Smart Electrical Grid

Operations Research Forum 2025年第2期6卷 1-23页

作者： Du, T.X. Jorshary, Kamilia Mehrabi Seyedrezaei, Mirali Uglu, Valisher Sapayev Odilbek School of Management Harbin Institute of Technology Harbin Heilongjiang 150001 China Energy Systems Department Ajman University Ajman United Arab Emirates Department of Industrial and Systems Engineering Russ College of Engineering and Technology Ohio University Athens OH United States Department of Economics and Business Colorado School of Mines Golden 80401 CO United States General Professional Science Department Mamun University Khiva Uzbekistan

In this paper, electrical grid scheduling with hybrid demand management via a multi-level optimization approach is implemented. At the upper level, demand management approaches are implemented, and at the lower level, multi-objective functions are optimized. The implementation of demand management approaches, including load shifting and interruption approaches, is carried out. These approaches are optimized subject to power prices in the upstream grid. The multi-objectives like reliability index and operation costs are optimized subject to demand management approaches at the upper level. By using the linear programming technique for the multidimensional analysis of preference (LINMAP) and fuzzy methods, the optimal solution in Pareto solutions for multi-objectives is selected. The performance of the approach is analyzed on the 69-bus network by employing power scheduling and integrating results from mathematical modeling through several case studies. With the implementation of the demand management approaches in optimal power consumption, the reliability index is improved by 0.6% and operating costs are minimized by 2.43% compared to the lack of demand management. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Demand management approaches multi-level optimization approach multi-objective functions Power prices

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multi-objective functions in Particle Swarm Optimization for Intrusion Detection 3

Multi-objective Functions in Particle Swarm Optimization for...

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3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Cleetus, Nimmy Dhanya, K. A. SCMS Sch Engn & Technol Dept Comp Sci & Engn Ernakulam Kerala India

ISBN: (纸本)9781479930807

The paper constitutes the study of particle swarm optimization in multi-objective functions. Swarm intelligence plays a vital role in intrusion detection. Intrusion detection system identifies the normal as well as abnormal behavior of a system. The weighted aggregation method is considered as multi-objective functions. We propose an intrusion detection mechanism based on particle swarm optimization which has a strong global search capability and used for the dimensionality optimization. Random forest is used as the classifier for modelling attacks and legitimate set. An accuracy of 91.71% at detection time of 0.22sec is obtained.

关键词： Particle Swarm Optimization multi-objective functions intrusion detection fitness function

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PYGENALGO: A simple and powerful toolkit for genetic algorithms

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SOFTWAREX 2025年 30卷

作者： Vrettas, Michail D. Silvestri, Stefano Natl Res Council ICAR CNR Inst High Performance Comp & Networking Via Pietro Castellino 111 I-80131 Naples Italy

Genetic algorithms (GAs) are meta-heuristic algorithms that are used for solving constrained and unconstrained optimization problems, mimicking the process of natural selection in biological evolution. Due to the fact that GAs do not require the optimization function to be differentiable, they are suitable for application in cases where the derivative of the objective function is either unavailable or impractical to obtain numerically. This paper proposes a general purpose genetic algorithm toolkit, implemented in Python3 programming language, having only minimum dependencies in NumPy and Joblib, that handle some of the numerical and parallel execution details.

关键词： Genetic algorithms Optimization Evolutionary algorithms Island model (parallel) multi-objective functions Python3

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Optimal operation of the smart electrical network considering energy management of demand side

SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION

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SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION 2025年第1期80卷

作者： Askar, Shavan Saidoune, Fatmazohra Abduvalieva, Dilsora Ezhilarasan, G. Thakur, Rishabh Yadav, Yashpal Kumar, Raj Hussen, Mohammed Jawad, Israa Abed Ali, A. M. Erbil Polytech Univ Erbil Tech Engn Coll Informat Syst Engn Dept Erbil Iraq Univ Medea Renewable Energy & Mat Lab LERM Medea 26000 Algeria Tashkent State Pedag Univ Dept Math & Informat Technol Bunyodkor Ave 27 Tashkent 100070 Uzbekistan JAIN Sch Engn & Technol Dept Elect & Elect Engn Bangalore Karnataka India Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India NIMS Univ Rajasthan NIMS Inst Engn & Technol Dept Elect Elect & Elect Vehicle Engn Jaipur Rajasthan India Chandigarh Grp Coll Jhanjeri Chandigarh Engn Coll Dept Elect & Commun Engn Mohali 140307 Punjab India Islamic Univ Coll Tech Engn Dept Comp Tech Engn Najaf Iraq Islamic Univ Al Diwaniyah Coll Tech Engn Dept Comp Tech Engn Al Diwaniyah Iraq Islamic Univ Babylon Coll Tech Engn Dept Comp Tech Engn Babylon Iraq Al Nisour Univ Coll Dept Technol Baghdad Iraq Ajman Univ Energy Syst Dept Ajman U Arab Emirates

The smart electrical grid represents a significant advancement in generating, distributing, and consuming electricity. This sophisticated system integrates modern technology and communication tools to enhance energy management efficiency and improve demand costuming within the power network. In this paper, optimal operation of the electrical network with energy management and Demand Response Program (DRP) is implemented. The implementation of the optimal operation is done via multi-stage and multi-objective functions modeling. The DRP modeling is done in first stage to optimal management of consumption in demand side. In second stage, operating cost, emission, power losses and voltage profile are optimized as multi-objective functions modeling with attention to optimal management of consumption in demand side. The solving optimal operation of the electrical network is carried out by using Elephant Herding Optimization (EHO). This problem is implemented on 33-bus test system with hybrid energy resources. Finally, DRP leads to reducing costs, emissions and losses and improving voltage profile in proposed electrical network. Hence, operation costs, emission, power losses, and voltage deviation with the participation of DRP are minimized by 39.15%, 9.94%, 33.35%, and 30.73%, respectively. On the other side, voltage stability is enhanced by 3.66% without considering DRP.

关键词： Smart electrical grid Energy management Demand response program multi-objective functions Optimal operation

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Optimization of the SWAT plus model to adequately predict different segments of a managed streamflow hydrograph

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HYDROLOGICAL SCIENCES JOURNAL 2024年第9期69卷 1198-1217页

作者： Tigabu, Tibebe B. Visser, Ate Kadir, Tariq Abudu, Shalamu Cameron-Smith, Philip Dahlke, Helen E. Univ Calif Davis Dept Land Air & Water Resources 1 Shields Ave Davis CA 95616 USA Lawrence Livermore Natl Lab Livermore CA USA Calif Dept Water Resources Modeling Support Branch Bay Delta Off Sacramento CA USA

Complete representation of rainfall-runoff responses in complex, large watersheds using a single-objective parameterization approach in watershed models is often unachievable. In this study we present a calibration approach for the SWAT+ model that independently fits model parameters for different flow segments of the hydrograph. The approach is demonstrated for the Feather River, California, USA, using daily streamflow from the Lake Oroville Reservoir outlet gage. Results show that when model parameters were independently fitted for different flow segments the KGE, NSE, PBIAS, and RSR values improved to 0.96, 0.99, -3.3, and 0.10, respectively, compared to 0.72, 0.66, -9.30, and 0.53, respectively, achieved under a multiobjective and full hydrograph (average hydrograph) calibration. The results highlight that when considering the average hydrograph and flow duration curves, a more balanced representation of both poorly and well-performing segments is achieved, emphasizing the importance of segment-specific parameterization and multi-objective evaluation for accurately representing different flow conditions.

关键词： Feather River managed streamflow optimization flow segment multi-objective functions SWAT plus

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multi-objective forest harvesting under sustainable and economic principles

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Journal of Forestry Research 2023年第5期34卷 1379-1394页

作者： Talles Hudson Souza Lacerda Luciano Cavalcante de Jesus Franca Isáira Leite e Lopes Sammilly Lorrayne Souza Lacerda Evandro OrfanóFigueiredo Bruno Henrique Groenner Barbosa Carolina Souza Jarochinski e Silva Lucas Rezende Gomide Suzano SA Sao JoséDos CamposSao PauloBrazil Institute of Agricultural Sciences Federal University of Uberlandia–UFUMonte CarmeloMinas GeraisBrazil Department of Forest Sciences Federal University of Lavras–UFLALavrasMinas GeraisBrazil Brazilian Agricultural Research Corporation–EMBRAPA Rio BrancoAcreBrazil Department of Systems and Automation Federal University of Lavras–UFLALavrasMinas GeraisBrazil

Selective logging is well-recognized as an effective practice in sustainable forest ***,the ecological efficiency or resilience of the residual stand is often in *** time depends on operational variables,diversity,and forest *** logging is excellent but is open to *** may be resolved by mathematical programming and this study integrates the economic-ecological aspects in multi-objective function by applying two evolutionary *** function maximizes remaining stand diversity,merchantable logs,and the inverse of distance between trees for harvesting and log landings *** Brazilian rainforest database(566 trees)was used to simulate our 216-ha *** log landing design has a maximum volume limit of 500 *** nondominated sorting genetic algorithm was applied to solve the main optimization *** parallel,a sub-problem(p-facility allocation)was solved for landing allocation by a genetic *** frontier analysis was applied to distinguish the gradientsα-economic,β-ecological,andγ-*** expected,the solutions have high diameter changes in the residual stand(average removal of approximately 16 m^(3) ha^(-1)).All solutions showed a grouping of trees selected for harvesting,although there was no formation of large clearings(percentage of canopy removal<7%,with an average of 2.5 ind ha^(-1)).There were no differences in floristic composition by preferentially selecting species with greater frequency in the initial stand for *** implies a lower impact on the demographic rates of the remaining *** methodology should support projects of reduced impact logging by using spatial-diversity information to guide better practices in tropical forests.

关键词： Amazon rainforest management Computational intelligence multi-objective functions Evolutionary computing

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A multi-stage energy operation of the smart microgrid with optimal performance of the consumers

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multiSCALE AND multiDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 43-52页

作者： Jingjing, Wang Zibo Vocat Inst Coll Elect & Elect Engn Zibo 255000 Shandong Peoples R China

In this paper, multi-stage energy optimization with demand response programs (DRPs) in a smart microgrid (SMG) is investigated. The proposed approach by using tri-stage multi-objective functions is modeled. The two DRPs, including the demand shifting program (DSP) in the first stage and the load reduction program (LRP) as reserve strategy in the second stage, are taken into account as optimal consumers' participation based on electrical price traffic in the energy market. However, optimization of the third stage is modeled as a multi-objective for decreasing power losses and energy costs. In the proposed optimization, the electrical load demand in the first and second stages is modified. In the following, modified consumption is linked to the reduction of the multi-objective functions. Using the & epsilon;-constraint method, the proposed optimization approach is solved for the third stage. Finally, a simulation of the proposed optimization is done as two case studies to validation of the mentioned approach. The case studies considering the implementation of the DRPs are investigated. In the first case, DRPs are not implemented by the demand side, whereas in the second case, DRPs are performed. The results in the second case show consumers' participation in the modification of the consumption pattern has desired impacts on technical and economic indices in SMG.

关键词： multi-stage optimization Smart microgrid (SMG) Demand shifting program (DSP) Load reduction program (LRP) multi-objective functions LINMAP

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multiobjective Fitness functions With Nonlinear Switching for Antenna Optimizations

IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION

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IEEE OPEN JOURNAL OF ANTENNAS AND PROPAGATION 2022年 3卷 613-626页

作者： Khan, Md Rayhan Zekios, Constantinos L. Bhardwaj, Shubhendu Georgakopoulos, Stavros, V Florida Int Univ Dept Elect & Comp Engn Miami FL 33199 USA

In a multi-objective optimization process, several goals are traditionally combined into a single fitness function. In such cases, the choice of the objective function is critical, as it should accurately represent the desired optimization goals. Here, we introduce a new class of multi-objective functions with non-linearity and switching behavior, and also provide a method for objective function engineering. Notably, the proposed objective functions introduce versatile forms of fitness growth during the optimization, and provide a systematic approach for integrating the expertise in antenna design with the optimization process. The proposed optimization processes are applied in antenna optimization to demonstrate their enhanced performance. Our optimization examples consider problems based on both analytical electromagnetic models and full-wave simulation. Specifically, we consider the designs of an end-fire array, a pyramidal horn antenna, a Yagi-Uda array, and a wideband patch antenna. Our results suggest that, with minimum computation effort, the proposed non-linear fitness functions produce better performing designs when compared to a linear summation-based fitness function, e.g., 12% higher forward gain for the Yagi-Uda array, 9.3% lower side lobe level for the horn antenna, 23.38% higher directivity for the end-fire array, and approximately 1.5 times higher bandwidth for the wideband patch antenna.

关键词： Optimization Linear programming Switches Antenna arrays Logistics Horn antennas Broadband antennas Antenna optimization cost function multi-objective functions non-linear function particle swarm optimization

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ICDNSGA: Identification of Potential circRNA-Disease Associations Based on Improved Non-Dominated Sorting Genetic Algorithm

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2025年第2期22卷 557-569页

作者： Wang, Yuehao Lu, Pengli Lanzhou Univ Technol Sch Comp & Commun Lanzhou 730050 Gansu Peoples R China

Increasing biological research indicates that the expression levels of circRNAs fluctuate during the onset of various diseases, making them potential biomarkers for multiple conditions. Although numerous artificial intelligence-based computational methods are currently employed for circRNA-disease associations prediction, these methods often rely on a single objective function, which can lead to suboptimal prediction accuracy. To date, no method has designed a set of multi-objective functions specifically for the circRNA-disease prediction problem and optimized it using a non-dominated sorting genetic algorithm. This paper introduces a novel approach by utilizing multi-objective functions and an improved non-dominated sorting genetic algorithm (ICDNSGA) to identify potential associations of circRNA-disease. The method constructs a solution space through matrix factorization and network community characteristics, designing four distinct objective functions optimized via the enhanced multi-objective non-dominated sorting genetic algorithm. ICDNSGA incorporates a population-based adaptive normalization strategy, improving algorithm convergence and solution diversity. Experimental results show that ICDNSGA outperforms pure matrix factorization methods, non-dominated sorting genetic algorithms and other machine learning techniques in predictive performance. Additionally, the prediction results can be validated through existing research and biological analyses, underscoring ICDNSGA's potential as a valuable tool for biomedical experimentation.

关键词： Diseases RNA Sorting Linear programming Genetic algorithms Biology Semantics Feature extraction Matrix decomposition Proteins CircRNA-disease connections multi-objective functions non-dominated sorting genetic algorithm

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multi-objective optimisation based fuzzy association rule mining method

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2023年第3期26卷 1055-1072页

作者： Zheng, Hui He, Jing Liu, Qing Li, Jianhua Huang, Guangli Li, Peng Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China Jiangsu High Technol Res Key Lab Wireless Sensor Nanjing Jiangsu Peoples R China Swinburne Univ & Technol Melbourne Vic Australia CSIRO Software & Computat Syst Program Data 61 Canberra ACT Australia Deakin Univ Sch Info Technol Burwood Campus Melbourne Vic Australia Deakin Univ Sch Informat Technol Geelong Vic Australia

Fuzzy association rule mining (FARM) is a mainstream method to discover hidden patterns and association rules in quantitative data. It is essential to improve performance metrics, including quantity performance (e.g., the number of rules, the number of frequent itemsets) and quality performance (e.g., fuzzy support and confidence). The current approaches inadequately support optimisation of both quantity and quality performance. We propose a multi-objective optimisation algorithm for FARM (MOOFARM), where quantity and quality performance metrics are improved and validated simultaneously. The experimental evaluation conducted on a real dataset showcases the outstanding performance of MOOFARM against state-of-the-art works. In particular, at minimum support = 0.1, minimum confidence = 0.7, our MOOFARM increases the quantity performance up to 11 times. The proposed method improves the quality performance up to 71.05%.

关键词： multi-objective functions Optimisations Fuzzy association rules

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