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Multi-criteria decision-making optimization model for permeable breakwaters characterization

OCEAN ENGINEERING 2023年第1期280卷

作者： Gandomi, Mostafa Pirooz, Moharram Dolatshahi Nematollahi, Banafsheh Nikoo, Mohammad Reza Varjavand, Iman Etri, Talal Gandomi, Amir H. Univ Tehran Fac Civil Engn Tehran Iran Shiraz Univ Dept Civil & Environm Engn Shiraz Iran Sultan Qaboos Univ Dept Civil & Architectural Engn Muscat Oman Univ Technol Sydney Fac Engn & Informat Technol Sydney NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary

Permeable breakwaters have always been of interest due to their advantages over the traditional types. This study proposed a stochastic multi-criteria decision-making model to optimize the geometry of permeable breakwaters. A multi-objective optimization algorithm was conducted using the non-dominated sorting genetic algorithm-ii (NSGA-ii) coupling with the estimations made by a well-known machine learning (ML) model, the multi-layer perceptron neural network (MLP-NN) to achieve the objective. Considering the inherent uncertainties in the wave characteristics using the conditional value-at-risk (CVaR) method, the presented risk-based model could determine optimal tradeoffs between wave transmission, wave reflection, and rockfill materials volume. This CVaR-based multi-objective optimization model was experimentally applied to a permeable breakwater with maximum significant wave heights of 1-3.5 m at confidence levels of 50%, 75%, 90%, and 99% for risk analysis. To rank several alternatives between the Pareto-optimal solutions, a decisive so-called multi-criterion decision-making (MCDM) approach was employed, which coupled the fuzzy decision-making trial and evalua-tion laboratory (DEMATEL) method and analytical network process (ANP) procedure. Results indicated that the heavier permeable breakwater was the most appropriate for greater wave heights. To this extent, the relative rockfill materials height and width increased to 1.1 from 0.77 and 1.1 from 0.41, respectively, by increasing the specific wave height from 2 to 3.5 m.

关键词： Analytical network process (ANP) Conditional value-at-risk (CVaR) Fuzzy decision-making trial and evaluation laboratory (DEMATEL) non-dominated sorting genetic algorithm-ii (NSGA-ii) Permeable breakwater Reflection and transmission coefficients

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Multi-variable approach to groundwater vulnerability elucidation: A risk-based multi-objective optimization model

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023年第1期338卷 117842页

作者： Zare, Masoumeh Nikoo, Mohammad Reza Nematollahi, Banafsheh Gandomi, Amir H. Farmani, Raziyeh Shiraz Univ Dept Civil & Environm Engn Shiraz Iran Sultan Qaboos Univ Dept Civil & Architectural Engn Muscat Oman Univ Calif Riverside Dept Environm Sci Riverside CA USA Univ Technol Sydney Fac Engn & IT Sydney NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary Univ Exeter Ctr Water Syst Dept Engn Math & Phys Sci Exeter Devon England

Groundwater vulnerability mapping is essential in environmental management since there is an increase in contamination caused by excessive population growth. However, to our knowledge, there is rare research dedicated to optimizing the groundwater vulnerability models, considering risk conditions, using a robust multi -objective optimization algorithm coupled with a multi-criteria decision-making model (MCDM). This study filled this knowledge gap by developing an innovative hybrid risk-based multi-objective optimization model using three distinguished models. The first model generated two series of scenarios for rate modifications associated with two common contaminations, Nitrate and Sulfate, based on susceptibility index (SI) and DRASTICA models. The second model was a multi-objective optimization framework using non-dominated sorting genetic algo- rithms-ii and iiI (NSGA-ii and NSGA-iiI), considering uncertainties in the input rates by the conditional value-at -risk (CVaR) technique. Finally, the third model was a well-known MCDM model, the COmplex PRoportional ASsessment (COPRAS), which identified the best compromise solution among Pareto-optimal solutions for weights of the contaminations. Regarding the Sulfate's results, although the optimized DRASTICA model led to the same correlation as the initial model, 0.7, the optimized SI model increased the correlation to 0.8 compared to the initial model as 0.58. For the Nitrate, both the optimized SI and the optimized DRASTICA models raised the correlation to 0.6 and 0.7 compared to the initial model with a correlation value of 0.36, respectively. Hence, the best and the lowest correlation among the optimized models were between SI and Sulfate concentration and SI and Nitrate concentration, respectively.

关键词： Complex proportional assessment (COPRAS) technique Groundwater vulnerability map Land use-based index model Multi-criteria decision-making (MCDM) model non-dominated sorting genetic algorithm-ii and iiI (NSGA-ii and NSGA-iiI)

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Multi-objective hybrid job-shop scheduling with multiprocessor task (HJSMT) problem with cooperative effect

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第2期46卷 5203-5217页

作者： Fan, Kun Zhang, Dingran Lv, Yuanyuan Zhou, Lang Qu, Hua Beijing Forestry Univ Sch Econ & Management Beijing Peoples R China

In order to solve the problem of discrete manufacturing customization and personalized production scheduling, considering the influence of manual labor on processing time, we propose a multi-objective Hybrid Job-shop Scheduling with Multiprocessor Task(HJSMT) problem with cooperative effect model. Based on the actual production, two optimization objectives are set, i. e. minimizing the maximum completion time and the total tardiness. Firstly, considering the situation where workers' cooperation reduces job processing time, the cooperative effect of workers co-processing is considered by referring to the learning effect curve in the model. Subsequently, we develop an Improved non-dominated sorting genetic algorithm-ii (INSGA-ii) to solve the multi-objective HJSMT problem by improving Precedence Operation Crossover (POX) and Multiple Mutations (MM) operations. Finally, the scheduling results and the C values are compared with other algorithms to verify the effectiveness of the algorithm. Simultaneously, the multi-objective HJSMT problem with the cooperative effect is solved by the INSGA-ii algorithm, and the experimental results also demonstrate the superior performance of the algorithm.

关键词： Hybrid job-shop scheduling multiprocessor task cooperative effect multi-objective optimization improved non-dominated sorting genetic algorithm-ii

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Optimal placement and capacity sizing of energy storage systems via NSGA-ii in active distribution network

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FRONTIERS IN ENERGY RESEARCH 2023年 10卷

作者： Su, Rui He, Guobin Su, Shi Duan, Yanru Cheng, Junzhao Chen, Hao Wang, Kaijun Zhang, Chao Dali Power Supply Bur Yunnan Power Grid Co Ltd Dali Peoples R China Elect Power Res Inst Yunnan Power Grid Co Ltd Kunming Peoples R China Grid Planning Res Ctr Yunnan Power Grid Co Ltd Kunming Peoples R China

In recent years, with the rapid development of renewable energy, the penetration rate of renewable energy generation in the active distribution network (ADN) has increased. Because of the instability of renewable energy generation, the operation stability of ADN has decreased. Due to the ability to cut peak load and fill valley load, battery energy storage systems (BESSs) can enhance the stability of the electric system. However, the placement and capacity of BESSs connected to ADN are extremely significant, otherwise, it will lead to a further decline in the stability of ADN. To ensure the effectiveness of the BESSs connected to the grid, this work uses the fuzzy kernel C-means (FKCM) method for scene clustering. Meanwhile, a multi-objective optimization model of BESS configuration is established with the objective of BESS configuration cost, voltage fluctuation, and load fluctuation, and solved by non-dominated sorting genetic algorithm-ii (NSGA-ii). In this work, the grey target decision method based on the entropy weight method (EWM) is used to obtain the optimal compromise solution from the Pareto non-dominated set. Moreover, the proposed method is tested and verified in the extended IEEE-33 node system and the extended IEEE-69 node system. The results show that the BESSs configuration scheme obtained by NSGA-ii can effectively reduce the fluctuation of voltage and load, and improve the stability of ADN operation.

关键词： battery energy storage systems fuzzy kernel C-means non-dominated sorting genetic algorithm-ii entropy weight method active distribution network

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An emergency multi-objective compromise framework for reservoir operation under suddenly injected pollution

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JOURNAL OF HYDROLOGY 2021年 598卷 126242-126242页

作者： Vanda, Sadegh Nikoo, Mohammad Reza Taravatrooy, Narges Sadegh, Mojtaba Al-Wardy, Malik Adamowski, Jan Franklin Shiraz Univ Dept Civil & Environm Engn Shiraz Iran Boise State Univ Dept Civil Engn Boise ID 83725 USA Sultan Qaboos Univ Dept Soils Water & Agr Engn Muscat Oman McGill Univ Dept Bioresource Engn 21111 Lakeshore Rd Ste Anne De Bellevue PQ Canada

An emergency multi-objective framework was developed to achieve an optimal reservoir operating strategy under sudden pollution injection. To assess a wide range of responses to potential future pollution injection events, a large number of reservoir release and pollution injection scenarios (1000 release modes and three injection location scenarios) were considered. The CE-QUAL-W2 model served to simulate pollutant concentration and reservoir cleanup time (RCT) under each scenario. To minimize the reservoir water quality modeling's computational burden, a multilayer perceptron (MLP) neural network was trained and validated against simulated responses to various scenarios. To reduce the dimensions of the problem, forcings of this surrogate model were made orthogonal by principal component analysis (PCA). This surrogate model helps estimate response variables at any intermediate scenario and can be readily coupled with a non-dominated sorting genetic algorithm-ii (NSGA-ii) optimization model to underpin Pareto optimal solutions. Four objective functions were considered: (i) non-supplied water demand, (ii) weighted combination of frequency and magnitude of pollution violation, (iii) ratio of pollution rate released from reservoir outlets to the total rate of injection, and (iv) difference between the reservoir volume and its normal water storage. Finally, a multi-method decision-making procedure was applied to select the best compromise solution for all stakeholders (i.e., Ministry of Energy, Ministry of Agriculture, Center for Environmental Health and Regional Water Authority). This study is the first to propose a reservoir optimal operation model using an MLP-PCA model coupled with several conflict resolution models. Findings showed that the closer the pollution injection point was to the reservoir outlet, the shorter the RCT. Following a 40-day simulation, the solution selected for the injection site closest to the reservoir outlet resulted in a 4-day RCT. Under ide

关键词： Sudden pollution injection Principal component analysis non-dominated sorting genetic algorithm-ii Social choice rules Fallback bargaining

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Pareto multi-objective optimization of metro train energy-saving operation using improved NSGA-ii algorithms

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CHAOS SOLITONS & FRACTALS 2023年 176卷

作者： Zhang, Zhenyu Cheng, Xiaoqing Xing, Zongyi Gui, Xingdong Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China

Quantitative analysis of the factors affecting the energy consumption of metro trains and finding out the breakthrough points of energy conservation is of great practical significance for reducing transport costs and improving energy utilization. Train operation curve optimization is one of the main methods to reduce the energy consumption of train operation. This paper introduces the metro train energy-saving operation optimization model with fixed travel time. Firstly, taking the energy consumption index and punctuality index as the objectives, a train energy-saving operation optimization model is established. Then, the solution method based on the improved NSGA-ii algorithm is developed. Finally, the developed optimization model is applied to Guangzhou Metro Line 7 to verify the performance. The results show that the energy consumption of the developed operation strategy is 24.4 % less than that of the minimum travel time strategy, and the total operation time meets the punctuality requirements. Meanwhile, the improved NSGA-ii algorithm has less energy consumption than GA and PSO algorithms in the application of Guangzhou Metro Line 7.

关键词： Metro train Energy-saving optimization non-dominated sorting genetic algorithm-ii (NSGA-ii) Pareto multi-objective optimization

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Developing sustainable strategies by LID optimization in response to annual climate change impacts

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JOURNAL OF CLEANER PRODUCTION 2023年第1期416卷

作者： Zamani, Mohammad G. Saniei, Khashayar Nematollahi, Banafsheh Zahmatkesh, Zahra Poor, Mohammad Moghadari Nikoo, Mohammad Reza Amirkabir Univ Technol Dept Water Resources Engn Tehran Iran Shahid Beheshti Univ Dept Water Resources Engn Tehran Iran Shiraz Univ Dept Civil & Environm Engn Shiraz Iran McMaster Univ Fac Engn Dept Civil Engn Hamilton ON Canada Arak Univ Fac Civil Water & Environm Engn Dept Civil Engn Arak Iran Sultan Qaboos Univ Dept Civil & Architectural Engn Muscat Oman

Designing urban runoff drainage systems is prominent in effectively managing floods due to increasing impermeable regions worldwide. However, although urban runoff drainage systems are configured mainly based on rainfall analysis, climate change influences their hydrological properties substantially. This research introduces an innovative methodology to analyze climate variations on optimal low impact developments (LIDs) of urban drainage systems in historical periods based on annual impacts (AIs) in projection periods considering uncertainties assessments. First, Storm Water Management Model (SWMM) is employed for simulating the process of rainfall-runoff considering quality and quantity analyses. This simulation model is coupled with a nondominated genetic algorithm- ii (NSGA-ii) optimization algorithm to minimize the cost of LIDs, flood volume, and pollutant load. Then, future runoff and daily rainfall are projected on a yearly basis, including maximum, minimum, and median rainfall, to identify how climate change affects catchment properties. These projections and an ensemble model are obtained based on the Long Ashton Research Station Weather Generator (LARS-WG), which considers uncertainties. After that, the projected daily precipitation is fragmented into hourly segments utilizing the change factor approach (CFA). Finally, the ideal optimum type, size, and placement of the chosen LIDs are determined using the presented simulation-optimization (SO) model. To prove the effectiveness and appropriateness of the presented framework, it is implemented in a real-world study area in Darabad catchment, Tehran, Iran. Results indicate that the developed optimal LIDs are well-designed and sufficient in both the historical and projection periods. The findings also depict that with the current LIDs established for the historical period, the flooding volume and summation of total suspended solid (TSS) and total Nitrogen (TN) removals are decreased by 55.96% and 60.2% compare

关键词： Annual impacts Climate change Long ashton research station weather generator (LARS-WG) Low impact developments (LIDs) non-dominated sorting genetic algorithm-ii (NSGA-ii) Storm water management model (SWMM)

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Manufacturing service supply-demand optimization with dual diversities for industrial internet platforms

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COMPUTERS & INDUSTRIAL ENGINEERING 2021年 156卷 107237-107237页

作者： Hao, Ruiyang Cheng, Ying Zhang, Yongping Tao, Fei Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Dept Automat Beijing 100084 Peoples R China

Industrial internet has been put forward to achieve interconnection between physical world and cyber space towards smart manufacturing, where geographically distributed manufacturing resources are centralized to achieve resource sharing and collaboration among manufacturing enterprises in the form of manufacturing services (MSs). However, MS supply-demand types are diversified and the quantity of MS supply and demand is dynamic, which would pose challenges to MS supply-demand matching and the operational stability of industrial internet platforms. A classified hypernetwork is proposed to depict the dual diversities in both types and quantity variation of MS supply-demand, and the supply-demand ratio of manufacturing services is defined as a main index to reflect the carrying capability of platforms. Then the problem of manufacturing service supply-demand optimization (MS-SDO) is modeled to improve the platforms' operational stability and to satisfy different stakeholders in the platforms, in which the impacts of supply-demand ratio on stakeholders' utility and platforms' operational stability are fully analyzed. Afterwards, a knowledge-guided NSGA-ii algorithm is designed to address the issue of MS-SDO, where traditional genetic procedures are modified to suit our issue and a knowledge-guided search approach is used to improve the search performance. Finally, numerous experiments are carried out for algorithm validation and issue analysis, with a simulation system constructed. Experimental results indicate that the majority of Pareto solutions by our proposed algorithm can dominate the solutions by the traditional algorithm, and some implications are concluded for the platform management and the service integration.

关键词： Supply-demand optimization (SDO) Manufacturing service (MS) Industrial internet Knowledge-guided search non-dominated sorting genetic algorithm-ii (NSGA-ii)

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MEMS Gyroscope Temperature Compensation Based on Improved Complete Ensemble Empirical Mode Decomposition and Optimized Extreme Learning Machine

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MICROMACHINES 2024年第5期15卷 609-609页

作者： Zhang, Zhihao Zhang, Jintao Zhu, Xiaohan Ren, Yanchao Yu, Jingfeng Cao, Huiliang Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Cent South Univ Coll Mech & Elect Engn Changsha 410083 Peoples R China Quanzhou Yunjian Measurement Control & Percept Tec Quanzhou 362000 Peoples R China North Univ China Key Lab Instrumentat Sci & Dynam Measurement Minist Educ Taiyuan 030051 Peoples R China

Herein, we investigate the temperature compensation for a dual-mass MEMS gyroscope. After introducing and simulating the dual-mass MEMS gyroscope's working modes, we propose a hybrid algorithm for temperature compensation relying on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy, time-frequency peak filtering, non-dominated sorting genetic algorithm-ii (NSGA ii) and extreme learning machine. Firstly, we use ICEEMDAN to decompose the gyroscope's output signal, and then we use sample entropy to classify the decomposed signals. For noise segments and mixed segments with different levels of noise, we use time-frequency peak filtering with different window lengths to achieve a trade-off between noise removal and signal retention. For the feature segment with temperature drift, we build a compensation model using extreme learning machine. To improve the compensation accuracy, NSGA ii is used to optimize extreme learning machine, with the prediction error and the 2-norm of the output-layer connection weight as the optimization objectives. Enormous simulation experiments prove the excellent performance of our proposed scheme, which can achieve trade-offs in signal decomposition, classification, denoising and compensation. The improvement in the compensated gyroscope's output signal is analyzed based on Allen variance;its angle random walk is decreased from 0.531076 degrees/h/root Hz to 6.65894 x 10-3 degrees/h/root Hz and its bias stability is decreased from 32.7364 degrees/h to 0.259247 degrees/h.

关键词： dual-mass MEMS gyroscope temperature compensation improved complete ensemble empirical mode decomposition with adaptive noise sample entropy time-frequency peak filtering non-dominated sorting genetic algorithm-ii extreme learning machine

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Data-driven multi-objective optimization design method for shale gas fracturing parameters

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2022年第0期99卷 104420-104420页

作者： Wang, Lian Yao, Yuedong Wang, Kongjie Adenutsi, Caspar Daniel Zhao, Guoxiang Lai, Fengpeng China Univ Petr State Key Lab Petr Resources & Prospecting Beijing 102249 Peoples R China CNPC Chuanqing Drilling Engn Co Ltd Changqing Downhole Technol Co Xian 710018 Peoples R China Kwame Nkrumah Univ Sci & Technol Fac Civil & Geoengn Dept Petr Engn Reservoir Simulat Lab Kumasi Ghana China Univ Geosci Sch Energy Resources Beijing 100083 Peoples R China

Accurate design of fracturing parameters in shale gas reservoirs does not only increase the productivity and profitability of wells but also minimize the damage fracturing fluids cause to fluid-sensitive formations. However, obtaining a highly accurate and optimized fracturing parameters with rapid as well cost-effective simulation run is difficult to achieve. This study proposed a framework for optimizing shale gas fracturing parameters based on least squares support vector regression (LSSVR) prediction model and non-dominated sorting genetic algorithm ii (NSGA-ii). This framework was named the multi-objective optimization prediction model (MOO-PM). In the proposed framework, the flowback ratio of fracturing fluid (FBR) and first month gas production (PROD) were considered as the objective functions while the fracturing parameters including horizontal length, number of fractured sections, fractured length, fracturing fluid injection rate, fracturing fluid viscosity, volume of fracturing fluid and proppant amount were chosen as the optimization variables. Firstly, based on field data the LSSVR prediction model was established. Secondly, the accuracy of the model was verified by the remaining field data. Subsequently, the Latin Hypercube method was used to construct a sample set of fracturing parameters within a predetermined range of variables and the corresponding objective functions were calculated by the LSSVR method. Finally, Pareto front was used to generate flowback ratio and first-month production by the NSGA-ii algorithm. To the best of our knowledge, this is the first time that LSSVR prediction model and multi objective method are applied simultaneously to optimize shale gas fracturing parameters. Furthermore, the novel multi-objective optimization framework was applied to data from the Weiyuan as well as the Changning shale gas reservoirs in Sichuan basin, southwest China. It was concluded that this framework could optimize fracturing parameters of shale g

关键词： Multi-objective optimization Fracturing parameters Shale gas non-dominated sorting genetic algorithm-ii

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