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Heuristic algorithms for the bi-objective hierarchical multimodal hub location problem in cargo delivery systems

APPLIED MATHEMATICAL MODELLING 2021年 91卷 412-437页

作者： Shang, Xiaoting Yang, Kai Jia, Bin Gao, Ziyou Ji, Hao Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Xian Technol Univ Sch Econ & Management Xian 710021 Peoples R China

In this paper, we introduce an extended version of hub location problem, called bi-objective hierarchical multimodal hub location problem to simultaneously minimize the overall system-wide costs and the maximum delivery time. This problem is distinct from the classic hub location problem in designing a hierarchical multimodal hub-and-spoke network involving multiple transportation modes, multi-class hubs and corresponding layers. Combining cost and time dimensions, we first propose a bi-objective mixed-integer linear programming to model this problem formally with diverse flow balance constraints. We then show that the proposed model can be efficiently solved by a reformulation approach based on the epsilon-constraint method for only small instances. Hence, we develop two heuristics, a variable neighborhood search algorithm and an improved non-dominated sorting genetic algorithm-ii to obtain high-quality Pareto solutions for realistic-sized instances. We further illustrate the application of the proposed model to provide decision support for cargo delivery systems. Finally, we conduct extensive numerical experiments based on Turkish network to demonstrate the superiority of the proposed solution methods compared to the standard non-dominated sorting genetic algorithm-ii. The statistical results confirm the efficacy of the developed heuristic algorithms by adopting the Wilcoxon test. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Hierarchical hub location problem Cargo delivery systems Bi-objective programming Variable neighborhood search non-dominated sorting genetic algorithm-ii

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Energy-efficient path planning for a multi-load automated guided vehicle executing multiple transport tasks in a manufacturing workshop environment

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2024年 1-21页

作者： Zhang, Zhongwei Wu, Lihui Zhang, Boqiang Jia, Shun Liu, Weipeng Peng, Tao Henan Univ Technol Henan Key Lab Superhard Abras & Grinding Equipment Zhengzhou 450001 Peoples R China Shanghai Inst Technol Sch Mech Engn Shanghai 201418 Peoples R China Shandong Univ Sci & Technol Dept Ind Engn Qingdao 266590 Peoples R China Zhejiang Univ City Coll Sch Engn Hangzhou 310015 Zhejiang Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China

Automated guided vehicles (AGVs) are typical intelligent logistics equipment, and path planning plays a significant role in the efficient use of AGVs. To better utilize multi-load AGVs and enhance the sustainability of the logistics process, an energy-efficient path planning model is formulated for a multi-load AGV executing multiple transport tasks in a manufacturing workshop environment, with transport distance and energy consumption (EC) serving as optimization objectives. Furthermore, a two-stage approach is proposed to solve it. In the first stage, the optimal energy-efficient paths connecting any two different nodes are acquired based on the workshop transport network expressed as a topological map. Afterward, the non-dominated sorting genetic algorithm-ii is adopted in the second stage to determine the optimal execution sequence of pickup and delivery operations related to the assigned transport tasks, as well as to select the optimal path from the first stage's output information to execute each operation simultaneously. Moreover, the experimental study validates the energy-saving effect of the established model and the effectiveness of the solution method, and the factors affecting the multi-load AGV EC are analyzed.

关键词： Energy consumption Multi-load automated guided vehicle Manufacturing workshop Energy-efficient path planning Multiple transport tasks non-dominated sorting genetic algorithm-ii

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Multi-Objective genetic algorithm Assisted by an Artificial Neural Network Metamodel for Shape Optimization of a Centrifugal Blood Pump

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ARTIFICIAL ORGANS 2019年第5期43卷 E76-E93页

作者： Ghadimi, Behnam Nejat, Amir Nourbakhsh, Seyed Ahmad Naderi, Nasim Univ Tehran Coll Engn Sch Mech Engn Tehran Iran Rajaie Cardiovasc Med & Res Ctr Tehran Iran

A centrifugal blood pump is a common type of the pump used as a left ventricular assist device (LVAD) in the medical industries. The reduction of the LVADs hemolysis level to reduce the blood damage is one of the major concerns in designing of such devices. Also, the enhancement of the LVADs efficiency to decrease the battery size is another design requirement. The blood damage critically depends on the state of the blood being pumped. Besides the blood state, the blood damage also depends on the pump impeller and volute geometries. In this research, a multi-objective optimization of a centrifugal blood pump is performed. A complete 3D-optimization platform is established for both impeller and volute of a centrifugal blood pump consisting of parametric modeling, automatic mesh generation, computational fluid dynamics (CFD) simulation, and optimization strategy. A vast number of cases with various impeller and volute shapes are numerically simulated. Three different metamodels are created using artificial neural networks (ANNs) in order to approximate the pump hydraulic efficiency, hemolysis index (HI), and pressure head. The inverse of the relative pressure head is defined as the first objective and the summation of relative hemolysis index and the inverse of the relative efficiency is assumed as the second objective. non-dominated sorting genetic algorithm-ii (NSGA-ii) is used to find the Pareto Front. A set of optimal points is selected. Finally, for the physiological flow conditions, the optimum design that provides 11.9% HI reduction and 7.2% efficiency enhancement is selected.

关键词： Centrifugal blood pump Multi-objective optimization Metamodel non-dominated sorting genetic algorithm-ii Artificial neural network

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Multi-Objective Optimization for DC Microgrid Using Combination of NSGA-ii algorithm and Linear Search Method

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2023年第3期13卷 789-796页

作者： Ren, Zijun Qu, Xiaohui Wang, Minzhi Zou, Changyue Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Jiangsu Key Lab Smart Grid Technol & Equipment Nanjing 210096 Peoples R China China Southern Power Grid Co Ltd Elect Power Res Inst State Key Lab HVDC Guangzhou 510080 Peoples R China

With the penetration of renewable sources, the DC microgrid is much more efficient and flexible to link renewable power generators and DC loads. Due to the uncertainties in both sources and loads, it is hard to maintain the economic and optimal operation simultaneously in DC microgrids. To solve it, this paper builds a multi-objective optimization model including the operation cost, power loss, and load expectation ratio to satisfy the overall optimal management and system economy requirement. To fasten the optimization, a novel hybrid algorithm that combines the non-dominated sorting genetic algorithm-ii (NSGA-ii) and linear search method (LSM) is proposed as NSGA-LSM, which uses the fast global searching capacity of the LSM to accelerate the iteration process of NSGA-ii. Therefore, it is superior to optimize more than two objectives, and then suitable for the microgrid with different kinds of renewable sources. Finally, the simulation in a DC microgrid with distributed photovoltaics (PVs) as an example verifies the above analysis well.

关键词： Microgrids Optimization Batteries Costs State of charge Renewable energy sources genetic algorithms DC microgrid multi-objective optimization non-dominated sorting genetic algorithm-ii linear search method

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Artificial intelligence based structural optimization of solid oxide fuel cell with three-dimensional reticulated trapezoidal flow field

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第72期48卷 28131-28149页

作者： Xia, Lei Khosravi, Ali Han, Minfang Sun, Li Southeast Univ Sch Energy & Environm Natl Engn Res Ctr Power Generat Control & Safety Nanjing 210096 Peoples R China Univ Southern Denmark SDU Mechatron & Ctr Ind Mech Dept Mech & Elect Engn DK-6400 Sonderborg Denmark Tsinghua Univ Dept Energy & Power Engn State Key Lab Power Syst Beijing 100084 Peoples R China

Three-dimensional Reticulated trapezoidal flow field (RTFF) is promising in improving the performance and durability of the solid oxide fuel cell (SOFC). However, the structural complexity makes it challenging for the geometry configuration of the splitter and mixer. To this end, an intelligent optimization framework is proposed by coupling artificial neural network (ANN) and non-dominated sorting genetic algorithm-ii (NSGA-ii), in order to maximize the net power density and oxygen uniformity simultaneously. The ANN prediction model is trained to obtain the computationally efficient surrogate model of the computational fluid dynamics (CFD) numerical simulation. NSGA-ii is used for the multiobjective optimization of the RTFF structural parameters. The results illustrate that the prediction model is of high prediction precision and generalization capability. In comparison to SOFC with conventional parallel flow fields (CPFF), the degree of the performance improvement of SOFC with optimized RTFF depends on the working condition, i.e., fuel and air flow rates and operating temperatures. The SOFC with the optimal RTFF achieves a higher molar concentration of oxygen and a more uniform distribution of oxygen and current density than the CPFF SOFC. The proposed optimization framework provides an efficient design method for the development of the next-generation SOFC flow field. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Solid oxide fuel cell Reticulated trapezoidal flow field Artificial neural network non-dominated sorting genetic algorithm-ii Numerical simulation

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Design and Optimization of Magnetic Shielding Structures for Current Measurement in Triangularly Arranged Conductors

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第5期60卷 7746-7756页

作者： Zhou, Xingyu Cai, Dongsheng Zhang, Zhengyuan Han, Zhiwei Wang, Peng Khawaja, Arsalan Habib Huang, Qi Chengdu Univ Technol Coll Nucl Technol & Automat Engn Chengdu 610059 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Mech & Elect Engn Sichuan Prov Key Lab Power Syst Wide Area Measurem Chengdu 611731 Peoples R China Changan Commun Technol Co Beijing 100031 Peoples R China Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621053 Peoples R China

Accurately measuring the current is crucial for assessing the operational status of the line. The tunnel transmission approach presents challenges due to its intricate line layout in limited space. Utilizing non-contact current measurement offers a superior alternative. To suppress the potential magnetic interference in the transmission tunnel, a novel M-shaped magnetic shielding design for current measurement is proposed. The structure is used to measure current in triangularly arranged conductors where magnetic interference exists at random position. First, a one-dimensional and two-dimensional sensor system based on a triangular arrangement of conductors are used to analyze the magnetic field. Then, the structure of the magnetic shielding is derived. The relationship between the geometrical parameters of shielding structure and the position of the sensors with mean shielding uncertainty (MSU) is discussed by finite element analysis (FEA) method. At current of 2000 A in the conductors, the lowest MSU for the M-shape shielding structure (0.19%) is significantly lower than the lowest MSU for the C-shape shielding (1.63%). Moreover, the M-shaped shielding fits better with the geometry of conductors. In addition, thickness of the shielding structure, position of shielding structure and position of sensors are optimized based on the non-dominated sorting genetic algorithm-ii (NSGA-ii). The FEA method is used to simulate magnetic field interference acting on a three-phase AC current with an amplitude of 2000A. The two-dimensional sensor system with shielding structure can reduce the root mean square error (RMSE) of current to be measured from 14.42% to 3.79%.

关键词： Magnetic shielding Sensors Magnetic noise Magnetic fields Magnetic sensors Current measurement Interference magnetic shielding mean shielding uncertainty non-dominated sorting genetic algorithm-ii

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Multi-objective optimization of functionally graded materials, thickness and aspect ratio in micro-beams embedded in an elastic medium

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2018年第1期58卷 265-285页

作者： Taati, Ehsan Sina, Nima Sharif Univ Technol Sch Mech Engn Azadi Ave Tehran Iran Islamic Azad Univ Najafabad Branch Dept Mech Engn Najafabad Iran

Optimal design of micron-scale beams as a general case is an important problem for development of micro-electromechanical devices. For various applications, the mechanical parameters such as mass, maximum deflection and stress, natural frequency and buckling load are considered in strategies of micro-manufacturing technologies. However, all parameters are not of equal importance in each operating condition but multi-objective optimization is able to select optimal states of micro-beams which have desirable performances in various micro-electromechanical devices. This paper provides optimal states of design variables including thickness, distribution parameter of functionally graded materials, and aspect ratio in simply supported FG micro-beams resting on the elastic foundation using analytical solutions. The elastic medium is assumed to be as a two-layered foundation including a shear layer and a linear normal layer. Also, the size effect on the mechanical parameters is considered using the modified strain gradient theory and non-dominated sorting genetic algorithm-ii is employed to optimization procedure. The target functions are defined such that the maximum deflection, maximum stress and mass must be minimized while natural frequency and critical buckling load must be maximized. The optimum patterns of FG micro-beams are presented for exponential and power-law FGMs and the effect of theory type and elastic foundation discussed in details. Findings indicate that the elastic foundation coefficients and internal length scale parameters of materials have the significant influences on the distribution of design variables. It is seen that the optimum values of inhomogeneity parameter and aspect ratio for E-FG micro-beams predicted by the modified strain gradient theory are larger than those of the classical continuum theory. Also, the multi-objective optimization is able to improve the normalized values of mass, maximum deflection, buckling load and natural frequency o

关键词： Multi-objective optimization non-dominated sorting genetic algorithm-ii Size effect Modified strain gradient theory Euler-Bernoulli model Functionally graded materials

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Modeling and multi-objective optimization for ANAMMOX process under COD disturbance using hybrid intelligent algorithm

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2018年第21期25卷 20956-20967页

作者： Xie, Bin Ma, Yong-wen Wan, Jin-quan Wang, Yan Yan, Zhi-cheng Liu, Lin Guan, Ze-yu South China Univ Technol Coll Environm Sci & Engn Guangzhou 510640 Guangdong Peoples R China

Anaerobic ammonium oxidation (ANAMMOX) has been regarded as an efficient process to treat nitrogen-containing wastewater. However, the treatment process is not fully understood in terms of reaction mechanisms, process simulation, and control. In this paper, a multi-objective control strategy mixed soft-sensing model (MCSSM) is developed to systematically design the operating variations for multi-objective control by integrating the developed model, a least square support vector machine optimized with principal component analysis (PCA-LSSVM) and non-dominated sorting genetic algorithm-ii (NSGA-ii). The results revealed that the PCA-LSSVM model is a feasible and efficient tool for predicting the effluent ammonia nitrogen concentration () and the total nitrogen removal concentration (C (TN, rem) ) with determination coefficients (R (2)) were 0.997 for and 0.989 for C (TN, rem) , and gives us the reasonable solutions in influent by using NSGA-ii. To achieve a better removal effect, the influent pH should be kept between 7.50 and 7.52, the COD/TN ratio is suggested to maintain at 0.15 and the NH4 (+)-N/NO2 (-)-N ratio is suggested to maintain at 0.61. The developed MCSSM approach and its general modeling framework have a high potential of applicability and guidance to bioprocess in wastewater treatment, and numerical models can be structured for predicting and optimization and experiments can be conducted for data acquisition and model establishment.

关键词： ANAMMOX Least square support vector machine modeling Multi-objective optimization non-dominated sorting genetic algorithm-ii Wastewater treatment

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Multi-objective optimisation of coordinated control strategy for reducing shift shock based on engine model with reduced-order

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INTERNATIONAL JOURNAL OF HEAVY VEHICLE SYSTEMS 2024年第6期31卷 829页

作者： Shang, Xianhe Zhang, Fujun Zhang, Zhenyu Cui, Tao Beijing Inst Technol Sch Mech Engn Fundamental Sci Vehicular Power Syst Lab 5 Zhongguancun South St Beijing 10081 Peoples R China

To address the issue of increased shifting shocks caused by the limitations of the engine's full-speed regulation (FSR) characteristics during upshifting in heavy-duty vehicles, this paper proposes a coordinated control multi-objective optimisation strategy for reducing shifting shocks. This strategy takes into account the transient characteristics of the engine during the shifting process and uses long short-term memory (LSTM) neural networks to establish a reduced-order engine model. Based on the influence of the transient characteristics of the engine on shifting shock, a coordinated control scheme is formulated. To obtain the optimal solution for control parameters, a multi-objective optimisation was performed using the nondominated sorting genetic algorithm-ii (NSGA-ii) algorithm with the minimisation of root mean square of shifting shocks and friction work as optimisation objectives. Finally, the proposed coordinated control strategy was verified through simulation comparisons, demonstrating its superior control effectiveness in significantly reducing shifting shocks.

关键词： LSTM neural network NSGA-ii non-dominated sorting genetic algorithm-ii reduced-order model shift shock coordinated control

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Multi-objective classification based on NSGA-ii

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2018年第6期9卷 539-546页

作者： Zhao, Binping Xue, Yu Xu, Bin Ma, Tinghuai Liu, Jingfa Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Engn Res Ctr Commun & Network Technol Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210044 Jiangsu Peoples R China

The fast and elitist non-dominated sorting genetic algorithm-ii (NSGA-ii) is currently the most popular multi-objective evolutionary algorithm (MOEA). NSGA-ii has been shown to work well for two-objective problems by attaining near-optimal diverse and uniformly distributed Pareto solutions. To use the powerful multi-objective optimisation performance of NSGA-ii directly and conveniently, an optimisation classification model is presented. In the optimisation classification model, a linear equation set is constructed according to classification problems. In this paper, we introduced NSGA-ii to solve the optimisation classification model. Besides, eight different datasets have been chosen in experiments to test the performance of NSGA-ii. The results show that NSGA-ii is able to find much better spread of solutions and has high classification accuracy and robustness.

关键词： evolutionary classification algorithm non-dominated sorting genetic algorithm-ii NSGA-ii multi-objective optimisation

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