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Pareto front generation for integrated drive-train and structural optimisation of a robot manipulator conceptual design via NSGA-II

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ADVANCES IN MECHANICAL ENGINEERING 2023年第3期15卷

作者： Gulec, Musa Ozgun Ertugrul, Seniz Istanbul Tech Univ Mech Engn Dept Istanbul Turkiye Izmir Univ Econ Mechatron Engn Dept Izmir Turkiye Istanbul Tech Univ Mech Engn Dept Gumussuyu Inonu Cd TR-34437 Istanbul Turkiye

Due to the complexity of the process, there is no single solution for determining the motors, gearboxes and structures of a robot manipulator according to the desired dynamic performance while minimising both the deflections in the structure during the dynamic motion and total robot weight. The solution of this integrated drive-train and dynamic structural optimisation problem is generalised for three degrees of freedom (DOF) robot manipulator via non-dominated sorting genetic algorithm II (NSGA-II) to obtain the Pareto front of any desired robot manipulator overall conceptual design, including motors, gearboxes and thicknesses of the links. A flexible body dynamic simulation model was created in the MATLAB Simmechanics environment. The flexible bodies were defined via lumped parameter estimation method, which allows observation of the deflections in links during the dynamic motion. A library containing technical data related to motors and gearboxes was created to be utilised in the optimisation algorithm. The method accelerates the time-consuming iterative process for obtaining optimum conceptual design solutions for a dynamic system and allows for easy modification of design parameters and constraints. It also makes the algorithm suitable for different types of dynamic system designs.

关键词： Robot design integrated conceptual design optimisation dynamic simulation of flexible bodies the lumped parameter estimation drive-train optimisation non-dominated sorting genetic algorithm

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An improved NSGA-Ⅱ algorithm for multi-satellite observation scheduling

An improved NSGA-Ⅱ algorithm for multi-satellite observatio...

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第43届中国控制会议

作者： Chaozhou Huang Lizhen Shao Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Shunde Innovation School University of Science and Technology Beijing

Earth observation satellites(EOS) equipped with high-resolution cameras and infrared sensors are used to observe various types of objects on the Earth's surface. When there are numerous ground targets, the available time windows for multiple EOS to observe the given task should be utilized effectively. This problem is known as the multi-satellite observation scheduling problem(MSOSP). To address the scheduling problem of dense tasks with multiple satellites, this paper establishes a bi-objective mixed integer programming model with minimizing the maximum completion time and the total weighted completion time simultaneously. Then, an improved NSGA-II algorithm(INSGA-II), which incorporates a density-based clustering algorithm called DBSCAN to enhance the diversity of solutions during the population evolution process, is proposed to solve the model. Finally, two observation scenarios are designed, and computational experiments are conducted. The results show that INSGA-II outperforms the NSGA-II algorithm in terms of C-metric and hypervolume metric.

关键词： Earth observation satellites Multi-satellite observation scheduling problem non-dominated sorting genetic algorithm

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A hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems

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Decision Analytics Journal 2024年 11卷

作者： Nessari, Saman Tavakkoli-Moghaddam, Reza Bakhshi-Khaniki, Hessam Bozorgi-Amiri, Ali School of Industrial Engineering College of Engineering University of Tehran Tehran Iran Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey

The flexible job shop scheduling problem (FJSSP) is a complex optimization challenge that plays a crucial role in enhancing productivity and efficiency in modern manufacturing systems, aimed at optimizing the allocation of jobs to a variable set of machines. This paper introduces an algorithm to tackle the FJSSP by minimizing makespan and total weighted earliness and tardiness under uncertainty. This hybrid algorithm effectively addresses the complexities of stochastic multi-objective optimization by integrating the equilibrium optimizer (EO) as an initial solutions generator, non-dominated sorting genetic algorithm II (NSGA-II), and simulation techniques. The algorithm's effectiveness is validated by showcasing specific instances and delivering decision results for optimal scheduling across varying levels of uncertainty. Results reveal the algorithm's consistent superiority in managing the complexities of stochastic parameters across various problem scales, achieving lower makespan and improved Pareto front quality compared to existing methods. Particularly notable is the algorithm's faster convergence and robust performance, as validated by the statistical Wilcoxon test, which confirms its reliability and efficacy in handling dynamic scheduling situations. These findings underscore the algorithm's potential in providing flexible, robust solutions. The proposed algorithm's unique balance of exploitative and explorative capabilities within a simulation framework enables effective handling of uncertainty in the FJSSP, offering flexibility and customization that is adaptable to various scheduling environments. © 2024 The Author(s)

关键词： Equilibrium optimizer Flexible job shop scheduling Multi-objective stochastic optimization non-dominated sorting genetic algorithm Simheuristics

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Hierarchical multi-objective optimal planning model of active distribution network considering distributed generation and demand-side response

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartA期53卷

作者： Xu, Xiaomin Niu, Dongxiao Peng, Luyao Zheng, Shipeng Qiu, Jinpeng North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China North China Elect Power Univ Beijing Key Lab New Energy & Low Carbon Dev Beijing 102206 Peoples R China Elect Engn Corp Southwest Elect Power Design Inst Chengdu 610056 Peoples R China

Power grid planning is the basis of power system development, the structure of distribution network in China has changed from single source unidirectional network to multi-source multidirectional complex power network, which further increases the uncertainty and complexity of distribution network operation and planning. From the perspective of distributed energy grid connection, this paper constructs a hierarchical optimization model of distribution network planning under active management mode. The relationship among balanced network loss, line investment, power purchase cost, carbon emission cost and policy subsidies are comprehensively considered. To improve the convergence and effectiveness of the algorithm, NSGA-TS hybrid algorithm is applied for example analysis, which has better global search and local search ability. From the calculation and analysis results of an example, it can be seen that the model is effective in practical analysis, and expands the analysis idea of distribution network planning.

关键词： Active distribution network Distributed generation Hierarchical multi-objective programming non-dominated sorting genetic algorithm Tabu search

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Modelling and multi-objective optimization of hybrid energy storage solution for photovoltaic powered off-grid net zero energy building

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JOURNAL OF ENERGY STORAGE 2022年 55卷

作者： Adeyemo, Ayotunde A. Amusan, Olumuyiwa T. Norwegian Univ Sci & Technol NTNU Dept Elect Power Engn Trondheim Norway Univ Johannesburg Dept Elect & Elect Engn Sci Johannesburg South Africa Univ Lagos Dept Elect & Elect Engn Lagos Nigeria

This paper investigates the modelling and multi-objective optimization (using non-dominated sorting genetic algorithm (NSGA-II)) of a photovoltaic-battery-hydrogen hybrid renewable energy system (HRES) for a net zero energy building (NZEB) that is a five-bedroom duplex detached residential house located in the heart of Lagos, Nigeria. Three battery technologies: lithium iron phosphate (LFP) battery, retired electric vehicle battery (REVB) and Ortsfest -stationary PanZerplatte -tubular plate Verschlossen -closed lead acid (OPzV) battery, were investigated for their economic viability. An in-house simplified degradation model was used to evaluate the capacity loss (as this can have significant impact on cost calculations if not done) of the batteries with cycling and calendar aging. This addresses the lack of implementation of battery degradation modelling by many authors optimizing for HRES due to inapplicability of the models in literature for different battery chemistries. Also, this work evaluated battery-fuel cell hybrid configurations on the three objectives: annualized cost of system (ACS), loss of power supply probability (LPSP) and potential energy waste probability (PEWP) and the results were contrasted with the battery only configurations to identify scenarios where hybrid energy storage system might be more economical. The results showed that LFP and REVB are the best battery options. LFP and REVB battery only configurations are better for LPSP constraints >1 % whereas for LPSP of 1 % and below LFP-hydrogen and REVB-hydrogen are more economical.

关键词： Multi-objective optimization Net zero energy building non-dominated sorting genetic algorithm (NSGA-II) Hybrid renewable energy system Retired electric vehicle battery Lithium iron phosphate battery

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Zero-jitter differentiated traffic scheduling algorithm in 5G fronthaul hybrid network

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OPTIK 2022年 265卷

作者： Wu, Lijuan Gan, Chaoqin Xu, Zhongsen Hui, Jianqiang Shanghai Univ Key Lab Specialty Fiber Opt & Opt Access Networks Joint Int Res Lab Specialty Fiber Opt & Adv Commu Shanghai 200444 Peoples R China

The emergence of services such as high-definition video, smart home, and virtual reality has led to stringent requirements for delay jitter in 5G fronthaul network. In this paper, a novel differentiated traffic scheduling algorithm is proposed in 5G fronthaul hybrid network. It can not only eliminate the delay jitter, but also simultaneously transmit CPRI traffic and eCPRI traffic for the first time. To CPRI traffic, zero-jitter of delay is achieved by innovatively mapping the scheduling timeslots of the flow's packets into an arithmetic progression. Furthermore, the optimal flow scheduling with minimizing the delay and minimizing the number of conflicting time slots is realized by combining differential evolution (DE) with non-dominated sorting genetic algorithm II (NSGA-II). To eCPRI traffic, zero-jitter flow scheduling is performed by innovatively proposing a fuzzy time slot method. In this method, by implementing different caching policies for marked and not marked flows, zero-jitter is achieved. By controlling the upper limit of packet sending time, the delay performance is guaranteed. Finally, by the simulation, the effectiveness of the proposed algorithm is demonstrated. The simulation results indicate that the optimal flow scheduling based on this algorithm has good performance in jitter, delay and bandwidth utilization.

关键词： Fronthaul network Common public radio interface Traffic scheduling algorithm Jitter non-dominated sorting genetic algorithm

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Hybrid NSGA III/dual simplex approach to generation and transmission maintenance scheduling

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 135卷 107498-107498页

作者： San Martin, Luis Salinas Yang, Jin Liu, Ying Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Lanark Scotland

The generation and transmission maintenance scheduling (GTMS) problem presents generation (GENCOs) and transmission (TRANSCO) companies scheduling their facilities for maintenance to maximize their profits, while the independent system operator (ISO) pushes for maintenance schedules (MS) that guarantees system reliability and minimizes operation cost. Inherently, GTMS is a high-dimensional, non-linear, non-convex, multi-objective optimization problem that contains conflicting objectives related to different participants in the market. This paper develops a hybrid model to tackle the GTMS problem in a deregulated market environment by combining in a novel way the non-dominated sorting genetic algorithm III (NSGA III) and the Dual-Simplex (DS) techniques. The model manages to minimize the total system operational cost and keep high system adequacy, both aspects of interest for the independent system operator (ISO), while increasing the profits of GENCOs. The approach used matches accepted industry maintenance practices with cutting-edge optimization techniques developed in academia. The model, tested in the IEEE-RTS 24 bus test network, delivers a set of feasible MS solutions that address the conflicting relationships between the GENCOs and the ISO in the market, displays a degree of coordination among generation and transmission MS and their impact on electricity prices. Finally, it allows the ISO to use this set to identify the best using the technique for ordering preferences according to the similarity to an ideal solution (TOPSIS) decision-making tool.

关键词： Generation and transmission maintenance scheduling Deregulated electricity market Multi-objective optimization Multi-objective evolutionary algorithms non-dominated sorting genetic algorithm Dual-Simplex optimization technique

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Maximizing the thermal performance index applying evolutionary multi-objective optimization approaches for double pipe heat exchanger

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APPLIED THERMAL ENGINEERING 2022年 211卷 1页

作者： Colaco, Andrey Barbosa Mariani, Viviana Cocco Salem, Mohamed Reda Coelho, Leandro dos Santos Pontif Catholic Univ Parana Dept Mech Engn Curitiba Brazil Univ Fed Parana Dept Elect Engn Curitiba Brazil Benha Univ Fac Engn Shoubra Mech Engn Dept 108 Shoubra St Cairo 11629 Egypt Pontif Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS Curitiba Brazil

The thermal performance index (TPI) known as the efficiency parameter evaluates the highlight of the heat transfer for the same pumping power requirement. The optimum design of a heat exchanger with minimum pressure drops and efficient heat transfer is a big challenging task from an energy-saving point of view. One way to optimize this efficiency parameter is by changing the geometric parameters of the heat exchanger. For this purpose, the multi-objective reinforcement learner non-dominated sorting genetic algorithm (NSGA-RL) is employed maximizing TPI and Nusselt number and minimizing Fanning friction factor to the optimal design of a double pipe heat exchanger, with perforated baffles in the annulus side. Its performance is compared with a nondominated sorting genetic algorithm - version II (NSGA-II), and a chaotic non-dominated sorting genetic algorithm (CNSGA). The calculations were carried out using the turbulent shear stress transport k-omega model. The multi-objective optimization techniques presented promising results in terms of the number of nondominated solutions, Euclidean distances to the origin point (reference point), and hypervolume indicators. However, in general, the results based on mean values indicate that the NSGA-RL outperforms the NSGA-II and CNSGA concerning the three performance indicators for the two double pipe heat exchanger evaluated cases. TPI increases 78% for case 1, and 108% for case 2 compared without baffles in a double pipe heat exchanger (DPHE). Numerical results revealed that DPHE employing internal perforated baffles improved heat transfer significantly with Nusselt number increased around 7.93-8.25 times and friction factor increased by 6.5-9.75 times compared with plain DPHE.

关键词： Double pipe heat exchanger non-dominated sorting genetic algorithm Turbulent flow Thermal performance index multi-objective optimization

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A Novel Two-Level Optimization Strategy for Multi-Debris Active Removal Mission in LEO

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Computer Modeling in Engineering & Sciences 2020年第1期122卷 149-174页

作者： Junfeng Zhao Weiming Feng Jianping Yuan Shandong University Jinan250031China Northwestern Polytechnical University Xi’an710072China National Key Laboratory of Aerospace Flight Dynamics Xi’an710072China

Recent studies of the space debris environment in Low Earth Orbit(LEO)have shown that the critical density of space debris has been reached in certain *** Active Debris Removal(ADR)mission,to mitigate the space debris density and stabilize the space debris environment,has been considered as a most effective *** this paper,a novel two-level optimization strategy for multi-debris removal mission in LEO is proposed,which includes the low-level and high-level optimization *** improve the overall performance of the multi-debris active removal mission and obtain multiple Pareto-optimal solutions,the ADR mission is seen as a Time-Dependant Traveling Salesman Problem(TDTSP)with two objective functions to minimize the total mission duration and the total propellant *** problem includes the sequence optimization to determine the sequence of removal of space debris and the transferring optimization to define the orbital *** optimization models for the two-level optimization strategy are built in solving the multi-debris removal mission,and the optimal Pareto solution is successfully obtained by using the non-dominated sorting genetic algorithm II(NSGA-II).Two test cases are presented,which show that the low level optimization strategy can successfully obtain the optimal sequences and the initial solution of the ADR mission and the high level optimization strategy can efficiently and robustly find the feasible optimal solution for long duration perturbed rendezvous problem.

关键词： Two-level optimization strategy active debris removal non-dominated sorting genetic algorithm bi-objective optimization LEO

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A novel hybrid feature selection and modified KNN prediction model for coal and gas outbursts

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第5期39卷 7671-7691页

作者： Liu, Xuning Zhang, Guoying Zhang, Zixian China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing Peoples R China Shijiazhuang Univ Dept Comp Engn Shijiazhuang Hebei Peoples R China Liaocheng Univ Sch Foreign Univ Liaocheng Shandong Peoples R China

The feature selection of influencing factors of coal and gas outbursts is of great significance for presenting the most discriminative features and improving prediction performance of a classifier, the paper presents an effective hybrid feature selection and modified outbursts classifier framework which aims at solving exiting coal and gas outbursts prediction problems. First, a measurement standard based on maximum information coefficient(MIC) is employed to identify the wide correlations between two variables;Second, based on a ranking procedure using non-dominated sorting genetic algorithm(NSGAII), maximum relevance minimum redundancy(MRMR) algorithm is subsequently performed to find out candidate feature set highly related to the class label and uncorrelated with each other;Third, random forest(RF) is employed to search the optimal feature subset from the candidate feature set, then the optimal feature subset that influences the classification performance of coal and gas outbursts is obtained;Finally, an improved classifier model has been proposed that combines gradient boosting decision tree(GBDT) and k-nearest neighbor(KNN) for outbursts prediction. In the modified classifier model, the GBDT is utilized to assign different weights to features, then the weighted features are input into the KNN to verify the effectiveness of proposed method on coal and gas outbursts dataset. The experimental results conclude that our proposed scheme is effective in the number of feature and prediction accuracy when compared with other related state-of-the-art prediction models based on feature selection for coal and gas outbursts.

关键词： Coal and gas outbursts Maximum information coefficient non-dominated sorting genetic algorithm Maximum relevance minimum redundancy Random forest Gradient boosting decision tree K-nearest neighbor

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