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multi-objective optimisation and decision-making of space station logistics strategies

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2016年第13期47卷 3132-3148页

作者： Zhu, Yue-he Luo, Ya-zhong Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha Hunan Peoples R China

Space station logistics strategy optimisation is a complex engineering problem with multiple objectives. Finding a decision-maker-preferred compromise solution becomes more significant when solving such a problem. However, the designer-preferred solution is not easy to determine using the traditional method. Thus, a hybrid approach that combines the multi-objective evolutionary algorithm, physical programming, and differential evolution (DE) algorithm is proposed to deal with the optimisation and decision-making of space station logistics strategies. A multi-objective evolutionary algorithm is used to acquire a Pareto frontier and help determine the range parameters of the physical programming. Physical programming is employed to convert the four-objective problem into a single-objective problem, and a DE algorithm is applied to solve the resulting physical programming-based optimisation problem. Five kinds of objective preference are simulated and compared. The simulation results indicate that the proposed approach can produce good compromise solutions corresponding to different decision-makers' preferences.

关键词： Space station logistics multi-objective evolutionary algorithm physical programming differential evolution algorithm decision-making preference

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A multi-criteria fusion feature selection algorithm for fault diagnosis of helicopter planetary gear train

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Chinese Journal of Aeronautics 2020年第5期33卷 1549-1561页

作者： Canfei SUN Youren WANG Guodong SUN College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Testing Center Aviation Key Laboratory of Science and Technology on Fault Diagnosis and Health ManagementShanghai 201601China

Planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the *** health condition monitoring,the selection of a fault sensitive feature subset is meaningful for fault diagnosis of helicopter planetary gear *** to actual situation,this paper proposed a multi-criteria fusion feature selection algorithm (MCFFSA) to identify an optimal feature subset from the highdimensional original feature *** MCFFSA,a fault feature set of multiple domains,including time domain,frequency domain and wavelet domain,is first extracted from the raw vibration *** targeted criteria are then fused by multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find Proto-efficient subsets,wherein two criteria for measuring diagnostic performance are assessed by sparse Bayesian extreme learning machine (SBELM).Further,Fmeasure is adopted to identify the optimal feature subset,which was employed for subsequent fault *** effectiveness of MCFFSA is validated through six fault recognition datasets from a real helicopter transmission *** experimental results illustrate the superiority of combination of MOEA/D and SBELM in MCFFSA,and comparative analysis demonstrates that the optimal feature subset provided by MCFFSA can achieve a better diagnosis performance than other algorithms.

关键词： Fault detection Feature selection F-measure Helicopter planetary gear train multi-objective evolutionary algorithm

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Teaching learning based optimization with Pareto tournament for the multiobjective software requirements selection

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2015年 43卷 89-101页

作者： Chaves-Gonzalez, Jose M. Perez-Toledano, Miguel A. Navasa, Amparo Univ Extremadura Dept Comp Sci Caceres Spain

Software requirements selection is a problem which consists of choosing the set of new requirements which will be included in the next release of a software package. This NP-hard problem is an important issue involving several contradictory objectives which have to be tackled by software companies when developing new releases of software packages. Software projects have to stick to a budget, but they also have to satisfy the highest number of customer requirements. Furthermore, when managing real instances of the problem, the requirements tackled suffer interactions and other restrictions which make the problem even harder. In this paper, a novel multi-objective teaching learning based optimization (TLBO) algorithm has been successfully applied to several instances of the problem. For doing this, the software requirements selection problem has been formulated as a multiobjective optimization problem with two objectives: the total software development cost and the overall customer's satisfaction. In addition, three interaction constraints have been also managed. In this context, the original TLBO algorithm has been adapted to solve real instances of the problem generated from data provided by experts. Numerical experiments with case studies on software requirements selection have been carried out in order to prove the effectiveness of the multiobjective proposal. In fact, the obtained results show that the developed algorithm performs better than other relevant algorithms previously published in the literature. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Software requirements selection multi-objective evolutionary algorithm Teaching learning based optimization Search Based Software Engineering Next Release Problem Swarm intelligence

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multi-objective planning-operation co-optimization of renewable energy system with hybrid energy storages

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RENEWABLE ENERGY 2022年第0期184卷 776-790页

作者： He, Yi Guo, Su Zhou, Jianxu Ye, Jilei Huang, Jing Zheng, Kun Du, Xinru Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing 211000 Peoples R China Nanjing Technol Univ Sch Energy Sci & Engn Nanjing 211816 Peoples R China

In order to alleviate the resource depletion as well as achieve decarbonization, developing renewable energy system is a feasible solution. This paper establishes a wind-photovoltaic-battery-thermal energy storage hybrid power system, and investigates its multi-objective planning-operation co-optimization. The hybrid system utilizes the cost-effectiveness of thermal energy storage and flexibility of battery to jointly tackle the intermittency of renewable energy. A novel coordinated operation strategy based on the operation threshold of power block is proposed, and the planning-operation co-optimization model considers the minimization of net present cost and loss of power supply probability to determine the optimal operation threshold and sizing decision variables. The co-optimization problem is solved by a proposed multi-objective evolutionary algorithm with decision-making (MOEA-DM), which introduces the preference information of decision-maker to guide the evolution towards preferred region. Furthermore, the uncertainties and losses of wind power are captured by a data-driven forecast model. Finally, the results of case study show that: (1) the data-driven model performs higher accuracy in wind power forecast compared to commonly-used physical models;(2) The proposed MOEA-DM has better convergence, diversity and robustness performance in decision-maker's preferred region compared to widely-used Non-dominated Sorting Genetic algorithm-II (NSGA-II);(3) Hybrid battery-thermal energy storage system achieves better economy and reliability through the optimal coordinated operation strategy compared to either single energy storage under different test conditions. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Renewable energy Hybrid energy storage Operation threshold optimization Planning-operation co-optimization multi-objective evolutionary algorithm Data-driven model

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A multi-objective memetic algorithm based on locality-sensitive hashing for one-to-many-to-one dynamic pickup-and-delivery problem

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INFORMATION SCIENCES 2016年 329卷 73-89页

作者： Zhu, Zexuan Xiao, Jun He, Shan Ji, Zhen Sun, Yiwen Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Shenzhen Univ Dept Biomed Engn Shenzhen 518060 Peoples R China

This paper presents an early attempt to solve one-to-many-to-one dynamic pickup-and-delivery problem (DPDP) by proposing a multi-objective memetic algorithm called LSH-MOMA, which is a synergy of multi-objective evolutionary algorithm and locality-sensitive hashing (LSH) based local search. Three objectives namely route length, response time, and workload are optimized simultaneously in an evolutionary framework. In each generation of LSH-MOMA, LSH-based rectification and local search are imposed to repair and improve the individual solutions. LSH-MOMA is evaluated on four benchmark DPDPs and the experimental results show that LSH-MOMA is efficient in obtaining optimal tradeoff solutions of the three objectives. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Memetic algorithm multi-objective evolutionary algorithm Dynamic pickup and delivery problem Locality-sensitive hashing

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multi-objective Optimization of a Rotating Cooling Channel with Airfoil-Shaped Guide Vanes

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2013年第1期27卷 52-60页

作者： Moon, Mi-Ae Kim, Kwang-Yong Inha Univ Dept Mech Engn Inchon 402751 South Korea

A multi-objective design optimization of a rotating two-pass cooling channel with airfoil-shaped guide vanes in the turning region has been performed to enhance heat transfer and reduce pressure drop in the channel. An optimization procedure using three-dimensional Reynolds-averaged Navier Stokes analysis, a surrogate method, and a multi-objective evolutionary algorithm is presented. Two objective functions related to heat transfer and friction loss are considered to estimate the cooling performance of the rotating two-pass channel. For the optimization, four design variables, the angles and radii of the airfoil-shaped guide vanes, are selected. A comparison of the friction factors between the experimental data and numerical results for a smooth channel is made to validate the numerical results. The optimized airfoil-shaped guide vanes in the turning region reduce pressure loss and enhance heat transfer throughout the channel.

关键词： Heat transfer Optimization Procedure cooling channel multi-objective evolutionary algorithm PRESSURE LOSS friction factor cooling characteristic Two-Pass Friction losses objective function Optimal design guide vanes

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Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm

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JOURNAL OF CLEANER PRODUCTION 2016年第0期137卷 1516-1531页

作者： Lu, Chao Gao, Liang Li, Xinyu Chen, Peng Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

Energy savings have become an essential consideration in sustainable manufacturing projects due to the associated environmental impacts and constraints on carbon emissions. In the past, machining operations primarily examined technological consideration (e.g., machining quality) and neglected energy consumption. Therefore, this paper investigates an energy-efficient multi-pass turning operation problem and establishes a multi-objective multi-pass turning operations model. Energy consumption and machining quality are both considered in this problem. Although several models of this problem have considered these criteria, the objectives are usually combined into a single objective using a weighted sum approach, which results in poor non-dominated solutions. To obtain high quality trade-offs between the two challenging objectives, a novel multi-objective backtracking search algorithm is proposed to solve this multi-objective optimization problem. To verify the feasibility and validity of the proposed algorithm, it is compared with other classical multi-objective metaheuristics on multi-objective multi pass turning operations. This study's experimental results demonstrate that the proposed algorithm significantly outperforms other algorithms for this optimization problem, which is a significant result regarding practical application. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： multi-objective evolutionary algorithm Energy consumption Parameter optimization Backtracking search algorithm

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Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

ROBOTIC INTELLIGENCE AND AUTOMATION

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ROBOTIC INTELLIGENCE AND AUTOMATION 2024年第1期44卷 34-47页

作者： Cheng, Wenlong Meng, Wenjun Taiyuan Univ Sci & Technol Coll Mech Engn Taiyuan Peoples R China

PurposeThis study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing ***/methodology/approachIn this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid *** experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the ***/valueIn this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

关键词： multi-objective evolutionary algorithm Path optimization AGV collision avoidance multi-AGV system

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multiobjective differential evolution using homeostasis based mutation for application in software cost estimation

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APPLIED INTELLIGENCE 2018年第3期48卷 628-650页

作者： Singh, Shailendra Pratap Kumar, Anoj Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci & Engn Allahabad Uttar Pradesh India

The problem in software cost estimation revolves around accuracy. To improve the accuracy, heuristic/meta-heuristic algorithms have been known to yield better results when it is applied in the domain of software cost estimation. For the sake of accuracy in results, we are still modifying these algorithms. Here we have proposed a new meta-heuristic algorithm based on Differential Evolution (DE) by Homeostasis mutation operator. Software development requires high prediction and low Root Mean Squared Error (RMSE) and mean magnitude relative error(MMRE). The problem in software cost estimation relates to accurate prediction and minimization of RMSE and MMRE, which are used to solve multiobjective optimization. Many versions of DE were proposed, however multi-objective versions where the concept of Pareto optimality is used, are most popular. Pareto-Based Differential Evolution (PBDE) is one of them. Although the performance of this algorithm is very good, its convergence rate can be further improved by minimizing the time complexity of nondominated sorting, and by improving the diversity of solutions. This has been implemented by using efficient nondominated algorithm whose time complexity is better than the previous one and a new mutation scheme is implemented in DE which can provide more diversity among solutions. The proposed variant multiplies the Homeostasis value with one more vector, named the Homeostasis mutation vector, in the existing mutation vector to provide more bandwidth for selecting effective mutant solutions. The proposed approach provides more promising solutions to guide the evolution and helps DE escape the situation of stagnation. The performance of the proposed algorithm is evaluated on twelve benchmark test functions (bi-objective and tri-objective) on the Pareto-optimal front. The performance of the proposed algorithm is compared with other state-of-the-art algorithms on five multi-objective evolutionary algorithms (MOEAs). The result verifies t

关键词： Adaptation Optimization multi-objective evolutionary algorithm Software cost estimation

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Model-based computational intelligence multi-objective optimization for gasoline direct injection engine calibration

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2019年第6期233卷 1391-1402页

作者： Ma, He Li, Ziyang Tayarani, Mohamad Lu, Guoxiang Xu, Hongming Yao, Xin Univ Birmingham Sch Engn Birmingham B15 2TT W Midlands England Univ Birmingham Sch Comp Sci Birmingham W Midlands England

For modern engines, the number of adjustable variables is increasing considerably. With an increase in the number of degrees of freedom and the consequent increase in the complexity of the calibration process, traditional design of experiments-based engine calibration methods are reaching their limits. As a result, an automated engine calibration approach is desired. In this paper, a model-based computational intelligence multi-objective optimization approach for gasoline direct injection engine calibration is developed, which can optimize the engine's indicated specific fuel consumption, indicated specific particulate matter by mass, and indicated specific particulate matter by number simultaneously, by intelligently adjusting the engine actuators' settings through Strength Pareto evolutionary algorithm 2. A mean-value model of gasoline direct injection engine is developed in the author's earlier work and used to predict the performance of indicated specific fuel consumption, indicated specific particulate matter by mass, and indicated specific particulate matter by number with given value of intake valves opening timing, exhaust valves closing timing, spark timing, injection timing, and rail pressure. Then a co-simulation platform is established for the introduced intelligence engine calibration approach in the given engine operating condition. The co-simulation study and experimental validation results suggest that the developed intelligence calibration approach can find the optimal gasoline direct injection engine actuators' settings with acceptable accuracy in much less time, compared to the traditional approach.

关键词： Computational intelligence multi-objective evolutionary algorithm gasoline direct injection engine calibration

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