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A Global Approach to the Optimal Trajectory Based on an Improved Ant Colony algorithm for Cold Spray

JOURNAL OF THERMAL SPRAY TECHNOLOGY 2016年第8期25卷 1631-1637页

作者： Cai, Zhenhua Chen, Tingyang Zeng, Chunnian Guo, Xueping Lian, Huijuan Zheng, You Wei, Xiaoxu Wuhan Univ Technol Automat Sch Wuhan 430000 Peoples R China Wuhan Univ Technol Informat Sch Wuhan 430000 Peoples R China Jimei Univ Marine Engn Coll Xiamen 361021 Peoples R China Ningbo Univ Technol Ningbo 315000 Zhejiang Peoples R China

This paper is concerned with finding a global approach to obtain the shortest complete coverage trajectory on complex surfaces for cold spray applications. A slicing algorithm is employed to decompose the free-form complex surface into several small pieces of simple topological type. The problem of finding the optimal arrangement of the pieces is translated into a generalized traveling salesman problem (GTSP). Owing to its high searching capability and convergence performance, an improved ant colony algorithm is then used to solve the GTSP. Through off-line simulation, a robot trajectory is generated based on the optimized result. The approach is applied to coat real components with a complex surface by using the cold spray system with copper as the spraying material.

关键词： cold spray intelligent algorithm robot trajectory traveling salesman problem

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Optimization of geometry quality model for wire and arc additive manufacture based on adaptive multi-objective grey wolf algorithm

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SOFT COMPUTING 2020年第22期24卷 17401-17416页

作者： Zhao, Yun-tao Li, Wei-gang Liu, Ao Minist Educ Engn Res Ctr Met Automat & Measurement Technol Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Sch Management Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn 947 Heping Rd Wuhan 430081 Hubei Peoples R China

In order to obtain good geometry quality in the wire and arc additive manufacture, it is important to select the appropriate process parameters. Firstly, based on the 4-factor and 5-level experiments, the multi-objective mathematical model of process parameters and geometry quality is established by response surface methodology. Secondly, an adaptive grey wolf algorithm for solving multi-objective problems is proposed. The algorithm introduces external Archive, adaptive hunting mechanism, and fusion polynomial mutation mechanism to improve the search ability of the grey wolf algorithm. Experiments show that the Pareto set obtained by the adaptive multi-objective grey wolf algorithm is more diverse and convergent than the other five well-known algorithms. Meanwhile, in order to obtain the desired geometry quality, the TOPSIS algorithm is used to analyze the Pareto set obtained to get the optimal process parameters.

关键词： Wire and arc additive manufacture intelligent algorithm Multi-objective grey wolf algorithm TOPSIS

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Decision-making of transportation vehicle routing based on particle swarm optimization algorithm in logistics distribution management

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2023年第6期26卷 3707-3718页

作者： Cai, Linya Baotou Tech Coll Sch E Commerce Baotou 014035 Inner Mongolia Peoples R China

Path planning, as the core of transportation vehicle routing decisions in logistics distribution management, has become a hotspot in logistics and distribution. In recent years, particle swarm optimization (PSO) has been proposed. In this paper, a heuristic elastic PSO algorithm was proposed for the advantages and disadvantages of different path planning algorithms. The A* algorithm was used to globally guide the path planning in the large-scale grid. The elastic PSO algorithm used the contraction operation to determine the globally optimal path formed by the local optimal nodes so that the particles can converge quickly. In addition, during the iterative process, the diversity of the particles was ensured by the rebound operation. This paper ensured the diversity of particles through rebound operation. Computer simulation and experimental results showed that the algorithm not only overcomes the disadvantage that the A* algorithm can not produce the shortest path but also avoids the problem that it can not converge to the globally optimal path due to the lack of heuristic. In addition, the algorithm indicated the simplicity and efficiency of the two algorithms. Compared with the genetic algorithm, the results showed that EPSO algorithm improved the convergence speed of particle diversity of the PSO algorithm. The global optimal path can be quickly obtained through the contraction and rebound operations of EPSO algorithm. This algorithm overcomes the shortcoming that the PSO algorithm was easy to falls into optimal local solution.

关键词： PSO intelligent algorithm Logistics and distribution Path planning

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An Opposition-Based Learning Adaptive Chaotic Particle Swarm Optimization algorithm

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Journal of Bionic Engineering 2024年第6期21卷 3076-3097页

作者： Chongyang Jiao Kunjie Yu Qinglei Zhou State Key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering UniversityZhengzhou450001China Henan Information Engineering School Zhengzhou Vocational College of Industrial SafetyZhengzhou450000China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou450001China

To solve the shortcomings of Particle Swarm Optimization(PSO)algorithm,local optimization and slow convergence,an Opposition-based Learning Adaptive Chaotic PSO(LCPSO)algorithm was *** chaotic elite opposition-based learning process was applied to initialize the entire population,which enhanced the quality of the initial individuals and the population diversity,made the initial individuals distribute in the better quality areas,and accelerated the search efficiency of the *** inertia weights were adaptively customized during evolution in the light of the degree of premature convergence to balance the local and global search abilities of the algorithm,and the reverse search strategy was introduced to increase the chances of the algorithm escaping the local *** LCPSO algorithm is contrasted to other intelligent algorithms on 10 benchmark test functions with different characteristics,and the simulation experiments display that the proposed algorithm is superior to other intelligence algorithms in the global search ability,search accuracy and convergence *** addition,the robustness and effectiveness of the proposed algorithm are also verified by the simulation results of engineering design problems.

关键词： PSO Opposition-based learning Chaotic motion Inertia weight intelligent algorithm

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A systematic approach of process planning and scheduling optimization for sustainable machining

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JOURNAL OF CLEANER PRODUCTION 2015年第1期87卷 914-929页

作者： Wang, S. Lu, X. Li, X. X. Li, W. D. Coventry Univ Fac Engn & Comp Coventry CV1 5FB W Midlands England

The lack of effective process planning and scheduling solutions for the sustainable management of machining shop floors, whose manufacturing activities are usually characterized by high variety and low volume, has been crippling the implementation of sustainability in companies. To address the issue, an innovative and systematic approach for milling process planning and scheduling optimization has been developed and presented in this paper. This approach consists of a process stage and a system stage, augmented with intelligent mechanisms for enhancing the adaptability and responsiveness to job dynamics in machining shop floors. In the process stage, key operational parameters for milling a part are optimized adaptively to meet multiple objectives/constraints, i.e., energy efficiency of the milling process and productivity as objectives and surface quality as a constraint. In the consecutive system stage, to achieve higher energy efficiency and shorter makespan in the entire shop floor, sequencing/set-up planning of machining 'features/operations and scheduling for producing multiple parts on different machines are optimized. Artificial Neural Networks are used for establishing the complex nonlinear relationships between the key process parameters and measured datasets of energy consumption and surface quality. Several intelligent algorithms, including Pattern Search, Genetic algorithm and Simulated Annealing, are applied and benchmarked to identify optimal solutions. Experimental tests indicate that the approach is effective and configurable to meet multiple objectives and technical constraints for sustainable process planning and scheduling. The approach, validated through industrial case studies provided by a European machining company, demonstrates significant potential of applicability in practice. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Sustainable manufacturing Computer numerical control machining Process planning Process scheduling intelligent algorithm Machining feature

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A Reliability Evaluation Method for Intermittent Jointed Rock Slope Based on Evolutionary Support Vector Machine

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Computer Modeling in Engineering & Sciences 2021年第10期129卷 149-166页

作者： Shuai Zheng An-Nan Jiang Kai-Shuai Feng College of Transportation Engineering Dalian Maritime UniversityDalian116026China

The randomness of rock joint development is an important factor in the uncertainty of geotechnical engineering *** this study,a method is proposed to evaluate the reliability of intermittent jointed rock *** least squares support vector machine(LSSVM)evolved by a bacterial foraging optimization algorithm(BFOA)is used to establish a response surface model to express the mapping relationship between the intermittent joint parameters and the slope safety *** training samples are obtained from the numerical calculation based on the joint finite element method during this *** the randomness of the intermittent joint parameters in the actual project,each parameter is evaluated at different locations on the site,and its distribution characteristics are *** to these statistical results,a large number of parameter combinations are obtained through Monte Carlo *** trained machine learning mapping model is used to obtain the slope safety factor corresponding to each group,and these results are then used to obtain the slope *** the research results were applied to slope disaster treatment along the Yalu River in China’s Jilin Province,it was found that the joint length and joint inclination angle both play key roles in rock slope stability,which should receive more attention in the slope *** summary,this study establishes a method for evaluating the reliability of intermittent jointed rock slope based on an evolutionary SVM model,and its feasibility is verified by engineering application.

关键词： Slope reliability intermittent joint safety factor intelligent algorithm parameter sensitivity

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Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第13期41卷 5804-5816页

作者： Luo, Jian-ping Li, Xia Chen, Min-rong Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China

Cloud computing aims to provide dynamic leasing of server capabilities as scalable virtualized services to end users. However, data centers hosting cloud applications consume vast amounts of electrical energy, thereby contributing to high operational costs and carbon footprints. Green cloud computing solutions that can not only minimize the operational costs but also reduce the environmental impact are necessary. This study focuses on the Infrastructure as a Service model, where custom virtual machines (VMs) are launched in appropriate servers available in a data center. A complete data center resource management scheme is presented in this paper. The scheme can not only ensure user quality of service (through service level agreements) but can also achieve maximum energy saving and green computing goals. Considering that the data center host is usually tens of thousands in size and that using an exact algorithm to solve the resource allocation problem is difficult, the modified shuffled frog leaping algorithm and improved extremal optimization are employed in this study to solve the dynamic allocation problem of VMs. Experimental results demonstrate that the proposed resource management scheme exhibits excellent performance in green cloud computing. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Cloud computing Data center Resource management intelligent algorithm

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Installed performance seeking control based on supersonic variable inlet/engine coupling model

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INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES 2024年第2期41卷 189-200页

作者： Wang, Chen Sun, Ximing Du, Xian Dalian Univ Technol Sch Control Sci & Engn Room B701Chuang Xin Bldg 2 Dalian 116024 Peoples R China Dalian Univ Technol Dalian Peoples R China

With the advancement of the supersonic aero propulsion system, optimizing the combined performance of inlet/engine integration has become increasingly crucial. To solve the coupling inlet/engine problem, a quasi-one-dimensional inlet modeling and drag calculation method are proposed, integrated performance seeking control (PSC) based on the neighborhood-based speciation differential evolution-grey wolf optimizer (NSDE-GWO) is presented and quantitatively analyses the influence of variable geometry inlet regulation on performance. The results reveal that the optimization effect of the ramp angle adjustment is generally better than that of the bleed adjustment, and the NSDE-GWO hybrid algorithm achieves remarkable optimization solutions in all three different modes. The PSC with variable geometry inlet adjustment provides more additional potential for optimization compared with fixed geometry inlet, and the performance can be maximized by adjusting both the bleed adjustment and the ramp angle. This study maximizes the exploitation of potential and has theoretical guidance and practical engineering significance.

关键词： inlet engine coupling intelligent algorithm performance seeking control propulsion system supersonic variable inlet

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Optimal control of rapid cooperative spacecraft rendezvous with multiple specific-direction thrusts

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2020年第16期234卷 2296-2322页

作者： Su, Piaoyi Feng, Weiming Kun, Yang Zhao, Junfeng Shandong Univ Dept Engn Mech Jinan 250062 Peoples R China

Focusing on the long-distance rapid cooperative rendezvous of two spacecraft under finite continuous thrust, this paper proposes a practical strategy for space operation using multiple specific-direction thrusts. Based on the orbital dynamic theory and Pontryagin's maximum principle, the dynamic equations and optimal control equations for radial, circumferential, and normal thrust are determined. The optimization method is a hybrid algorithm. The initial costate variables for the fuel-optimal rapid cooperative rendezvous problem are obtained using quantum particle swarm optimization and subsequently set as the initial values in the sequence quadratic programming to search for the exact convergent solution. The elliptical and near-circular mission orbital rendezvous for spacecraft with multiple specific-direction thrusts are simulated and optimized. Numerical examples verifying the proposed method are provided. The results facilitate easier realization of rapid spacecraft maneuvering under continuous thrust conditions.

关键词： Multiple specific-direction thrusts rapid cooperative rendezvous hybrid method optimal control intelligent algorithm

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EXACT METHOD BASED ON SOLUTION SPACE CUT FOR BI-OBJECTIVE SERU PRODUCTION

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2023年第12期19卷 8709-8730页

作者： Yu, Zixuan Sun, Wei Zhou, Jianwen Liu, Yang Yu, Jiao Li, Xiaolong Northeastern Univ Dept Intelligent Data & Syst Engn State Key Lab Synthet Automat Proc Ind Shenyang Liaoning Peoples R China Liaoning Univ Business Sch Shenyang Liaoning Peoples R China Beijing Inst Comp Technol & Applicat Beijing Peoples R China Ecol Environm Bur Chifeng City Chifeng Inner Mongolia Peoples R China

Seru Production is an innovative production mode frequently utilized in electronic assembly enterprises. Up to now, there is no exact algorithm for the bi-objective Seru Production. We develop an innovative exact method based on decomposition and cutting solution space to obtain the exact Pareto-optimal solutions for the first time. Seru Production is decomposed into two decision processes, i.e., seru formation and seru scheduling. We define dominated seru formations and cut 97% dominated seru formations based on the ideal point and Non-Dominated Incumbent Solutions (NDIS). Thus, the Pareto-optimal solutions is obtained by executing Non-dominant Sort (NS) for all the non-dominated seru formations. The epsilon-constraint method is applied in seru scheduling to obtain the Pareto-optimal schedules for a given seru formation. In addition, the current best algorithms for solving multi-objective Seru Production are used to ensure the initial NDIS high-quality. Finally, parallel programming is performed according to the characteristics of Seru Production. Test data verify that proposed exact method can find the Pareto-optimal solution set for all small-scale and most medium-scale instances of bi-objective Seru Production.

关键词： Seru Production bi-objective programming exact method so-lution space cut seru formation dominance intelligent algorithm parallel pro-gramming

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