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An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem 认领 引用 被引量：11
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作者 Bingjie Li Guohua Wu +2 位作者 Yongming He Mingfeng Fan Witold Pedrycz 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第7期1115-1138,共24页
The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contribute... The vehicle routing problem(VRP)is a typical discrete combinatorial optimization problem,and many models and algorithms have been proposed to solve the VRP and its variants.Although existing approaches have contributed significantly to the development of this field,these approaches either are limited in problem size or need manual intervention in choosing parameters.To solve these difficulties,many studies have considered learning-based optimization(LBO)algorithms to solve the VRP.This paper reviews recent advances in this field and divides relevant approaches into end-to-end approaches and step-by-step approaches.We performed a statistical analysis of the reviewed articles from various aspects and designed three experiments to evaluate the performance of four representative LBO algorithms.Finally,we conclude the applicable types of problems for different LBO algorithms and suggest directions in which researchers can improve LBO algorithms. 展开更多
关键词 End-to-end approaches learning-based optimization(LBO)algorithms reinforcement learning step-by-step approaches vehicle routing problem(VRP)
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Human-Robot Collaborative Planning for Navigation Based on Optimal Control Theory 认领 引用
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作者 Sousso Kelouwani 《Open Journal of Optimization》 2013年第3期72-79,共8页
Navigation modules are capable of driving a robotic platform without direct human participation. However, for some specific contexts, it is preferable to give the control to a human driver. The human driver participat... Navigation modules are capable of driving a robotic platform without direct human participation. However, for some specific contexts, it is preferable to give the control to a human driver. The human driver participation in the robotic control process when the navigation module is running raises the share control issue. This work presents a new approach for two agents collaborative planning using the optimal control theory and the three-layer architecture. In particular, the problem of a human and a navigation module collaborative planning for a trajectory following is analyzed. The collaborative plan executed by the platform is a weighted summation of each agent control signal. As a result, the proposed architecture could be set to work in autonomous mode, in human direct control mode or in any aggregation of these two operating modes. A collaborative obstacle avoidance maneuver is used to validate this approach. The proposed collaborative architecture could be used for smart wheelchairs, telerobotics and unmanned vehicle applications. 展开更多
关键词 Robotic Architecture Share Control Three-Layer Architecture Cooperative Control Collaborative Control Optimal Control
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A Step-by-Step Optimization Process to Fabricate Narrow Sized Dual Drug Loaded Polymeric Nanoparticles Using Modified Nanoprecipitation Technique 认领 引用 被引量：2
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作者 Moorthi Chidambaram Kathiresan Krishnasamy 《Nano Biomedicine & Engineering》 2013年第3期107-115,共9页
The primary aim of the study was to prepare narrow sized polymeric nanoparticles by implementing few modifications to the conventional nanoprecipitation technique and to evaluate the effect of various process paramete... The primary aim of the study was to prepare narrow sized polymeric nanoparticles by implementing few modifications to the conventional nanoprecipitation technique and to evaluate the effect of various process parameters on prepared polymeric nanoparticles.Eudragit E 100 nanoparticles were prepared by modified nanoprecipitation technique and step-by-step optimization was carried out to evaluate the effect of various process parameters such as organic solvent,polymer concentration,percentage of organic solvent,mode of addition of organic solvent in to aqueous phase,volume of aqueous phase,poloxamer 188 concentration,β-cyclodextrin concentration,temperature generated during sonication process,sonication duration,and drug concentration on the particle size,surface area,distribution width and uniformity of the prepared nanoparticles.The optimized process parameters were implemented to fabricate dual drug loaded Eudragit E 100 nanoparticles which were spherical in shape with mean particle size in the range of 118 to 140 nm,polydispersity index in the range of 0.187 to 0.254 and zeta potential in the range of 16.6 to 28.8 mV.Thus developed modified nanoprecipitation method can be used to fabricate narrow sized polymeric nanoparticles. 展开更多
关键词 Dual drug loaded polymeric nanoparticles Eudragit E 100 Modified nanoprecipitation method Step-by-step optimization
Cascade model for continuous prediction of silicon content of molten iron with coupled state variable nodes 认领 引用 被引量：3
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作者 Yang Han Ze-qian Cui +3 位作者 Li-jing Wang Jie Li Ai-min Yang Yu-zhu Zhang 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2023年第5期897-914,共18页
With the goal of achieving advanced and multi-step prediction of silicon content of molten iron in the blast furnace ironmaking process,a path adaptive optimization seeking strategy coupled with simulated annealing al... With the goal of achieving advanced and multi-step prediction of silicon content of molten iron in the blast furnace ironmaking process,a path adaptive optimization seeking strategy coupled with simulated annealing algorithm and genetic algorithm was proposed from the perspective of innovative intelligent algorithm application.It was further coupled with wavelet neural network algorithm to deeply explore the nonlinear and strong coupling relationship between the information of big data samples and construct a cascade model for continuous prediction of silicon content of molten iron with the intelligent research results of state variables such as permeability index as the node and silicon content forecast as the output.In the model construction process,the 3r criterion was used for non-anomaly estimation of abnormal data to build a time-aligned sample set for multi-step forecasting of iron content,the normalization method was used to eliminate the influence of dimensionality of sample information,and the spearman correlation analysis algorithm was used to eliminate the time delay between state variables,control variables,and silicon content of molten iron in the blast furnace smelting process.The results show that permeability and theoretical combustion temperature as the key state variable nodes have real-time correlation with the silicon content of molten iron,and there are accurate forecasting results on the optimal path with the endpoint of molten iron silicon content prediction.The path finding based on the improved genetic algorithm of simulated annealing has good effect on the downscaling and depth characterization of sample data and improves the data ecology for the application of wavelet neural network algorithm.The accuracy of the real-time continuous forecasting model for the silicon content of molten iron reaches 95.24%;the hit rate of continuous forecasting one step ahead reaches 91.16%,and the hit rate of continuous forecasting five steps ahead is 87.41%.This model,which can realize the nodal dynamics of state variables,has better promotion value. 展开更多
关键词 Silicon content Molten iron Optimal path State variable node Wavelet neural network Step-by-step prediction
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Three-Layer Multi-UAVs Path Planning Based on ROBL-MFO 认领 引用 被引量：5
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作者 Salvador N.Obama Oyana Jun Li Muhammad Usman 《Guidance, Navigation and Control》 2022年第3期106-134,共29页
This paper proposes a new three-layer path planning method,where we fused two existing path planning methods(global path and local path)into a single problem for multi-unmanned aerial vehicles(UAVs)path planning for U... This paper proposes a new three-layer path planning method,where we fused two existing path planning methods(global path and local path)into a single problem for multi-unmanned aerial vehicles(UAVs)path planning for UAV.The global-path network layer contains the latest information and algorithms for global planning according to specific applications.The trajectory planning layer represents the kinematics and different motion characteristics,the planningexecution layer implements the local planning algorithm for obstacle avoidance.In the last layer,we propose a new swarm intelligence algorithm called the refraction principle and opposite-based-learning moth flame optimization(ROBL-MFO).In contrast to the classical MFO,the proposed algorithm addresses the shortcoming of the classical MFO algorithm.First,it adapts the moth position update formula to the notion of historical optimal flame average and improves the convergence speed of the algorithm.Second,it utilizes a random inverse learning strategy to narrow down the search space.Finally,the principle of refraction gives the algorithm the ability to jump out of local optima and helps the algorithm avoid premature convergence.The experimental results show that the performance of the proposed algorithm is versatile,robust,and stable. 展开更多
关键词 Refraction principle and opposite-based-learning moth flame optimization multiUAVs optimal path planning three-layer
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