Integrated Clustering and Routing Design and Triangle Path Optimization for UAV-Assisted Wireless Sensor Networks

With the development of the Internet of Things(IoT),it requires better performance from wireless sensor networks(WSNs),such as larger coverage,longer lifetime,and lower latency.However,a large amount of data generated from monitoring and long-distance transmission places a heavy burden on sensor nodes with the limited battery power.For this,we investigate an unmanned aerial vehicles assisted mobile wireless sensor network(UAV-assisted WSN)to prolong the network lifetime in this paper.Specifically,we use UAVs to assist the WSN in collecting data.In the current UAV-assisted WSN,the clustering and routing schemes are determined sequentially.However,such a separate consideration might not maximize the lifetime of the whole WSN due to the mutual coupling of clustering and routing.To efficiently prolong the lifetime of the WSN,we propose an integrated clustering and routing scheme that jointly optimizes the clustering and routing together.In the whole network space,it is intractable to efficiently obtain the optimal integrated clustering and routing scheme.Therefore,we propose the Monte-Las search strategy based on Monte Carlo and Las Vegas ideas,which can generate the chain matrix to guide the algorithm to find the solution faster.Unnecessary point-to-point collection leads to long collection paths,so a triangle optimization strategy is then proposed that finds a compromise path to shorten the collection path based on the geometric distribution and energy of sensor nodes.To avoid the coverage hole caused by the death of sensor nodes,the deployment of mobile sensor nodes and the preventive mechanism design are indispensable.An emergency data transmission mechanism is further proposed to reduce the latency of collecting the latency-sensitive data due to the absence of UAVs.Compared with the existing schemes,the proposed scheme can prolong the lifetime of the UAVassisted WSN at least by 360%,and shorten the collection path of UAVs by 56.24%.

The Existing Problems and Optimization Paths of Digital Textbooks Under Background of Internet

With the rapid development of information technology,the combination of terminal technology,big data and mobile Internet and textbooks has become an irresistible trend in the modern education field.Under the context of the Internet,carrying out education and teaching activities based on digital textbooks can give full play to the rich media,openness and interaction of digital textbooks,broaden students′horizon,enrich students′knowledge,and promote the improvement of students′ability and all-round development.However,in the specific teaching practice,there are also problems such as old compilation ideas,single compilation mode and low efficiency of personalized learning.Therefore,schools and teachers need to constantly innovate the presentation and arrangement of digital textbooks,strengthen technical support,deepen students′understanding of the teaching content of digital textbooks,promote the comprehensive development of students and improve the effectiveness of digital textbook teaching.

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

LSTM-DPPO based deep reinforcement learning controller for path following optimization of unmanned surface vehicle

To solve the path following control problem for unmanned surface vehicles(USVs),a control method based on deep reinforcement learning(DRL)with long short-term memory(LSTM)networks is proposed.A distributed proximal policy opti-mization(DPPO)algorithm,which is a modified actor-critic-based type of reinforcement learning algorithm,is adapted to improve the controller performance in repeated trials.The LSTM network structure is introduced to solve the strong temporal cor-relation USV control problem.In addition,a specially designed path dataset,including straight and curved paths,is established to simulate various sailing scenarios so that the reinforcement learning controller can obtain as much handling experience as possible.Extensive numerical simulation results demonstrate that the proposed method has better control performance under missions involving complex maneuvers than trained with limited scenarios and can potentially be applied in practice.

Investigating the Influence of Digital Economy Growth on Industrial Structural Optimization and Upgrading

With the rapid development of the social economy,science and technology continue to upgrade and optimize,ushering in the digital era,which provides technical support for industrial innovation and development across all sectors.At this stage,vigorously developing the digital economy has gradually become the only means to optimize and upgrade the industrial structure.Therefore,local leaders and relevant departments need to enhance the importance of constructing the digital economy,enabling the local industrial structure to be optimized and upgraded under the impetus of the digital economy,ultimately promoting overall economic high-quality development.To this end,this paper,combined with existing research results,first elaborates on the positive impact of the digital economy on the optimization and upgrading of the industrial structure.It then analyzes the challenges hindering the process of industrial structure optimization and upgrading and proposes practical pathways to address them,benefiting relevant stakeholders.

Multi-Objective Redundancy Optimization of Continuous-Point Robot Milling Path in Shipbuilding

The 6-DOF manipulator provides a new option for traditional shipbuilding for its advantages of vast working space,low power consumption,and excellent flexibility.However,the rotation of the end effector along the tool axis is functionally redundant when using a robotic arm for five-axis machining.In the process of ship construction,the performance of the parts’protective coating needs to bemachined tomeet the Performance Standard of Protective Coatings(PSPC).The arbitrary redundancy configuration in path planning will result in drastic fluctuations in the robot joint angle,greatly reducing machining quality and efficiency.There have been some studies on singleobjective optimization of redundant variables,However,the quality and efficiency of milling are not affected by a single factor,it is usually influenced by several factors,such as the manipulator stiffness,the joint motion smoothness,and the energy consumption.To solve this problem,this paper proposed a new path optimization method for the industrial robot when it is used for five-axis machining.The path smoothness performance index and the energy consumption index are established based on the joint acceleration and the joint velocity,respectively.The path planning issue is formulated as a constrained multi-objective optimization problem by taking into account the constraints of joint limits and singularity avoidance.Then,the path is split into multiple segments for optimization to avoid the slow convergence rate caused by the high dimension.An algorithm combining the non-dominated sorting genetic algorithm(NSGA-II)and the differential evolution(DE)algorithm is employed to solve the above optimization problem.The simulations validate the effectiveness of the algorithm,showing the improvement of smoothness and the reduction of energy consumption.

Types and Optimization Paths Between Poverty Alleviation Effectiveness and Rural Revitalization:A Case Study of Hunan Province,China

Exploring the synergy types and optimization paths between Poverty Alleviation Effectiveness and Rural Revitalization is necessary for achieving the two centenary goals.Taking poverty alleviation counties in Hunan Province,China as an example,our study proposed an indicator to measure the synergistic development between Poverty Alleviation Effectiveness and Rural Revitalization using the multi-index integrated evaluation method.Then,the coupling types were classified based on both the proposed indicator and regional characteristics.Besides,the corresponding optimization path for each coupling type was proposed to promote the synergistic development of Poverty Alleviation and Rural Revitalization.Results are as follows:1)Lower synergy focused on the southwestern Hunan,while low synergy is widely distributed(such as the west,southwest,northwest,and midland).Moderate synergy is in the midland,such as Huaihua and Chenzhou cities.High synergy is distributed in Yongzhou,Huaihua,Xiangxi cities,etc.Besides,only Hecheng City belongs to the higher synergy.2)This paper proposes corresponding development paths for different development characteristics and main problems from multiple perspectives of the protection system,industrial planning,and rural market.Continuously consolidate and enhance the effectiveness of Poverty Alleviation and Rural Revitalization to achieve coupled and synergistic development of the two systems.Our research results can provide theoretical support for implementing Poverty Alleviation and Rural Revitalization in Hunan Province,China.

Research on dual-command operation path optimization based on Flying-V warehouse layout

To enhance the efficiency of warehouse order management,this study investigates a dual-com-mand operation mode in the Flying-V non-traditional warehouse layout.Three dual-command opera-tion strategies are designed,and a dual-command operation path optimization model is established with the shortest path as the optimization goal.Furthermore,a genetic algorithm based on a dynamic decoding strategy is proposed.Simulation results demonstrate that the Flying-V layout warehouse management and access cooperation operation can reduce the operation time by an average of 25%-35%compared with the single access operation path,and by an average of 13%-23%compared with the‘deposit first and then pick’operation path.These findings provide evidence for the effec-tiveness of the optimization model and algorithm.

Construction of Legal System of China's Farmland Protection under the Coexistence of Multiple Objectives:Historical Logic,Practical Problems and Optimization Paths

[Objectives]To explore the evolution of the legal system of farmland protection and explore the rules and characteristics of policy development based on the theory and logic of institutional change since China's reform and opening up,reveal the problems and deep-seated reasons of its legislation,clarify the direction of farmland protection in the new period,and solve the"non-agricultural""non-grain"and ecological problems of farmland.[Methods]Literature analysis and inductive deduction methods were used.[Results]The evolution of the farmland protection legal system has gone through the process of"national consciousness-policy guidelines-institutional system",the change from"single subject to multiple subjects";change from the use of"one-way administrative means to coordinated use of administrative,economic and technical means".The practical problems of the farmland protection legal system are mainly due to the insufficient systematization of the farmland protection legal system itself,the generalization of quantity protection,the transformation of quality protection,and the absence of ecological protection.[Conclusions]It is recommended to improve the existing farmland protection legal system from the establishment of the Farmland Protection Law,the improvement of the farmland protection public participation mechanism and supervision mechanism,the establishment of the farmland quality construction and improvement system,the differentiated farmland occupation and supplementation balance system,and the ecological restoration system.

Research on the Teaching Optimization Path in Children’s Creative Art Curriculum Under the Perspective of“Sanquan Education”

Children’s creative art curriculum is a professional course supported by art materials and techniques and facilitated through creative activities,aiming at cultivating students’ability to organize creative art activities using various art materials and techniques.In light of the evolving focus in China’s colleges and universities from“what to know”to“how to apply,”the pivotal question for educators is how to maximize the value of children’s creative art curriculum.Based on this,this article takes“Sanquan education”as the starting point,combines factual and theoretical arguments,and analyzes the teaching optimization path of children’s creative art curriculum under the perspective of Sanquan education,to improve the quality and efficiency of teaching.

Traffic Management in Internet of Vehicles Using Improved Ant Colony Optimization

The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion.Therefore,optimal path selection to route traffic between the origin and destination is vital.This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access.Firstly,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal routes.Considering the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path.The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path.Thus,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.

Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization

This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.

Path Planning of Continuum Robot Based on a New Improved Particle Swarm Optimization Algorithm

Continuum robot is a new type of biomimetic robot,which realizes the motion by bending some parts of its body.So its path planning becomes more difficult even compared with hyper-redundant robots.In this paper a circular arc spline interpolating method is proposed for the robot shape description,and a new two-stage position-selectable-updating particle swarm optimization(TPPSO)algorithm is put forward to solve this path planning problem.The algorithm decomposes the standard PSO velocity’s single-step updating formula into twostage multi-point updating,specifically adopting three points as candidates and selecting the best one as the updated position in the first half stage,and similarly taking seven points as candidates and selecting the best one as the final position in the last half stage.This scheme refines and widens each particle’s searching trajectory,increases the updating speed of the individual best,and improves the converging speed and precision.Aiming at the optimization objective to minimize the sum of all the motion displacements of every segmental points and all the axial stretching or contracting displacements of every segment,the TPPSO algorithm is used to solve the path planning problem.The detailed solution procedure is presented.Numerical examples of five path planning cases show that the proposed algorithm is simple,robust,and efficient.

Improved Ant Colony-Genetic Algorithm for Information Transmission Path Optimization in Remanufacturing Service System

The information transmission path optimization(ITPO) can often a ect the e ciency and accuracy of remanufactur?ing service. However, there is a greater degree of uncertainty and complexity in information transmission of remanu?facturing service system, which leads to a critical need for designing planning models to deal with this added uncer?tainty and complexity. In this paper, a three?dimensional(3D) model of remanufacturing service information network for information transmission is developed, which combines the physic coordinate and the transmitted properties of all the devices in the remanufacturing service system. In order to solve the basic ITPO in the 3D model, an improved 3D ant colony algorithm(Improved AC) was put forward. Moreover, to further improve the operation e ciency of the algorithm, an improved ant colony?genetic algorithm(AC?GA) that combines the improved AC and genetic algorithm was developed. In addition, by taking the transmission of remanufacturing service demand information of certain roller as example, the e ectiveness of AC?GA algorithm was analyzed and compared with that of improved AC, and the results demonstrated that AC?GA algorithm was superior to AC algorithm in aspects of information transmission delay, information transmission cost, and rate of information loss.

Theoretical and Experimental Research on Error Analysis and Optimization of Tool Path in Fabricating Aspheric Compound Eyes by Precision Micro Milling

Structure design and fabricating methods of three-dimensional （3D） artificial spherical compound eyes have been researched by many scholars. Micro-nano optical manufacturing is mostly used to process 3D artificial compound eyes. However, spherical optical compound eyes are less at optical performance than the eyes of insects, and it is difficult to further improve the imaging quality of compound eyes by means of micro-nano optical manufacturing. In this research, nonhomogeneous aspheric compound eyes （ACEs） are designed and fabricated. The nonhomogeneous aspheric structure is applied to calibrate the spherical aberration. Micro milling with advantages in processing three-dimensional micro structures is adopted to manufacture ACEs. In order to obtain ACEs with high imaging quality, the tool paths are optimized by analyzing the influence factors consisting of interpolation allowable error, scallop height and tool path pattern. In the experiments, two kinds of ACEs are manufactured by micro-milling with different too path patterns and cutting parameter on the miniature precision five-axis milling machine tool. The experimental results indicate that the ACEs of high surface quality can be achieved by circularly milling small micro-lens individually with changeable cutting depth. A prototype of the aspheric compound eye （ACE） with surface roughness （Ra） below 0.12 p.m is obtained with good imaging performance. This research ameliorates the imaging quality of 3D artificial compound eyes, and the proposed method of micro-milling can improve surface processing quality of compound eyes.

Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution routes.This article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational costs.To overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational costs.The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function suite.HFSHO outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path optimization.This comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization scenarios.These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient choice.Through the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics operations.It presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.

GPU-accelerated scanning path optimization in particle cancer therapy

When using the beam scanning method for particle beam therapy, the target volume is divided into many iso-energy slices and is irradiated slice by slice. Each slice may comprise thousands of discrete scanning beam positions. An optimized scanning path can decrease the transit dose and may bypass important organs. The minimization of the scanning path length can be considered as a variation of the traveling salesman problem; the simulated annealing algorithm is adopted to solve this problem. The initial scanning path is assumed as a simple zigzag path;subsequently, random searches for accepted new paths are performed through cost evaluation and criteria-based judging. To reduce the optimization time of a given slice,random searches are parallelized by employing thousands of threads. The simultaneous optimization of multiple slices is realized by using many thread blocks of generalpurpose computing on graphics processing units hardware.Running on a computer with an Intel i7-4790 CPU and NVIDIA K2200 GPU, our new method required only 1.3 s to obtain optimized scanning paths with a total of 40 slices in typically studied cases. The procedure and optimization results of this new method are presented in this work.

An Enhanced Equilibrium Optimizer for Solving Optimization Tasks

The equilibrium optimizer(EO)represents a new,physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equilibrium.Despite its innovative foundation,the EO exhibits certain limitations,including imbalances between exploration and exploitation,the tendency to local optima,and the susceptibility to loss of population diversity.To alleviate these drawbacks,this paper introduces an improved EO that adopts three strategies:adaptive inertia weight,Cauchy mutation,and adaptive sine cosine mechanism,called SCEO.Firstly,a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance between exploration and exploitation.Next,an adaptive sine cosine mechanism is embedded to boost the global exploratory capacity.Finally,the Cauchy mutation is utilized to prevent the loss of population diversity during searching.To validate the efficacy of the proposed SCEO,a comprehensive evaluation is conducted on 15 classical benchmark functions and the CEC2017 test suite.The outcomes are subsequently benchmarked against both the conventional EO,its variants,and other cutting-edge metaheuristic techniques.The comparisons reveal that the SCEO method provides significantly superior results against the standard EO and other competitors.In addition,the developed SCEO is implemented to deal with a mobile robot path planning(MRPP)task,and compared to some classical metaheuristic approaches.The analysis results demonstrate that the SCEO approach provides the best performance and is a prospective tool for MRPP.

Development of Path Planning Algorithm Using Probabilistic Roadmap Based on Modified Ant Colony Optimization

In this paper, a unique combination among probabilistic roadmap, modified ant colony optimization, and third order B-spline curve has been proposed to solve path planning problems?in complex and very complex environments. This proposed approach can be divided into three stages. First stage involves constructing a random roadmap depending on the environment complexity using probabilistic roadmap algorithm. Roadmap can be constructed by distributing N nodes randomly in complex and very complex static environments then pairing these nodes together according to some criteria or conditions. The constructed roadmap contains a huge number of possible random paths that may lead to connecting?the start and the goal points together. Second stage includes finding path within the pre-constructed roadmap. Modified ant colony optimization has been proposed to find or to search the best path between start and goal points, where in addition to the proposed combination, ACO has been modified to increase its ability to find shorter path. Finally, the third stage uses B-spline curve?to smooth and reduce the total length of the found path in the previous stage. The results of the proposed approach ensure?the?feasible?path between start and goal points in complex and very complex environments. Also, the path is guaranteed to be short, smooth, continuous?and safe.

Bi-objective path optimization of flapping airfoils based on a surrogate model

A bi-objective optimization problem for flapping airfoils is solved to maximize the time-averaged thrust coefficient and the propulsive efficiency. Design variables include the plunging amplitude, the pitching amplitude and the phase shift angle. A well defined Kriging model is used to substitute the time-consuming high fidelity model, and a multi-objective genetic algorithm is employed as the search algorithm. The optimization results show that the propulsive efficiency can be improved by reducing the plunging amplitude and the phase shift angle in a proper way. The results of global sensitivity analysis using the Sobol’s method show that both of the time-averaged thrust coefficient and the propulsive efficiency are most sensitive to the plunging amplitude, and second most sensitive to the pitching amplitude. It is also observed that the phase shift angle has an un-negligible influence on the propulsive efficiency, and has little effect on the time-averaged thrust coefficient.