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In the deployment of Wireless Sensor Networks (WSNs), fault tolerance and energy consumption are the two crucial challenges. For this, clustering is the most effective energy-saving techniques for extending throughput and enhancing fault tolerance. The most energy saving method for extending network lifespan is clustering method. Therefore, Cluster based Hybrid Flamingo Hazelnut Tree with Improved Marine Predators for Energy Efficient Fault Tolerant Routing in WSN (CHFHIM-EEFR) is proposed for Main Cluster Head (MainCH)election and optimum routing in the network. Initially Hybrid Flamingo Hazelnut tree search algorithm is utilized to elect the MainCH on the basis offitness function. Based on the selected MainCH, Back Up Node (BCH) is selected for improving the fault tolerant. A harmful node enters after the BCH-based MainCH selection; therefore, an Improved Marine Predators Algorithm (Im-MPA) is utilized for choosing the most efficient route out of the multiple paths. Finally, the secure data communication is done in the optimum trust path. The developed approach is executed in Network Simulator and validated with the existing protocols. The simulations outcomes show that the CHFHIM-EEFRmethod attains 2.2mslowerdelay, 99.95% higher PDR99% high throughput, 20.3 ms low time complexity and 99.98 Mbps high network life time. [ABSTRACT FROM AUTHOR]

After the occurrence of mine water inrush, choosing the appropriate emergency evacuation path is very important to reduce the casualty and the mine accident level. The traditional Dijkstra algorithm can not be effectively applied to the dynamic environment of mine water inrush. It can only statically plan a single optimal escape path. In view of the limitations of this algorithm, the time equivalent length is introduced to optimize the traditional Dijkstra algorithm to realize the dynamic change of the optimal escape path with time and water level height. Moreover, the optimized Dijkstra algorithm is combined with the K optimal path algorithm to realize the dynamic planning of multiple optimal escape paths. Field application shows that the optimized dynamic D-K algorithm can be effectively applied to mine water inrush with characteristics of randomness, dynamics and complex, which can provide important decision support for affected personnel to choose emergency escape path.

Summary: A vehicular ad hoc network (VANET) is a wireless network composed of a collection of mobility devices that link via a wireless channel without the need for permanent infrastructure or centralized administration. Multicasting is a method of sending data from a single node to multiple recipients at the same time. Because of its energizing network topology and constricted resources, a mobile ad hoc network (MANET) presents a number of issues. Because network execution measurements such as latency, bandwidth, throughput, and energy fluctuate so rapidly owing to node movement, these network properties in a wireless mobile ad hoc network are ambiguous. Because of the influence of these uncertainty issues, determining the top‐grade path from the originating node to a set of recipient nodes is challenging. In order to preserve network resources, we used a Swarm Intelligence‐based fuzzy logic strategy in this study that includes channel caliber factor and connection termination time to try to alleviate these uncertainty issues. This strategy integrates all of the network indicators available for the routes into an individual quantity named as fuzzy cost or cost of communication. The routes with the lowest fuzzy cost will be regarded optimum, and data will be delivered along this route from the originating point to a set of receivers. The simulation was carried out using NS‐3.38 and MATLAB, and the results reveal that the suggested protocol, HACOFLM, outperforms ODMRP, OFAODV, MAODV, and DFMCRP by means of packet delivery ratio, latency, control packet overhead, and throughput. DFMCRP outperforms MAODV by 3% to 6%, OFAODV by 13% to 19%, and ODMRP by 16% to 23%. According to HACOSMO, based on the number of vehicles and velocities, routing overhead is 7% to 10% less than DFMCRP, 9% to 13% less than MAODV, 17% to 29% less than OFAODV, and 22% to 36% less than ODMRP. It improvise throughput by 4% to 6% when contrasted to DFMCRP, 5% to 9% when contrasted to MAODV, 10% to 14% when contrasted to OFAODV, and 14% to 23% when contrasted to ODMRP. [ABSTRACT FROM AUTHOR]

Routes discovery that can provide reliable data transmission in Mobile Ad-hoc Networks is challenging due to its wireless channel characteristics and dynamic transmission environment. Ad-hoc networks frequently experience link failure because nodes are mobile and their positions are not fixed. The cellular ad hoc community's dynamic nature makes it possible to analyze multi-route routing protocols specifically. One of the most trustworthy and environment-friendly routing methods is the Zone Routing Protocol used in Mobile Ad Hoc Networks. Offering timely and trustworthy communication services, however, depends critically on maintaining the Quality of Service, electricity performance, and outstanding resource management. In this research, we suggested an energy-efficient routing method for zone-based MANETs called enthalpy and net routing. First, a completely fuzzy quarter clustering based on an Energy guide is used to perform the clustering. With the help of fuzzy memberships, the AFCM set of rules permits the input of statistics for each elegance. In the second, the best route is selected using Enthalpy Ant Net Routing (EANR) while taking the following factors into account: community disconnection, channel error, buffer overflow, and contention at the link layer. The results of the experiment show that the collection of rules performs better than many existing algorithms in terms of community longevity, electricity consumption, and other metrics. [ABSTRACT FROM AUTHOR]

Safe and efficient navigation is critical for a mobile robot in a highly constrained workspace. Autonomous navigation is to be performed safely and robustly in the environment map with obstacles of various geometric shapes, sizes and colors positioned at random locations. In this paper, we present an approach to perform autonomous navigation by detecting the obstacles in the map and by generating auxiliary collision-free waypoints in obstacle-free space map. In achieving this, the object detection is done using SSDMobileNetV2 model and auxiliary collision-free navigation waypoints are generated using the Deepway neural network model. Further, the RRT path planner is applied to analyze the waypoints generated and to find the a global path between start and goal locations. An optimal local path is achieved using the A* path planner. Extensive simulations of various scenarios are performed and the proposed model is evaluated. The results reveal that the proposed model achieves significant improvements in terms of time, distance and F1-score. [ABSTRACT FROM AUTHOR]

Modern society has accumulated great amounts of knowledge since human start learning and communicating. With knowledge's continuously thriving and getting refined, knowledge itself has developed into a complex system with a huge number of content and structure features. Together with the development of science and technology, the ways of people learning have been changed greatly. Under the pressure of high-paced life and workplace competition, more and more people are in demand of personalized learning paths, in order to achieve their learning target as fast as possible. A personized and scientific learning path is the key to effective learning grogram. However, studies on algorithms of optimal learning path discovery are not comprehensively developed, especially on its performance and application. Therefore, in this article, we propose a new algorithm, Portfolio ST, which is improved from Steiner Tree, for personalized and optimal learning path mining. At first, this paper constructs the knowledge graph of finance, then builds learner models by profiling learners' owned and aimed knowledge based on the knowledge network. At last, according to the knowledge structure of a person's learner model, the most personized and shortest learning path is calculated by the Portfolio ST. After the experiment compared with two baseline algorithms, the Portfolio ST algorithm is proved to have the best performance and scalability, by changing owned/aimed/overall knowledge respectively, which is also the best algorithm of learning path mining that can achieve the global optimal among these three algorithms. [ABSTRACT FROM AUTHOR]

In recent years, the collaborative operation of multiple unmanned aerial vehicles (UAVs) has been an important advancement in drone technology. The research on multi-UAV collaborative flight path planning has garnered widespread attention in the drone field, demonstrating unique advantages in complex task execution, large-scale monitoring, and disaster response. As one of the core technologies of multi-UAV collaborative operations, the research and technological progress in trajectory planning algorithms directly impact the efficiency and safety of UAV collaborative operations. This paper first reviews the application and research progress of path-planning algorithms based on centralized and distributed control, as well as heuristic algorithms in multi-UAV collaborative trajectory planning. It then summarizes the main technical challenges in multi-UAV path planning and proposes countermeasures for multi-UAV collaborative planning in government, business, and academia. Finally, it looks to future research directions, providing ideas for subsequent studies in multi-UAV collaborative trajectory planning technology. [ABSTRACT FROM AUTHOR]

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Although the EVRP (Electric Vehicle routing problem) has promising results on the environmental scale, its implementation has proved challenging. The difficulty of the EVRP resides in the limited driving range of the electric vehicles, combined with the significant charging time. While the charging cost is less than the cost of fuel, this charge time adds to the overall travel time and may overlap with customers' time windows. All these factors increased the computational time exponentially and resulted in the need to overlook some constraints such as traffic congestion, road conditions, weather impact on energy consumption, and driving style, to name a few, in order to speed up execution time. While this method proved effective in accelerating the process of the EVRP, it did, however, render the approach unrealistic, as it steered far from real-life settings and made the approach unpredictable when facing dynamic and changing parameters. In this paper, we try to remedy this issue by proposing an approach in which we try to replicate real-life parameters such as heterogenous fleets, energy consumption, and infrastructure data. The objective of our approach was to minimize the total travel time, travel distance, energy consumed, and the number of vehicles deployed. To solve this problem, we propose a three-stages approach, in which the first stage consists of a newly developed dispatching approach where customers are assigned to vehicles. The second stage uses the genetic algorithm to find a set of optimal paths, and, finally, in the third stage, charging stations are inserted in the selected paths. Upon testing our approach on Solomon's instances, our approach proved effective in finding optimal solutions in a reasonable time for five- to fifteen-customer datasets. However, when trying to solve larger datasets, the approach was slowed down by the extreme number of constraints it had to satisfy. [ABSTRACT FROM AUTHOR]

Drone technology is advancing rapidly and represents significant benefits during firefighting operations. This paper presents a novel approach for autonomous firefighting missions for Unmanned Aerial Vehicles (UAVs). The proposed UAV framework consists of a local planner module that finds an obstacle-free path to guide the vehicle toward a target zone. After detecting the target point, the UAV plans an optimal trajectory to perform a precision ballistic launch of an extinguishing ball, exploiting its kinematics. The generated trajectory minimises the overall traversal time and the final state error while respecting UAV dynamic limits. The performance of the proposed system is evaluated both in simulations and real tests with randomly positioned obstacles and target locations. The proposed framework has been employed in the 2022 UAV Competition of the International Conference on Unmanned Aircraft Systems (ICUAS), where it successfully completed the mission in several runs of increasing difficulty, both in simulation and in real scenarios, achieving third place overall. A video attachment to this paper is available on the website . [ABSTRACT FROM AUTHOR]

This study proposes a path planning algorithm that combines an informed rapidly-exploring random tree (RRT*) and particle swarm optimization (PSO) algorithms. The informed RRT* algorithm can build optimal pathways quickly. However, in some cases, the informed RRT* strategy cannot help increase the convergence rate. Meanwhile, the PSO algorithm can be combined with other algorithms to improve algorithm performance. The proposed algorithm will combine the advantages of informed RRT* and PSO algorithms. The proposed algorithm is called the RRT-PSO algorithm. The RRT-PSO algorithm’s performance was compared to the informed RRT*, RRT*, RRT-ACS, and informed RRT*-connect algorithms using eight benchmark scenarios. The test results on benchmark scenarios with known optimal values demonstrate that the RRT-PSO algorithm achieves those optimal values. The statistical analysis shows that the RRT-PSO algorithm has a fast convergence rate compared to other comparison algorithms. The test results also demonstrate the RRT-PSO algorithm’s stability and robustness compared to other comparison algorithms. Our test results also show the RRT-PSO algorithm’s advantages over other PSO-based and traditional path planning algorithms. The results show that the RRT-PSO algorithm is suitable for applications requiring a fast and optimal path planning algorithm, such as self-driving cars and unmanned aerial vehicles (UAV).

Energy is seen as a significant factor in wireless sensor networks (WSNs). It is a challenge to balance between battery lifetime of the different sensors and network lifetime. The main contribution of the proposed approach is to decrease the energy consumption of each sensor node, overcome unbalanced energy usage among sensor nodes, reduce the data gathering time and enhance the network lifetime. To achieve these goals, we combine the Hierarchical Agglomerative algorithm and an optimal path selection method. First, the suitable cluster heads (CHs) are elected based on the Euclidean distance and the residual energy of each sensor node. Then, the base station is situated at the center of the field, which will be partitioned into equal subareas, one for every mobile data collector (MDC). Second, the Kruskal algorithm is used to create an optimal data gathering path from each subset of elected cluster heads. Finally, each mobile data collector travels the optimal path to collect the data from the set of cluster heads of each subarea and returns periodically to the base station to upload gathered data. Computer simulation proves that the proposed approach outperforms existing ones in terms of data gathering time, residual energy and network lifetime

Latency and energy are critical issues when working with time and power-constrained wireless sensor networks. To avoid wasting both time and energy, such systems require selecting optimal communication routes with minimum latency and energy. The energy and latency costs between sender and destination nodes are greatly affected by the occurrence of transmission holes (black hole and grey hole). Therefore, selecting the optimal path must consider the probability of transmission holes and investigate their impact on energy and latency costs. Based on these problems of silent failures, the current work, proposes i-OPC as an intelligent and effective system to address these problems by forecasting sources of such silent failures and resolving them before they occur. The proposed method uses a customized routing schedule and a multi-objective mathematical optimization approach to rank all candidate paths between the source and destination nodes. In addition, i-OPC implements a machine learning technique using deep learning to predict the energy and latency costs of the future location for mobile nodes (). Also, it determines if the future locations of mobile nodes can result in black holes. Experiments produces promising results in terms of delay, energy consumption, packet delivery rate and hole detection rate against existing methods.

Determining the shortest path on a network is widely used for improving network performance. In fact, not all network can be generated with a single parameter and are simple graphs. Many networks contain multiple parameters and are not simple graphs (multigraphs). The shortest path in a multigraph is determined by removing the most weight before calculating with the algorithm. Eliminations on different parameters leads to in various shortest paths, hence an optimal path cannot be found in the multigraph. Multigraphs are built in this research using interval-valued intuitionistic fuzzy numbers, which are a combination of multiple parameters. The Floyd-warshall algorithm was modified to create an algorithm that may be applied on interval-valued intuitionistic fuzzy multigraphs. The optimum path for a transportation network made of various parameters in the form of a multigraph was determined as an experiment using an interval-valued intuitionistic fuzzy number approach. The improved Floyd-warshall algorithm generates an optimal path on the transportation network under consideration. [ABSTRACT FROM AUTHOR]

A novel technology named Internet of Things aims to connect all common objects. How to secure D2D communication is one of the main problems preventing its development. Particularly, Key Management (KM) is among the most challenging security issues. The difficulty is caused by the lack of resources in the vast majority of IoT devices. Although several methods were created, most of them do not account for the dynamic nature of IoT. As the network evolves, other systems depend on a centralized entity to distribute new keys. Here, we have developed a secured model that has four working phases, which were described below. (1) Network Generation: In the first phase, the network is generated. (2) D2D Communication: In this second phase, for Device to Device (D2D) communication, it is important to select which devices to be connected. Thereby, there is a need for an essential path among them. This will be done optimally (optimal path identification), by the Chaotic Map Upgraded COOT optimization (CMU-CO). Here, the optimal path is chosen based on distance, mobility and delay. Encryption between nodes (Devices): Improved Elliptic Curve Cryptography (ECC) is used in this phase for secure communication, thereby, the data transmission become more secure. In this approach, the key generation takes place optimally by the new CMUCO. Decryption: In this final phase, the original data are retrieved by decrypting it. [ABSTRACT FROM AUTHOR]

As network services tend to be integrated to provide a better quality of service (QoS) to customers, correlations appear to be useful measurements to better design service compositions of an integrated network. However, as the integration intensifies, the determination of the service compositions of the network requires the ease of computational issues in this dynamic environment, which are resolved by the cloud computing platforms. The manuscript proposes an algorithm based on multi-constraint optimal path selection (MCOPS) that benefits from available notions such as QoS correlation criteria and correlation ratios, and skyline algorithm to construct a novel directed cyclic graph whose calculations are conducted on the cloud platform dynamically. Both cost and delay attributes in the construction of network service compositions for customers are included in the graph. Simulations suggest that both average calculation time and the quality of the path solution are substantially enhanced with the utilization of cloud computing in network service compositions. Consequently, a better service composition plan (SCP) is attained when a correlated structure is assumed to exist. [ABSTRACT FROM AUTHOR]

Latency and energy are critical issues when working with time and power-constrained wireless sensor networks. To avoid wasting both time and energy, such systems require selecting optimal communication routes with minimum latency and energy. The energy and latency costs between sender and destination nodes are greatly affected by the occurrence of transmission holes (black hole and grey hole). Therefore, selecting the optimal path must consider the probability of transmission holes and investigate their impact on energy and latency costs. Based on these problems of silent failures, the current work, proposes i-OPC as an intelligent and effective system to address these problems by forecasting sources of such silent failures and resolving them before they occur. The proposed method uses a customized routing schedule and a multi-objective mathematical optimization approach to rank all candidate paths between the source and destination nodes. In addition, i-OPC implements a machine learning technique using deep learning to predict the energy and latency costs of the future location for mobile nodes (). Also, it determines if the future locations of mobile nodes can result in black holes. Experiments produces promising results in terms of delay, energy consumption, packet delivery rate and hole detection rate against existing methods. [ABSTRACT FROM AUTHOR]

Energy is seen as a significant factor in wireless sensor networks (WSNs). It is a challenge to balance between battery lifetime of the different sensors and network lifetime. The main contribution of the proposed approach is to decrease the energy consumption of each sensor node, overcome unbalanced energy usage among sensor nodes, reduce the data gathering time and enhance the network lifetime. To achieve these goals, we combine the Hierarchical Agglomerative algorithm and an optimal path selection method. First, the suitable cluster heads (CHs) are elected based on the Euclidean distance and the residual energy of each sensor node. Then, the base station is situated at the center of the field, which will be partitioned into equal subareas, one for every mobile data collector (MDC). Second, the Kruskal algorithm is used to create an optimal data gathering path from each subset of elected cluster heads. Finally, each mobile data collector travels the optimal path to collect the data from the set of cluster heads of each subarea and returns periodically to the base station to upload gathered data. Computer simulation proves that the proposed approach outperforms existing ones in terms of data gathering time, residual energy and network lifetime. [ABSTRACT FROM AUTHOR]

Several branches of study and research arise from uncrewed aerial vehicle (UAV) technology. A relevant in-flight task focuses on path planning in 3D, which impliesa high computational cost and, consequently, must be achieved by improving the response time. This work aims to optimize the computation time and determine a complete 3D path. In this sense, a 3D flight environment as a 3D adaptive discrete mesh is considered subjected to minimal refinement in search of collision-free spaces. With the construction of the discrete mesh, a cost response methodology is applied in the manner of the discrete deterministic finite automaton (DDFA), which results in a set of optimal partial responses (recursively computed) that indicate the collision-free spaces in the final 3D path for the UAV flight. As a result, the path-planning 3D algorithm saves computational time and memory resources compared to classical techniques.

The prevention and control measures adopted by various countries have different effects in the face of COVID-19. The performance in some socialist countries and capitalist countries were compared from three aspects, including the proportion of confirmed cases and deaths to the national population, economic recovery and production and living, social satisfaction and international evaluation. The overall performance of listed socialist countries was better than that of capitalist countries. China's performance, forming a unique Chinese model of the epidemic prevention and control, was better than that of other socialist countries. The successful experience of Chinese model can be mainly reflected in four aspects, including resource allocation, social participation, speed and quality, system and mechanism optimization. The successful experience roots in the four institutional advantages of the leadership of the Communist Party of China (CPC), coordinated national response, basic economic system and traditional Chinese culture. In the future, Chinese government should adhere to the socialist system with Chinese characteristics and improve the public health and emergency management system. Chinese government should accelerate the construction of a community with a shared future for mankind and promote the modernization of the national governance system and governance capacity. These findings are of great significance in understanding China's prevention and control measures, the institutional advantages and subsequent optimization.

It is of great significance to build a modern comprehensive transportation system and coordinate the development of various modes of transportation for economic development. This paper introduces the game theory model into the logistics international trade transportation strategy problem, establishes the Stackelberg game model, takes the transportation cost, transportation time, external cost, transportation security as the evaluation index, uses the DHGF comprehensive algorithm to decouple, and constructs the logistics international trade transportation model based on the game theory. The selection of container transportation from place A to five other locations is an example of a quantitative analysis of different transportation options and strategies in logistics international trade transportation under various demands. The optimal path is based on the demand of managers to control the external cost of 27,500 to 31,900 yuan, the demand of consumers of high-value goods to control the cargo damage rate of 1.43% to 1.59%, the optimal path of the demand of operators for the more economical railroad transportation. The final utility evaluation of the optimal path of this paper’s model is 0.18%~3.99% higher than the comparison model. The results show that the model in this paper can effectively provide a higher utility optimization scheme and route reference for international trade transportation path optimization under different demands of participants.

To address an autonomous guided vehicle problem, this article presents extended variants of the established block over-relaxation method known as the Block Modified Two-Parameter Over-relaxation (B-MTOR) method. The main challenge in handling autonomous-driven vehicles is to offer an efficient and reliable path-planning algorithm equipped with collision-free feature. This work intends to solve the path navigation with obstacle avoidance problem explicitly by using a numerical approach, where the mobile robot must project a route to outperform the efficiency of its travel from any initial position to the target location in the designated area. The solution builds on the potential field technique that uses Laplace's equation to restrict the formation of potential functions across operating mobile robot regions. The existing block over-relaxation method and its variants evaluate the computation by obtaining four Laplacian potentials per computation in groups. These groups can also be viewed as groups of two points and single points if they're close to the boundary. The proposed B-MTOR technique employs red-black ordering with four different weighted parameters. By carefully choosing the optimal parameter values, the suggested B-MTOR improved the computational execution of the algorithm. In red-black ordering, the computational molecules of red and black nodes are symmetrical. When the computation of red nodes is performed, the updated values of their four neighbouring black nodes are applied, and conversely. The performance of the newly proposed B-MTOR method is compared against the existing methods in terms of computational complexity and execution time. The simulation findings reveal that the red-black variants are superior to their corresponding regular variants, with the B-MTOR approach giving the best performance. The experiment also shows that, by applying a finite difference method, the mobile robot is capable of producing a collision-free path from any start to a given target point. In addition, the findings also verified that numerical techniques could provide an accelerated solution and have generated a smoother path than earlier work on the same issue. [ABSTRACT FROM AUTHOR]

Aiming at the problem of overestimation, low sample utilization and sparse reward of DQN algorithm in mobile robot path planning, an end- to- end path planning method based on improved deep reinforcement learning is proposed, namely the curiosity distillation module dueling deep double Q-network prioritized experience replay (CDM-D3QN-PER). This method is based on D3QN to reduce the adverse effects of overestimation. Long short term memory (LSTM) is added to the input to process the information of radar and camera to obtain more favorable environmental information. It uses prioritized experience replay (PER) as sampling method to make full use of samples and improve sample utilization, and the curiosity distillation module (CDM) is introduced to alleviate the problem of reward sparsity to some extent. The experimental results show that compared with DQN, DDQN and D3QN, the number of robots reaching the target point trained by CDM-D3QN-PER algorithm is significantly increased, and it is three times that of DQN algorithm. The algorithm makes reward worthy of promotion, network convergence speed is improved, in unknown complex environment can better obtain the optimal path. [ABSTRACT FROM AUTHOR]

In recent years, WSNs are acquiring popularity due to small-sized and flexible implementation; many applications require quick data transfer with minimal energy consumption of nodes in the midst of the ubiquitous use of WSNs. These sensor nodes cover large regions according to application needs and choose the best optimal path. The main issue with WSN is how to cover the neighborhood correctly and send data to sink without falling into the trap of a single node and single route. Therefore, a recently researched approach namely the swarm-based dragonfly, which has been effectively used in miscellany applications is exploited for this work. The dragonfly method is based on the exploration phase using global search and exploitation phase using local search. The implicit swarming behaviors are thought to be the fundamental drive for routing algorithms. This paper introduce a Meta-heuristic based Optimized Opportunistic Routing Protocol for WSNs (MOORP) based upon the best optimal forwarder node selection and dragonfly route optimization. The forwarder node selection is optimized by residual energy and eucledian distance of the node. The path between forwarder and destination is identified by using the Dragon-fly algorithm. MOORP employs a route searching algorithm (RSA) and a Energy Level Matrix (ELM) update is used to enhancing the routing decision. The RSA finds an optimal path and selects the optimal forwarder node with the help of a heuristic update or ELM. MOORP performance is compared with other opportunistic routing protocols on important parameters such as the number of alive nodes, throughput, packet delivery ratio, message success rate, and average energy consumption,and also compare with pre-existing cluster based routing protocol. The simulation results show that the MOORP considerably outperforms its competitive techniques in terms of energy efficiency. [ABSTRACT FROM AUTHOR]

Although the EVRP (Electric Vehicle routing problem) has promising results on the environmental scale, its implementation has proved challenging. The difficulty of the EVRP resides in the limited driving range of the electric vehicles, combined with the significant charging time. While the charging cost is less than the cost of fuel, this charge time adds to the overall travel time and may overlap with customers’ time windows. All these factors increased the computational time exponentially and resulted in the need to overlook some constraints such as traffic congestion, road conditions, weather impact on energy consumption, and driving style, to name a few, in order to speed up execution time. While this method proved effective in accelerating the process of the EVRP, it did, however, render the approach unrealistic, as it steered far from real-life settings and made the approach unpredictable when facing dynamic and changing parameters. In this paper, we try to remedy this issue by proposing an approach in which we try to replicate real-life parameters such as heterogenous fleets, energy consumption, and infrastructure data. The objective of our approach was to minimize the total travel time, travel distance, energy consumed, and the number of vehicles deployed. To solve this problem, we propose a three-stages approach, in which the first stage consists of a newly developed dispatching approach where customers are assigned to vehicles. The second stage uses the genetic algorithm to find a set of optimal paths, and, finally, in the third stage, charging stations are inserted in the selected paths. Upon testing our approach on Solomon’s instances, our approach proved effective in finding optimal solutions in a reasonable time for five- to fifteen-customer datasets. However, when trying to solve larger datasets, the approach was slowed down by the extreme number of constraints it had to satisfy.

The on-time charging of Renewable Sensor Nodes (RSNs) prolongs the lifetime of the Wireless Rechargeable Sensor Network (WRSN). However, it necessitates the prompt and accurate transmission of Charging Requests (CR) from RSNs to Mobile Charging Drones (MCD), and accordingly the charging trip is planned by MCD. Most of the existing algorithms attempted to forecast the shortest path, which may not be an optimal path for the current network traffic. This causes increased packet loss, retransmissions thus consuming more energy. To address this, Firefly-based Optimal Path Selection Algorithm (FOPSA) is proposed to find an optimum and stable path for the on-time delivery of CRs to MCD using firefly optimization. The proposed fitness function utilizes geometric mean of the residual energy, distance, and hop count to predict the routing path for the transmission of CRs. It ensures that the path has potential RSNs with adequate energy to avoid routing holes. The proposed algorithm is simulated and compared with the existing algorithms in terms of throughput, packet loss, residual energy, and delay. [ABSTRACT FROM AUTHOR]

Path planning is a critical technology that could help mobile robots accomplish their tasks quickly. However, some path planning algorithms tend to fall into local optimum in complex environments. A path planning method using a modified Harris hawks optimization (MHHO) algorithm is proposed to address the problem and improve the path quality. The proposed method improves the performance of the algorithm through multiple strategies. A linear path strategy is employed in path planning, which could straighten the corner segments of the path, making the obtained path smooth and the path distance short. Then, to avoid getting into the local optimum, a local search update strategy is applied to theHHOalgorithm. In addition, a nonlinear control strategy is also used to improve the convergence accuracy and convergence speed. The performance of the MHHO method was evaluated through multiple experiments in different environments. Experimental results show that the proposed algorithm is more efficient in path length and speed of convergence than the ant colony optimization (ACO) algorithm, improved sparrow search algorithm (ISSA), and HHO algorithms. [ABSTRACT FROM AUTHOR]

Exploring the path of energy structure optimization to reduce carbon emissions and achieve a carbon peak has important policy implications for achieving the "Dual Carbon" target. To this end, this paper explores the optimal path for China to achieve the "dual carbon" target from the perspective of energy structure optimization in three steps: (1) we forecast China's carbon emissions and carbon intensity during 2024–2035 based on a combined forecasting model; (2) we simulate the development of energy consumption and carbon emissions under the "economic development scenario-energy structure scenario" with the help of Markov chain forecasting model; (3) we construct a multi-attribute decision model to account for the above elements as variables to calculate a composite index to analyze the optimal path for China to achieve "Dual Carbon" target under different decision preferences. It is found that (1) potential negative effects caused by COVID-19 are not as serious as reported; (2) only the scenario with low-speed economic growth and effective policies guiding, which doesn't follow laws of social development, can contribute to reaching carbon peaking by 2030 while maintaining a high-quality carbon intensity; (3) the optimal path that scenario with middle-speed economic growth and strict cost control is a sub-optimal choice subject to realities; (4) technologies innovations in green or low-carbon fields are needed to accelerate energy consumption structure optimization. [ABSTRACT FROM AUTHOR]

In view of the problems of traditional ant colony (ACO) algorithm, such as slow convergence speed, many iterations and easy to fall into local optimal, a dynamically ant colony optimization(DACO) algorithm was proposed. With the optimal path as reference, experiments were carried out on the values of pheromone heuristic factor and expectation heuristic factor of the traditional ant colony algorithm. When the value range of α is [1,3] and the value range of β is [7,9], the shortest path can be obtained. In view of the above range of values, the experiments on how to value the two parameters show that when α follows the normal function distribution curve and β follows the symmetric curve of α with respect to y=10, the convergence speed of the algorithm is accelerated and the number of iterations is reduced, so as to avoid the ant falling into the local optimum and thus failing to find the optimal solution. ROS robot was used to verify the experimental platform, and the results show that the optimization time of the improved algorithm is 6.05% shorter than that of the traditional ant colony algorithm.

It is possible to represent the road map on the paper and study it using Dijkstra`s algorithm to find the shortest path on the real earth. Dijkstra`s Algorithms are used for calculating the shortest path from source to sink to enable query operations that follow. Dynamic shortest-route techniques are needed to accommodate the modifications within the underlying community topology. Every solution includes figuring out the nodes whose shortest routes could be impacted through the updates and producing a list of affected vertices and their updated shortest pathways. In this work, the advent of the retroactive priority queue information structure makes the Dijkstra algorithm dynamic. In this research, the stepping is changed forward in the shortest direction for 2 networks and two directions. That changed by locating the shortest static path, then circulating to the first node after the beginning node. At this node, the weights inside the segments directed from this node will exchange and cancel the vintage shortest direction and locate the new direction. Then flow to the next node in the new find shortest path, and repeat the operation until the end. The idea is that the best path can be constantly changed based on the latest data. These continuous changes are addressed in this paper, where the proposed system can find the best methods and update them automatically according to the variables.

In the field of aerial photography, complete coverage path planning (CCPP) is the key to improving the task efficiency of unmanned aerial vehicles (UAVs) with a limited battery capacity. Previous CCPP algorithms consisted of Cellular Decomposition (CD) class, Probabilistic Roadmap (PRM) class, and Bio-inspired Neural Network (BINN) class. However, when faced with the complex environment of multiple forbidden areas, CD and PRM have high computational complexity and a long path length, and BINN has a large number of turns. In this paper, an improved back-and-forth (IBF) algorithm is proposed to achieve good performance in these three aspects at the same time based on a newly straight-turn mode and the corresponding auxiliary approaches (i.e. Local Traversal, Global Traversal, and continuity constraint function). An important difference between straight-turn mode and back-and-forth mode is that the next straight direction is not restricted to being parallel to the last straight direction but is determined by Local Traversal, which is introduced to evaluate the benefits of different paths and find the best direction for the next straight-turn mode at the ending cell. Global Traversal is available to choose a “jump” cell when “Dead zone” are faced. The continuity constraint function is used to maintain the continuity of the unexplored area. Under the comprehensive consideration of path length, computation time, and number of turns, the simulation and experimental results indicate that the proposed IBF algorithm has better performance in multiple forbidden areas.

Fusion of internet of things (IoT) and artificial intelligence for smart cities has allowed structured policy making.In particular, traffic jam is one of the serious concern in metropolitan cities. In advanced countries, specific devices are installed to assemble flow of traffic. The state of traffic is streamed to commuters through network. This dependency on internet connection results in inappropriate solution in absence of stable physical infrastructure. The developing countries lack availability of internet on roadway and urban zones face even worst condition. This study proposes a smart framework to enable optimal path selection by communicating with neighbouring devices. The android smart phones serve the purpose of embedded sensors. A temporary network is created for smart transportation system. Data processing and cleaning were conducted on different routes of two urban cities. The proposed system is evaluated on conventional and proposed optimal path selection techniques. Computed results depict that travel times have been decreased to around 23.3% in comparison to conventional methods using MDSP algorithm.

In this paper, we discuss the concept of using multiple parameters to find the optimal path in the multigraph. Multiple parameters indicate the number of parameters used in compiling a network. To be compiled into an edge weight of a multigraph involves converting the parameters into the same quantity without losing meaning. Here, the interval-valued intuitionistic fuzzy approach is used to perform the conversion. We will obtain the optimal multigraph path using a multiple-parameter transportation problem. [ABSTRACT FROM AUTHOR]

Motivated by an optimal visiting problem, we study a switching mean-field game on a network, where both a decisional and a switching time-variable are at disposal of the agents for what concerns, respectively, the instant to decide and the instant to perform the switch. Every switch between the nodes of the network represents a switch from $ 0 $ to $ 1 $ of one component of the string $ p = (p_1, \ldots, p_n) $ which, in the optimal visiting interpretation, gives information on the visited targets, being the targets labeled by $ i = 1, \ldots, n $. The goal is to reach the final string $ (1, \ldots, 1) $ in the final time $ T $, minimizing a switching cost also depending on the congestion on the nodes. We prove the existence of a suitable definition of an approximated $ \varepsilon $-mean-field equilibrium and then address the passage to the limit when $ \varepsilon $ goes to $ 0 $. [ABSTRACT FROM AUTHOR]

Future engineering systems with new capabilities that far exceed today's levels of autonomy, functionality, usability, dependability, and cyber security are predicted to be designed and developed using cyber-physical systems (CPSs). In this paper, the security of CPSs is investigated through a case study of a smart grid by using a reinforcement learning (RL) augmented attack graph to effectively highlight the subsystems' weaknesses. In particular, the state action reward state action (SARSA) RL technique is used, in which the agent is taken to be the attacker, and an attack graph created for the system is built to resemble the environment. SARSA uses rewards and penalties to identify the worst-case attack scenario; with the most cumulative reward, an attacker may carry out the most harm to the system with the fewest available actions. Results showed successfully the worst-case attack scenario with a total reward of 26.9 and identified the most severely damaged subsystems. [ABSTRACT FROM AUTHOR]

We consider the problem of optimal harvesting of a renewable resource whose dynamics are governed by logistic growth and whose payoff is proportional to the harvest. We consider both the case of a finite and an infinite time horizon and analyse the structure of the optimal solutions and their dependence on the parameters of the model. We show that the optimal policy can only have one of three structures: (1) maximal harvesting effort until the resource is depleted, (2) zero harvesting during an initial time interval followed by a subsequent switch to maximal harvesting effort, or (3) a singular solution, which corresponds to an intermediate level of harvesting, accompanied by the most rapid approach path. All three scenarios emerge, with minor variations, with finite and infinite time horizons, depending on the particular combination of parameters of the system. We characterize the conditions under which the singular solution is optimal and present suggestions for designing an optimal and sustainable harvesting strategy. Recommendations for Resource Managers: We have rigorously explored a standard optimal harvesting model and its steady states.We show that three different types of solutions may emerge: (i) maximal harvesting eventually leading to a complete depletion of the stock; (ii) maximal harvesting with a potential period of idleness leading to a positive stock; (iii) an initial phase of either no or full harvesting followed by a period of intermediate harvesting intensity leading to a positive stock (singular solution).With some modifications, similar results hold for a finite planning horizon.Which of these three scenarios emerges in the finite horizon case depends not only on the parameter values but also on the length of the planning horizon. [ABSTRACT FROM AUTHOR]

In view of the problems of traditional ant colony （ACO） algorithm, such as slow convergence speed, many iterations and easy to fall into local optimal, a dynamically ant colony optimization（ DACO） algorithm was proposed. With the optimal path as reference, experiments were carried out on the values of pheromone heuristic factor and expectation heuristic factor of the traditional ant colony algorithm. When the value range of α is [1,3] and the value range of β is [7,9],the shortest path can be obtained. In view of the above range of values, the experiments on how to value the two parameters show that when α follows the normal function distribution curve and β follows the symmetric curve of α with respect to y=10,the convergence speed of the algorithm is accelerated and the number of iterations is reduced, so as to avoid the ant falling into the local optimum and thus failing to find the optimal solution. ROS robot was used to verify the experimental platform, and the results show that the optimization time of the improved algorithm is 6.05% shorter than that of the traditional ant colony algorithm. [ABSTRACT FROM AUTHOR]

The optimal path search and planning in the road network has received widespread attention as an important part of location-based services (LBS). Positioning technologies such as radio frequency identification (RFID) have brought a lot of traffic data, which has become the foundation and challenge of research. Travel scenarios in cities are very sensitive to the dynamic changes of the road network. At the same time, the traffic situation in the city is complex and changeable, and the real road network and the trajectories of moving objects contain rich temporal and spatial semantic information. These are difficulties faced by the optimal route search in the road network. In response to these challenges, after analyzing the deficiencies of existing algorithms, a machine learning model GCNSearch based on graph convolutional networks for deep search is proposed, with reference to the heuristic idea of A* algorithm. Firstly, the model uses the spatio-temporal graph convolutional network to aggregate the spatio-temporal information of adjacent areas and past periods to model the recent dynamic changes of the road network on which urban travel depends. Secondly, the model expands the search depth, defines the depth evaluation value of the node, and uses a neural network to replace the artificially designed evaluation function to search for nodes that are conducive to the overall optimal path until the final path is generated. Comparative experiments on the RFID dataset provided by a traffic management bureau show that the GCN-Search algorithm can effectively use the temporal and spatial semantic information of RFID data, and can improve the accuracy of the optimal route search for short-term travel on the dynamic road network. [ABSTRACT FROM AUTHOR]

Tractor-trailer wheeled mobile robots (TTWRs) are known as a subcategory of wheeled mobile robots which have numerous applications in autonomous public transportation and conveying heavier payloads. So, they have attracted a great deal of interests in robotic fields. The presented article investigates a new nonlinear mathematical modeling the TTWRs considering wheel inertia and their optimal point-to-point path planning. To do this, the nonlinear dynamic equations of the articulated robot are derived by defining a new set of generalized coordinates and considering the nonholonomic kinematic constraints of the wheels. For accurate mathematical modeling, the inertia of the tractor-trailer wheels are taken into account and governing dynamic equations are obtained using the Lagrange multiplier principle. Then optimal point-to-point path planning of the TTWR is formulated as an optimal control problem and solved by means of the Pontryagin's minimum principle. The proposed solution method does not require any simplifications such as linearizing of the dynamics equations and discretization the robot's workspace. Moreover, the importance of wheels inertia is illustrated in simulation results which demonstrates the applicability of the proposed formulation and can be a benchmark for future works. [ABSTRACT FROM AUTHOR]

The most important field of robotics study is path planning. Path planning problem in general is an NP-complete problem. Though several attempts have been made using A*, PRM, RRT, and RRT* these algorithms explore too many nodes in the state space, not completely captured kinematic constraints, and are not optimal in real-time. In this paper, a Multi-Objective Hybrid Collision-free Optimal Path Finder (MOHC-OPF) is proposed which is an attempt to obtain a near-optimal solution by exploring fewer nodes compare to the above existing methods while considering kinematic constraints aiming to generate optimal drivable paths. The empirical study revealed that the proposed algorithm is capable of detecting static obstacles and finding a collision-free nearest-optimal, smooth and safe path to the destination in a static known environment. Multiple criteria, including path length, collision-free, execution time, and smooth path, are used to determine an optimal path.. The proposed algorithm shows efficiency in finding the shortest path length and execution time decreased in 90% of the experiments. [ABSTRACT FROM AUTHOR]

Harmonic potential fields are commonly used as guidance in a global approach for self-directed robot pathfinding. These harmonic potentials are generated using Laplace's equation solutions. The computation of these harmonic potentials often requires the use of immense amounts of computing resources. This study introduces a numerical technique called Rotated Block Accelerated Over-Relaxation (AOR), also known as Explicit Decoupled Group AOR (EDGAOR), to deal with pathfinding problem. Several robot navigation simulations were performed in a static, structured, known indoor environment to validate the efficiency of the suggested approach. The paths generated by the simulations are shown using several different starting and target positions. The performance of the proposed approach in computing harmonic potentials for solving pathfinding problems is also discussed.

To address an autonomous guided vehicle problem, this article presents extended variants of the established block over-relaxation method known as the Block Modified Two-Parameter Over-relaxation (B-MTOR) method. The main challenge in handling autonomous-driven vehicles is to offer an efficient and reliable path-planning algorithm equipped with collision-free feature. This work intends to solve the path navigation with obstacle avoidance problem explicitly by using a numerical approach, where the mobile robot must project a route to outperform the efficiency of its travel from any initial position to the target location in the designated area. The solution builds on the potential field technique that uses Laplace’s equation to restrict the formation of potential functions across operating mobile robot regions. The existing block over-relaxation method and its variants evaluate the computation by obtaining four Laplacian potentials per computation in groups. These groups can also be viewed as groups of two points and single points if they’re close to the boundary. The proposed B-MTOR technique employs red-black ordering with four different weighted parameters. By carefully choosing the optimal parameter values, the suggested B-MTOR improved the computational execution of the algorithm. In red-black ordering, the computational molecules of red and black nodes are symmetrical. When the computation of red nodes is performed, the updated values of their four neighbouring black nodes are applied, and conversely. The performance of the newly proposed B-MTOR method is compared against the existing methods in terms of computational complexity and execution time. The simulation findings reveal that the red-black variants are superior to their corresponding regular variants, with the B-MTOR approach giving the best performance. The experiment also shows that, by applying a finite difference method, the mobile robot is capable of producing a collision-free path from any start to a given target point. In addition, the findings also verified that numerical techniques could provide an accelerated solution and have generated a smoother path than earlier work on the same issue.

To optimize the QoS of optical burst switching networks, a QoS routing optimization algorithm based on a multi-objective genetic algorithm is proposed. A Bayesian network model is used to locate the fault of optical burst switching network and obtain the fault location of the transmission link of optical burst switching network; In this position, the routing optimization algorithm based on a multi-objective genetic algorithm transforms the multi constrained network quality of service routing optimization problem into a constrained multi-objective routing optimization problem. Under multiple constraints, the best path of optical burst switching network service is obtained to realize the optical burst switching network quality of service routing optimization. The results show that after applying the proposed algorithm, the average delay of video, text and picture transmission in an optical burst switching network is less than 400ms. The proposed algorithm can improve the packet delivery rate of information transmission in an optical burst switching network, reduce the transmission delay, blocking probability and use cost of an optical burst switching network, and optimize the service quality of an optical burst switching network.

Some recent researches have shown that the energy consumption problem caused by data collection in a wireless sensor network (WSN) based on a static data collector is a main threat to the network lifetime. However, with the progress of the mobile terminal technology, the implementation of mobile data collectors (MDCs) has become more popular in large-scale WSNs, but it remains a big problem to improve the Quality of Service (QoS) criteria and minimize the energy consumption at the same time. However, most existing systems based on MDCs do not successfully strike a balance between routing energy consumption and QoS. In addition, most WSN protocols fail to maintain their impact when the network topology changes. Thus, for a dynamic WSN, it is important to support an intelligent MDC to continue data propagation despite the inevitable changes in the WSN topology. Considering all the above challenges, we propose a new intelligent MDC based on the traveling salesman problem (TSP) to determine the optimal path traveled by the MDC for energy efficiency and latency. Specifically, our proposed Mobile Data Collectors-Traveling Salesman Problem-Low Energy Adaptive Clustering Hierarchy-K-Means (MDC-TSP-LEACH-K) protocol uses K-Means and Grid clustering algorithm to decrease energy consumption in the cluster head (CH) election phase. Additionally, MDC is utilized as an intermediate between CH and the sink to further enhance the QoS of WSNs, to reduce delays while collecting data, and improve the transmission phase of the LEACH protocol.

From the perspective of physics, this article analyzes the various factors that affect the distance of shot put in the process of actual shot put. Based on the actual situation, it uses fractional calculus and physical formulas to calculate the shot throw distance and to conduct a comprehensive analysis. The experiment confirms that the three factors that affect the shot flying distance are the shot angle, shot speed, and shot height and analyzes the relationship between these three factors and the impact of each univariate on the shot flying distance. The analysis results show that among the three factors, the shot speed has the greatest impact on the flight distance, followed by the shot angle, and the shot height has the least effect; moreover, the optimal shot angle is not a specific value but a general range.

To study the mathematical simulation analysis of shot-putter throwing optimal path.

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Determining the optimal path for the mobile robot for a given environment to reach the destination from the starting location is implemented in numerous ways. To obtain the optimal solution Genetic Algorithm (GA) is predominantly used in this research domain. While applying GA, initial solution space is created by randomly generated paths. In this study, the solution space ascertained is environment specific which is directly influencing the computational cost. Three strategies are characterized to construct the initial path depending on the three classified environments. The experiments are conducted with different maps and the outcomes are analyzed quantitatively as well as qualitatively to defend the proposed methodology. [ABSTRACT FROM AUTHOR]