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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.