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Shortest path problem has always been a hot topic in the study of graph theory, because of its wide application field, extending from operational research to the disciplines of geography, automatic control, computer science and traffic. According to its concrete application, scholars in the relevant field have presented many algorithms, but most of them are solely improvements based on Dijkstra algorithm. The purpose of this paper is to enrich the kinds of (and improve the efficiency of) the shortest path algorithms.

This paper puts forward an improved calculation method of shortest path using cellular automata model, which is designed to search the shortest path from one node to another node. Cellular state set is adjusted with combination of breeding and mature states. Evolution rule is improved to enhance its parallelism. At the same time, recording manner of cellular state turnover is modified to record all information sources.

The result indicates that the improved algorithm is correct and more efficient, in that it could reduce the times of cellular state turnover; meanwhile, it can solve multi‐paths problem.

In this paper, cellular state set in exiting shortest path algorithm based on cellular automata theory is adjusted; evolution rule is improved; and recording manner of cellular state turnover is modified to record all information sources. All of which make the parallelism of this algorithm enhanced and the multi‐paths problem solved.

This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning.

A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment.

The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based on the search of shortcut Dijkstra algorithm. Both USV achieves obstacle avoidances in the local region based on the modified APF algorithm after obstacle detection. Both the simulation and experimental results demonstrate that the combinatorial path planning method is more efficient in the complex environment.

This paper proposes a new path planning method for USV in changeable environment. The proposed method is capable of efficient navigation in changeable and unchangeable environment.

A general model descriptive of many algorithms on finite domains is developed. Properties of the model are investigated. Systematic principles are presented for constructing an algorithm specific to a problem. The theory is demonstrated by examples from sorting and searching. Indeed, efficient algorithms can be easily and automatically generated for virtually any sorting or searching problem. However, the theory is general, and applications go far beyond the example areas given.

Isometric feature mapping (Isomap) is a very popular manifold learning method and is widely used in dimensionality reduction and data visualization. The most time-consuming step in Isomap is to compute the shortest paths between all pairs of data points based on a neighbourhood graph. The classical Isomap (C-Isomap) is very slow, due to the use of Floyd’s algorithm to compute the shortest paths. The purpose of this paper is to speed up Isomap.

Through theoretical analysis, it is found that the neighbourhood graph in Isomap is sparse. In this case, the Dijkstra’s algorithm with Fibonacci heap (Fib-Dij) is faster than Floyd’s algorithm. In this paper, an improved Isomap method based on Fib-Dij is proposed. By using Fib-Dij to replace Floyd’s algorithm, an improved Isomap method is presented in this paper.

Using the S-curve, the Swiss-roll, the Frey face database, the mixed national institute of standards and technology database of handwritten digits and a face image database, the performance of the proposed method is compared with C-Isomap, showing the consistency with C-Isomap and marked improvements in terms of the high speed. Simulations also demonstrate that Fib-Dij reduces the computation time of the shortest paths from O(N³) to O(N²lgN).

Due to the limitations of the computer, the sizes of the data sets in this paper are all smaller than 3,000. Therefore, researchers are encouraged to test the proposed algorithm on larger data sets.

The new method based on Fib-Dij can greatly improve the speed of Isomap.

This research has developed a one-stop service supply chain mobile application for the purpose of marketing, product distribution and location-based logistics for elderly farmers and consumers in accordance with the Thailand 4.0 economic model. This is an investigation into the agricultural product distribution supply chain which focuses on marketing, distribution and logistics using the Dijkstra’s and Ant Colony Algorithms to respectively explore the major and minor product transport routes. The accuracy rate was determined to be 97%. The application is congruent with the product distribution, supply chain, in a value-based economy. The effectiveness of the mobile application was indicated to be at the highest level of results of learning outcomes, user comprehension and user experience of users. That is, the developed mobile application could be effectively used as a tool to support elderly farmers to distribute their agricultural products in the one-stop service supply chain which emphasizes marketing, distribution and location-based logistics for elderly farmers and consumers with respect to Thailand 4.0.

The purpose of this paper is to present a new approach for finding a minimum-length trajectory for an autonomous unmanned air vehicle or a long-range missile from a release point with specified release conditions to a destination with specified approach conditions. The trajectory has to avoid obstacles and no-fly zones and must take into account the kinematic constraints of the air vehicle.

A discrete routing model is proposed that represents the airspace by a sophisticated network. The problem is then solved by applying standard shortest-path algorithms.

In contrast to the most widely used grids, the generated networks allow arbitrary flight directions and turn angles, as well as maneuvers of different strengths, thus fully exploiting the flight capabilities of the aircraft. Moreover, the networks are resolution-independent and provide high flexibility by the option to adapt density.

As an application, a concept for in-flight replanning of flight paths to changing destinations is proposed. All computationally intensive tasks are performed in a pre-flight planning prior to the launch of the mission. The in-flight planning is based entirely on precalculated data, which are stored in the onboard computer of the air vehicle. In particular, no path finding algorithms with high or unpredictable running time and uncertain outcome have to be applied during flight.

The paper presents a new network-based algorithm for flight path optimization that overcomes weaknesses of grid-based approaches and allows high-quality solutions. The method can be applied for quick in-flight replanning of flight paths.

The routing algorithm is one of the most important components in designing a network-on-chip (NoC). An effective routing algorithm can cause better performance and throughput, and thus, have less latency, lower power consumption and high reliability. Considering the high scalability in networks and fault occurrence on links, the more the packet reaches the destination (i.e. to cross the number of fewer links), the less the loss of packets and information would be. Accordingly, the proposed algorithm is based on reducing the number of passed links to reach the destination.

This paper presents a high-performance NoC that increases telecommunication network reliability by passing fewer links to destination. A large NoC is divided into small districts with central routers. In such a system, routing in large routes is performed through these central routers district by district.

By reducing the number of links, the number of routers also decreases. As a result, the power consumption is reduced, the performance of the NoC is improved, and the probability of collision with a faulty link and network latency is decreased.

The simulation is performed using the Noxim simulator because of its ability to manage and inject faults. The proposed algorithm, XY routing, as a conventional algorithm for the NoC, was simulated in a 14 × 14 network size, as the typical network size in the recent works.

The purpose of this paper is to present an efficient method to integrate path generation and following for an unmanned aerial vehicle.

The shortest path is briefly reviewed using a straight line and a circular arc in a horizontal plane. Based on shortest paths, various path generation algorithms using oriented waypoints are described. Path design unit, which is structured concatenations of line segments and circular arcs, is proposed to represent different paths as one structure. Simple path following controller to follow a straight line and a circle was also implemented with linear‐quadratic regulator control laws. Some flight tests were conducted to verify the efficiency of proposed algorithm.

Proposed method represents various paths between given waypoints efficiently by a small number of parameters. It does not need a large amount of memory storage and computation time to run in real time on a low‐cost microprocessor.

This paper provides new structured method to generate different paths efficiently including Dubins' set, which makes path following easy by simple switching logic. It needs small computational time to run in real time. The proposed algorithm in this paper could be used as a basis of other applications such as air traffic control and curved landing approach, which require more accurate path control.

To solve the path planning problem of the intelligent driving vehicular, this paper designs a hybrid path planning algorithm based on optimized reinforcement learning (RL) and improved particle swarm optimization (PSO).

First, the authors optimized the hyper-parameters of RL to make it converge quickly and learn more efficiently. Then the authors designed a pre-set operation for PSO to reduce the calculation of invalid particles. Finally, the authors proposed a correction variable that can be obtained from the cumulative reward of RL; this revises the fitness of the individual optimal particle and global optimal position of PSO to achieve an efficient path planning result. The authors also designed a selection parameter system to help to select the optimal path.

Simulation analysis and experimental test results proved that the proposed algorithm has advantages in terms of practicability and efficiency. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.

The authors designed a pre-set operation to reduce the participation of invalid particles in the calculation in PSO. And then, the authors designed a method to optimize hyper-parameters to improve learning efficiency of RL. And then they used RL trained PSO to plan path. The authors also proposed an optimal path evaluation system. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.

With the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.

An improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.

Experimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.

The proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.