This paper presents an efficient parallel algorithm for the shortest path problem in planar layered digraphs that runs in O(log^3n) time with n processors. The algorithms uses a divide and conquer approach and is base...This paper presents an efficient parallel algorithm for the shortest path problem in planar layered digraphs that runs in O(log^3n) time with n processors. The algorithms uses a divide and conquer approach and is based on the novel idea of a one-way separator, which has the property that any directed path can be crossed only once.展开更多
This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of nodes.Themain objective was to del...This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of nodes.Themain objective was to delve into and refine the application of the Dijkstra’s algorithm in this context,a method conventionally esteemed for its efficiency in static networks.Thus,this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm,considering adaptation to the dynamic network conditions that are typical for MANETs.This paper has shown through detailed algorithmic analysis that Dijkstra’s algorithm,when adapted for dynamic updates,yields a very workable solution to the problem of real-time routing in MANETs.The results indicate that with these changes,Dijkstra’s algorithm performs much better computationally and 30%better in routing optimization than Bellman-Ford when working with configurations of sparse networks.The theoretical framework adapted,with the adaptation of the Dijkstra’s algorithm for dynamically changing network topologies,is novel in this work and quite different from any traditional application.The adaptation should offer more efficient routing and less computational overhead,most apt in the limited resource environment of MANETs.Thus,from these findings,one may derive a conclusion that the proposed version of Dijkstra’s algorithm is the best and most feasible choice of the routing protocol for MANETs given all pertinent key performance and resource consumption indicators and further that the proposed method offers a marked improvement over traditional methods.This paper,therefore,operationalizes the theoretical model into practical scenarios and also further research with empirical simulations to understand more about its operational effectiveness.展开更多
On the basis of Floyd algorithm with the extended path matrix, a parallel algorithm which resolves all-pair shortest path (APSP) problem on cluster environment is analyzed and designed. Meanwhile, the parallel APSP ...On the basis of Floyd algorithm with the extended path matrix, a parallel algorithm which resolves all-pair shortest path (APSP) problem on cluster environment is analyzed and designed. Meanwhile, the parallel APSP pipelining algorithm makes full use of overlapping technique between computation and communication. Compared with broadcast operation, the parallel algorithm reduces communication cost. This algorithm has been implemented on MPI on PC-cluster. The theoretical analysis and experimental results show that the parallel algorithm is an efficient and scalable algorithm.展开更多
Theoretical research often assumes all users arc homogeneous in their route choice decision and will always pick the route with the shortest travel cost,which is not necessarily the case in reality.This paper document...Theoretical research often assumes all users arc homogeneous in their route choice decision and will always pick the route with the shortest travel cost,which is not necessarily the case in reality.This paper documents the research effort in developing a Constrained Time-Dependent K Shortest Paths Algorithm inorder to find K Shortest Paths between two given locations.The goal of this research is to provide sound route options to travelers in order to assist their route choice decision process,during which the overlap and travel time deviation issues between the K paths will be considered.The proposed algorithm balancing overlap and travel time deviation is developed in this research.A numerical analysis is conducted on the Tucson 1-10 network,the outcome of the case study shows that our proposed algorithm is able to find different shortest paths with a reasonable degree of similarity and close travel time,which indicates that the result of the proposed algorithm is satisfactory.展开更多
Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the muhi-objective shortest path problem ( MSPP ) has a set of pareto optimal solutions and cannot be solved ...Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the muhi-objective shortest path problem ( MSPP ) has a set of pareto optimal solutions and cannot be solved in polynomial time. The present algorithms focused mainly on how to obtain a precisely pareto optimal solution for MSPP resulting in a long time to obtain multiple pareto optimal solutions with them. In order to obtain a set of satisfied solutions for MSPP in reasonable time to meet the demand of a decision maker, a genetic algo- rithm MSPP-GA is presented to solve the MSPP with typically competing objectives, cost and time, in this pa- per. The encoding of the solution and the operators such as crossover, mutation and selection are developed. The algorithm introduced pareto domination tournament and sharing based selection operator, which can not only directly search the pareto optimal frontier but also maintain the diversity of populations in the process of evolutionary computation. Experimental results show that MSPP-GA can obtain most efficient solutions distributed all along the pareto frontier in less time than an exact algorithm. The algorithm proposed in this paper provides a new and effective method of how to obtain the set of pareto optimal solutions for other multiple objective optimization problems in a short time.展开更多
In this figure, it finds a vertex to another vertex k shortest path algorithm. Provided there are n vertices and edges in the diagram. If the path loops, the time complexity of the algorithm is allowed O(w + n log 2...In this figure, it finds a vertex to another vertex k shortest path algorithm. Provided there are n vertices and edges in the diagram. If the path loops, the time complexity of the algorithm is allowed O(w + n log 2 n + kw log 2 k). If the request path does not contain the loop, the time complexity of the algorithm O(kn(w + n log2 n)+ kw log2 k). The algorithm utilizes a simple extension of the Dijkstra algorithm determined the end of the length of the shortest path to the other vertices, and then, based on these data, branch and bound method to identify the required path. Experimental results show that the actual running time has relations with the structure of FIG.展开更多
Genetic algorithm (GA) is one of the alternative approaches for solving the shortest path routing problem. In previous work, we have developed a coarse-grained parallel GA-based shortest path routing algorithm. With p...Genetic algorithm (GA) is one of the alternative approaches for solving the shortest path routing problem. In previous work, we have developed a coarse-grained parallel GA-based shortest path routing algorithm. With parallel GA, there is a GA operator called migration, where a chromosome is taken from one sub-population to replace a chromosome in another sub-population. Which chromosome to be taken and replaced is subjected to the migration strategy used. There are four different migration strategies that can be employed: best replace worst, best replace random, random replace worst, and random replace random. In this paper, we are going to evaluate the effect of different migration strategies on the parallel GA-based routing algorithm that has been developed in the previous work. Theoretically, the migration strategy best replace worst should perform better than the other strategies. However, result from simulation shows that even though the migration strategy best replace worst performs better most of the time, there are situations when one of the other strategies can perform just as well, or sometimes better.展开更多
In this article, we are interested in solving a combinatorial optimization problem, the shortest path problem in a multi-attribute graph, by the out-ranking methods. A multi-attribute graph has simultaneously qualitat...In this article, we are interested in solving a combinatorial optimization problem, the shortest path problem in a multi-attribute graph, by the out-ranking methods. A multi-attribute graph has simultaneously qualitative and quantitative criteria. This situation gives rise to incomparable paths thus forming the Pareto front. Outranking methods in Multi-criteria Decision Making (MCDM) are the only methods that can take into account this situation (incomparability of actions). After presenting the categories of Multi-criteria Decision Making (MCDM) and the difficulties related to the problems of the shortest paths, we propose an evolutionary algorithm based on the outranking methods to solve the problem of finding “best” paths in a multi-attribute graph with non-additive criteria. Our approach is based on the exploration of induced subgraphs of the outranking graph. Properties have been established to serve as algorithmic basis. Numerical experiments have been carried out and the results presented in this article.展开更多
A shortest path routing algorithm based on transient chaotic neural network is proposed in this paper. Gam-pared with previous models adopting Hopfield neural network, this algorithm has a higher ability to overcome t...A shortest path routing algorithm based on transient chaotic neural network is proposed in this paper. Gam-pared with previous models adopting Hopfield neural network, this algorithm has a higher ability to overcome the local minimum, and achieves a better performance. By introducing a special post-processing technique for the output matrixes, our algorithm can obtain an optimal solution with a high probability even for the paths that need more hops in large-size networks.展开更多
The shortest path planning issure is critical for dynamic traffic assignment and route guidance in intelligent transportation systems. In this paper, a Particle Swarm Optimization (PSO) algorithm with priority-based e...The shortest path planning issure is critical for dynamic traffic assignment and route guidance in intelligent transportation systems. In this paper, a Particle Swarm Optimization (PSO) algorithm with priority-based encoding scheme based on fluid neural network (FNN) to search for the shortest path in stochastic traffic networks is introduced. The proposed algorithm overcomes the weight coefficient symmetry restrictions of the traditional FNN and disadvantage of easily getting into a local optimum for PSO. Simulation experiments have been carried out on different traffic network topologies consisting of 15-65 nodes and the results showed that the proposed approach can find the optimal path and closer sub-optimal paths with good success ratio. At the same time, the algorithms greatly improve the convergence efficiency of fluid neuron network.展开更多
In this paper, we present a new algorithm of the time-dependent shortest path problem with time windows. Give a directed graph , where V is a set of nodes, E is a set of edges with a non-negative transit-time function...In this paper, we present a new algorithm of the time-dependent shortest path problem with time windows. Give a directed graph , where V is a set of nodes, E is a set of edges with a non-negative transit-time function . For each node , a time window ?within which the node may be visited and ?, is non-negative of the service and leaving time of the node. A source node s, a destination node d and a departure time?t0, the time-dependent shortest path problem with time windows asks to find an s, d-path that leaves a source node s at a departure time t0;and minimizes the total arrival time at a destination node d. This formulation generalizes the classical shortest path problem in which ce are constants. Our algorithm of the time windows gave the generalization of the ALT algorithm and A* algorithm for the classical problem according to Goldberg and Harrelson [1], Dreyfus [2] and Hart et al. [3].展开更多
In most network analysis tools the computation of the shortest paths between all pairs of nodes is a fundamental step to the discovery of other properties. Among other properties is the computation of closeness centra...In most network analysis tools the computation of the shortest paths between all pairs of nodes is a fundamental step to the discovery of other properties. Among other properties is the computation of closeness centrality, a measure of the nodes that shows how central a vertex is on a given network. In this paper, the authors present a method to compute the All Pairs Shortest Paths on graphs that present two characteristics: abundance of nodes with degree value one, and existence of articulation points along the graph. These characteristics are present in many real life networks especially in networks that show a power law degree distribution as is the case of biological networks. The authors' method compacts the single nodes to their source, and then by using the network articulation points it disconnects the network and computes the shortest paths in the biconnected components. At the final step the authors proposed methods merges the results to provide the whole network shortest paths. The authors' method achieves remarkable speedup compared to state of the art methods to compute the shortest paths, as much as 7 fold speed up in artificial graphs and 3.25 fold speed up in real application graphs. The authors' performance improvement is unlike previous research as it does not involve elaborated setups since the authors algorithm can process significant instances on a popular workstation.展开更多
The All-pairs shortest path problem(ALL-SPP)aims to find the shortest path joining all the vertices in a given graph.This study proposed a new optimal method,Dhouib-matrix-ALL-SPP(DM-ALL-SPP)to solve the ALL-SPP based...The All-pairs shortest path problem(ALL-SPP)aims to find the shortest path joining all the vertices in a given graph.This study proposed a new optimal method,Dhouib-matrix-ALL-SPP(DM-ALL-SPP)to solve the ALL-SPP based on column-row navigation through the adjacency matrix.DM-ALL-SPP is designed to generate in a single execution the shortest path with details among all-pairs of vertices for a graph with positive and negative weighted edges.Even for graphs with a negative cycle,DM-ALL-SPP reported a negative cycle.In addition,DM-ALL-SPP continues to work for directed,undirected and mixed graphs.Furthermore,it is characterized by two phases:the first phase consists of adding by column repeated(n)iterations(where n is the number of vertices),and the second phase resides in adding by row executed in the worst case(n∗log(n))iterations.The first phase,focused on improving the elements of each column by adding their values to each row and modifying them with the smallest value.The second phase is emphasized by rows only for the elements modified in the first phase.Different instances from the literature were used to test the performance of the proposed DM-ALL-SPP method,which was developed using the Python programming language and the results were compared to those obtained by the Floyd-Warshall algorithm.展开更多
Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario.In this study,we investigated the post-disaster rescue path planning probl...Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario.In this study,we investigated the post-disaster rescue path planning problem and modeled this problem as a variant of the travel salesman problem(TSP)with life-strength constraints.To address this problem,we proposed an improved iterated greedy(IIG)algorithm.First,a push-forward insertion heuristic(PFIH)strategy was employed to generate a high-quality initial solution.Second,a greedy-based insertion strategy was designed and used in the destruction-construction stage to increase the algorithm’s exploration ability.Furthermore,three problem-specific swap operators were developed to improve the algorithm’s exploitation ability.Additionally,an improved simulated annealing(SA)strategy was used as an acceptance criterion to effectively prevent the algorithm from falling into local optima.To verify the effectiveness of the proposed algorithm,the Solomon dataset was extended to generate 27 instances for simulation.Finally,the proposed IIG was compared with five state-of-the-art algorithms.The parameter analysiswas conducted using the design of experiments(DOE)Taguchi method,and the effectiveness analysis of each component has been verified one by one.Simulation results indicate that IIGoutperforms the compared algorithms in terms of the number of rescue survivors and convergence speed,proving the effectiveness of the proposed algorithm.展开更多
Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Fi...Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.展开更多
This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapi...This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.展开更多
A new genetic algorithm named niche pseudo-parallel genetic algorithm (NPPGA) is presented for path evolution and genetic optimization of autonomous mobile robot. The NPPGA is an effective improvement to maintain th...A new genetic algorithm named niche pseudo-parallel genetic algorithm (NPPGA) is presented for path evolution and genetic optimization of autonomous mobile robot. The NPPGA is an effective improvement to maintain the population diversity as well for the sake of avoiding premature and strengthen parallelism of the population to accelerate the search process combined with niche genetic algorithms and pseudo-parallel genetic algorithms. The proposed approach is evaluated by robotic path optimization, which is a specific application of traveler salesman problem (TSP). Experimental results indicated that a shortest path could be obtained in the practical traveling salesman problem named "Robot tour around Pekin", and the performance conducted by NPPGA is better than simple genetic algorithm (SGA) and distributed paralell genetic algorithms (DPGA).展开更多
A contour-parallel offset (CPO) tool-path linking algorithm is derived without toolretractions and with the largest practicability. The concept of "tool-path loop tree" (TPL-tree) providing the information on th...A contour-parallel offset (CPO) tool-path linking algorithm is derived without toolretractions and with the largest practicability. The concept of "tool-path loop tree" (TPL-tree) providing the information on the parent/child relationships among the tool-path loops (TPLs) is presented. The direction, tool-path loop, leaf/branch, layer number, and the corresponding points of the TPL-tree are introduced. By defining TPL as a vector, and by traveling throughout the tree, a CPO tool-path without tool-retractions can be derived.展开更多
The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operationa...The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.展开更多
文摘This paper presents an efficient parallel algorithm for the shortest path problem in planar layered digraphs that runs in O(log^3n) time with n processors. The algorithms uses a divide and conquer approach and is based on the novel idea of a one-way separator, which has the property that any directed path can be crossed only once.
基金supported by Northern Border University,Arar,Kingdom of Saudi Arabia,through the Project Number“NBU-FFR-2024-2248-03”.
文摘This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of nodes.Themain objective was to delve into and refine the application of the Dijkstra’s algorithm in this context,a method conventionally esteemed for its efficiency in static networks.Thus,this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm,considering adaptation to the dynamic network conditions that are typical for MANETs.This paper has shown through detailed algorithmic analysis that Dijkstra’s algorithm,when adapted for dynamic updates,yields a very workable solution to the problem of real-time routing in MANETs.The results indicate that with these changes,Dijkstra’s algorithm performs much better computationally and 30%better in routing optimization than Bellman-Ford when working with configurations of sparse networks.The theoretical framework adapted,with the adaptation of the Dijkstra’s algorithm for dynamically changing network topologies,is novel in this work and quite different from any traditional application.The adaptation should offer more efficient routing and less computational overhead,most apt in the limited resource environment of MANETs.Thus,from these findings,one may derive a conclusion that the proposed version of Dijkstra’s algorithm is the best and most feasible choice of the routing protocol for MANETs given all pertinent key performance and resource consumption indicators and further that the proposed method offers a marked improvement over traditional methods.This paper,therefore,operationalizes the theoretical model into practical scenarios and also further research with empirical simulations to understand more about its operational effectiveness.
基金the National Natural Science Foundation of China under Grant No. 60671033.
文摘On the basis of Floyd algorithm with the extended path matrix, a parallel algorithm which resolves all-pair shortest path (APSP) problem on cluster environment is analyzed and designed. Meanwhile, the parallel APSP pipelining algorithm makes full use of overlapping technique between computation and communication. Compared with broadcast operation, the parallel algorithm reduces communication cost. This algorithm has been implemented on MPI on PC-cluster. The theoretical analysis and experimental results show that the parallel algorithm is an efficient and scalable algorithm.
文摘Theoretical research often assumes all users arc homogeneous in their route choice decision and will always pick the route with the shortest travel cost,which is not necessarily the case in reality.This paper documents the research effort in developing a Constrained Time-Dependent K Shortest Paths Algorithm inorder to find K Shortest Paths between two given locations.The goal of this research is to provide sound route options to travelers in order to assist their route choice decision process,during which the overlap and travel time deviation issues between the K paths will be considered.The proposed algorithm balancing overlap and travel time deviation is developed in this research.A numerical analysis is conducted on the Tucson 1-10 network,the outcome of the case study shows that our proposed algorithm is able to find different shortest paths with a reasonable degree of similarity and close travel time,which indicates that the result of the proposed algorithm is satisfactory.
文摘Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the muhi-objective shortest path problem ( MSPP ) has a set of pareto optimal solutions and cannot be solved in polynomial time. The present algorithms focused mainly on how to obtain a precisely pareto optimal solution for MSPP resulting in a long time to obtain multiple pareto optimal solutions with them. In order to obtain a set of satisfied solutions for MSPP in reasonable time to meet the demand of a decision maker, a genetic algo- rithm MSPP-GA is presented to solve the MSPP with typically competing objectives, cost and time, in this pa- per. The encoding of the solution and the operators such as crossover, mutation and selection are developed. The algorithm introduced pareto domination tournament and sharing based selection operator, which can not only directly search the pareto optimal frontier but also maintain the diversity of populations in the process of evolutionary computation. Experimental results show that MSPP-GA can obtain most efficient solutions distributed all along the pareto frontier in less time than an exact algorithm. The algorithm proposed in this paper provides a new and effective method of how to obtain the set of pareto optimal solutions for other multiple objective optimization problems in a short time.
文摘In this figure, it finds a vertex to another vertex k shortest path algorithm. Provided there are n vertices and edges in the diagram. If the path loops, the time complexity of the algorithm is allowed O(w + n log 2 n + kw log 2 k). If the request path does not contain the loop, the time complexity of the algorithm O(kn(w + n log2 n)+ kw log2 k). The algorithm utilizes a simple extension of the Dijkstra algorithm determined the end of the length of the shortest path to the other vertices, and then, based on these data, branch and bound method to identify the required path. Experimental results show that the actual running time has relations with the structure of FIG.
文摘Genetic algorithm (GA) is one of the alternative approaches for solving the shortest path routing problem. In previous work, we have developed a coarse-grained parallel GA-based shortest path routing algorithm. With parallel GA, there is a GA operator called migration, where a chromosome is taken from one sub-population to replace a chromosome in another sub-population. Which chromosome to be taken and replaced is subjected to the migration strategy used. There are four different migration strategies that can be employed: best replace worst, best replace random, random replace worst, and random replace random. In this paper, we are going to evaluate the effect of different migration strategies on the parallel GA-based routing algorithm that has been developed in the previous work. Theoretically, the migration strategy best replace worst should perform better than the other strategies. However, result from simulation shows that even though the migration strategy best replace worst performs better most of the time, there are situations when one of the other strategies can perform just as well, or sometimes better.
文摘In this article, we are interested in solving a combinatorial optimization problem, the shortest path problem in a multi-attribute graph, by the out-ranking methods. A multi-attribute graph has simultaneously qualitative and quantitative criteria. This situation gives rise to incomparable paths thus forming the Pareto front. Outranking methods in Multi-criteria Decision Making (MCDM) are the only methods that can take into account this situation (incomparability of actions). After presenting the categories of Multi-criteria Decision Making (MCDM) and the difficulties related to the problems of the shortest paths, we propose an evolutionary algorithm based on the outranking methods to solve the problem of finding “best” paths in a multi-attribute graph with non-additive criteria. Our approach is based on the exploration of induced subgraphs of the outranking graph. Properties have been established to serve as algorithmic basis. Numerical experiments have been carried out and the results presented in this article.
文摘A shortest path routing algorithm based on transient chaotic neural network is proposed in this paper. Gam-pared with previous models adopting Hopfield neural network, this algorithm has a higher ability to overcome the local minimum, and achieves a better performance. By introducing a special post-processing technique for the output matrixes, our algorithm can obtain an optimal solution with a high probability even for the paths that need more hops in large-size networks.
文摘The shortest path planning issure is critical for dynamic traffic assignment and route guidance in intelligent transportation systems. In this paper, a Particle Swarm Optimization (PSO) algorithm with priority-based encoding scheme based on fluid neural network (FNN) to search for the shortest path in stochastic traffic networks is introduced. The proposed algorithm overcomes the weight coefficient symmetry restrictions of the traditional FNN and disadvantage of easily getting into a local optimum for PSO. Simulation experiments have been carried out on different traffic network topologies consisting of 15-65 nodes and the results showed that the proposed approach can find the optimal path and closer sub-optimal paths with good success ratio. At the same time, the algorithms greatly improve the convergence efficiency of fluid neuron network.
文摘In this paper, we present a new algorithm of the time-dependent shortest path problem with time windows. Give a directed graph , where V is a set of nodes, E is a set of edges with a non-negative transit-time function . For each node , a time window ?within which the node may be visited and ?, is non-negative of the service and leaving time of the node. A source node s, a destination node d and a departure time?t0, the time-dependent shortest path problem with time windows asks to find an s, d-path that leaves a source node s at a departure time t0;and minimizes the total arrival time at a destination node d. This formulation generalizes the classical shortest path problem in which ce are constants. Our algorithm of the time windows gave the generalization of the ALT algorithm and A* algorithm for the classical problem according to Goldberg and Harrelson [1], Dreyfus [2] and Hart et al. [3].
文摘In most network analysis tools the computation of the shortest paths between all pairs of nodes is a fundamental step to the discovery of other properties. Among other properties is the computation of closeness centrality, a measure of the nodes that shows how central a vertex is on a given network. In this paper, the authors present a method to compute the All Pairs Shortest Paths on graphs that present two characteristics: abundance of nodes with degree value one, and existence of articulation points along the graph. These characteristics are present in many real life networks especially in networks that show a power law degree distribution as is the case of biological networks. The authors' method compacts the single nodes to their source, and then by using the network articulation points it disconnects the network and computes the shortest paths in the biconnected components. At the final step the authors proposed methods merges the results to provide the whole network shortest paths. The authors' method achieves remarkable speedup compared to state of the art methods to compute the shortest paths, as much as 7 fold speed up in artificial graphs and 3.25 fold speed up in real application graphs. The authors' performance improvement is unlike previous research as it does not involve elaborated setups since the authors algorithm can process significant instances on a popular workstation.
文摘The All-pairs shortest path problem(ALL-SPP)aims to find the shortest path joining all the vertices in a given graph.This study proposed a new optimal method,Dhouib-matrix-ALL-SPP(DM-ALL-SPP)to solve the ALL-SPP based on column-row navigation through the adjacency matrix.DM-ALL-SPP is designed to generate in a single execution the shortest path with details among all-pairs of vertices for a graph with positive and negative weighted edges.Even for graphs with a negative cycle,DM-ALL-SPP reported a negative cycle.In addition,DM-ALL-SPP continues to work for directed,undirected and mixed graphs.Furthermore,it is characterized by two phases:the first phase consists of adding by column repeated(n)iterations(where n is the number of vertices),and the second phase resides in adding by row executed in the worst case(n∗log(n))iterations.The first phase,focused on improving the elements of each column by adding their values to each row and modifying them with the smallest value.The second phase is emphasized by rows only for the elements modified in the first phase.Different instances from the literature were used to test the performance of the proposed DM-ALL-SPP method,which was developed using the Python programming language and the results were compared to those obtained by the Floyd-Warshall algorithm.
基金supported by the Opening Fund of Shandong Provincial Key Laboratory of Network based Intelligent Computing,the National Natural Science Foundation of China(52205529,61803192)the Natural Science Foundation of Shandong Province(ZR2021QE195)+1 种基金the Youth Innovation Team Program of Shandong Higher Education Institution(2023KJ206)the Guangyue Youth Scholar Innovation Talent Program support received from Liaocheng University(LCUGYTD2022-03).
文摘Effective path planning is crucial for mobile robots to quickly reach rescue destination and complete rescue tasks in a post-disaster scenario.In this study,we investigated the post-disaster rescue path planning problem and modeled this problem as a variant of the travel salesman problem(TSP)with life-strength constraints.To address this problem,we proposed an improved iterated greedy(IIG)algorithm.First,a push-forward insertion heuristic(PFIH)strategy was employed to generate a high-quality initial solution.Second,a greedy-based insertion strategy was designed and used in the destruction-construction stage to increase the algorithm’s exploration ability.Furthermore,three problem-specific swap operators were developed to improve the algorithm’s exploitation ability.Additionally,an improved simulated annealing(SA)strategy was used as an acceptance criterion to effectively prevent the algorithm from falling into local optima.To verify the effectiveness of the proposed algorithm,the Solomon dataset was extended to generate 27 instances for simulation.Finally,the proposed IIG was compared with five state-of-the-art algorithms.The parameter analysiswas conducted using the design of experiments(DOE)Taguchi method,and the effectiveness analysis of each component has been verified one by one.Simulation results indicate that IIGoutperforms the compared algorithms in terms of the number of rescue survivors and convergence speed,proving the effectiveness of the proposed algorithm.
基金supported by National Natural Science Foundation of China(71904006)Henan Province Key R&D Special Project(231111322200)+1 种基金the Science and Technology Research Plan of Henan Province(232102320043,232102320232,232102320046)the Natural Science Foundation of Henan(232300420317,232300420314).
文摘Reducing casualties and property losses through effective evacuation route planning has been a key focus for researchers in recent years.As part of this effort,an enhanced sparrow search algorithm(MSSA)was proposed.Firstly,the Golden Sine algorithm and a nonlinear weight factor optimization strategy were added in the discoverer position update stage of the SSA algorithm.Secondly,the Cauchy-Gaussian perturbation was applied to the optimal position of the SSA algorithm to improve its ability to jump out of local optima.Finally,the local search mechanism based on the mountain climbing method was incorporated into the local search stage of the SSA algorithm,improving its local search ability.To evaluate the effectiveness of the proposed algorithm,the Whale Algorithm,Gray Wolf Algorithm,Improved Gray Wolf Algorithm,Sparrow Search Algorithm,and MSSA Algorithm were employed to solve various test functions.The accuracy and convergence speed of each algorithm were then compared and analyzed.The results indicate that the MSSA algorithm has superior solving ability and stability compared to other algorithms.To further validate the enhanced algorithm’s capabilities for path planning,evacuation experiments were conducted using different maps featuring various obstacle types.Additionally,a multi-exit evacuation scenario was constructed according to the actual building environment of a teaching building.Both the sparrow search algorithm and MSSA algorithm were employed in the simulation experiment for multiexit evacuation path planning.The findings demonstrate that the MSSA algorithm outperforms the comparison algorithm,showcasing its greater advantages and higher application potential.
基金the National Natural Science Foundation of China(Grant No.42274119)the Liaoning Revitalization Talents Program(Grant No.XLYC2002082)+1 种基金National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(Grant No.2022YFF1400500)the Key Project of Science and Technology Commission of the Central Military Commission.
文摘This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.
文摘A new genetic algorithm named niche pseudo-parallel genetic algorithm (NPPGA) is presented for path evolution and genetic optimization of autonomous mobile robot. The NPPGA is an effective improvement to maintain the population diversity as well for the sake of avoiding premature and strengthen parallelism of the population to accelerate the search process combined with niche genetic algorithms and pseudo-parallel genetic algorithms. The proposed approach is evaluated by robotic path optimization, which is a specific application of traveler salesman problem (TSP). Experimental results indicated that a shortest path could be obtained in the practical traveling salesman problem named "Robot tour around Pekin", and the performance conducted by NPPGA is better than simple genetic algorithm (SGA) and distributed paralell genetic algorithms (DPGA).
文摘A contour-parallel offset (CPO) tool-path linking algorithm is derived without toolretractions and with the largest practicability. The concept of "tool-path loop tree" (TPL-tree) providing the information on the parent/child relationships among the tool-path loops (TPLs) is presented. The direction, tool-path loop, leaf/branch, layer number, and the corresponding points of the TPL-tree are introduced. By defining TPL as a vector, and by traveling throughout the tree, a CPO tool-path without tool-retractions can be derived.
文摘The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.