SH: A Novel Method for the Dynamic and Shortest Path Problem

2010 ◽  
Vol 129-131 ◽  
pp. 1013-1017
Author(s):  
Ya Fei Guo ◽  
Zheng Qin ◽  
Rong Hua Guo ◽  
Lei Ji
Keyword(s):  
Shortest Path ◽  
Heuristic Search ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Real Life ◽  
Shortest Path Problem ◽  
Ant Colony ◽  
Current Path ◽  
Novel Method ◽  
Better Than

For the dynamic and shortest path problem, a novel algorithm SH(simulate human) is designed by simulating the process of our searching path in real life. The algorithm adopts the idea of heuristic search and integrates with the ant colony algorithm, in which the saved current path, the idea of “ask once every junction”, the bypassing barrier search and other some related definitions are proposed, as well as the ant colony algorithm is improved, so as to find the better solution and reduce the searching time. The experimental results show that the algorithm runs better than other existing methods. Moreover, it can find the shortest path or the approximate shortest one in a shorter time on road networks of any scales. Especially, SH algorithm is more effective for the large scale road network.

scholarly journals Parallel Ant Colony Algorithm for Shortest Path Problem

2019 ◽  
Author(s):  
Géza Katona ◽  
Balázs Lénárt ◽  
János Juhász
Keyword(s):  
Parallel Computing ◽  
Shortest Path ◽  
Ant Colony Algorithm ◽  
State Of The Art ◽  
Shortest Path Problem ◽  
Ant Colony ◽  
Parameter Study ◽  
Real Network

During travelling, more and more information must be taken into account, and travelers have to make several complex decisions. In order to support these decisions, IT solutions are unavoidable, and as the computational demand is constantly growing, the examination of state-of-the-art methodologies is necessary. In our research, a parallelized Ant Colony algorithm was investigated, and a parameter study on a real network has been made. The aim was to inspect the sensibility of the method and to demonstrate its applicability in a multi-threaded system (e.g. Cloud-based systems). Based on the research, increased effectiveness can be reached by using more threads. The novelty of the paper is the usage of the processors’ parallel computing capability for routing with the Ant Colony algorithm.

Sensor Configuration Optimizing in Modal Identification by Siege ant Colony Algorithm

2016 ◽  
Vol 33 (2) ◽  
pp. 269-277 ◽  
Author(s):  
S. Feng ◽  
J.-Q. Jia ◽  
J.-C. Zhang
Keyword(s):  
Large Scale ◽  
Ant Colony Algorithm ◽  
Three Dimensional ◽  
Modal Identification ◽  
Ant Colony ◽  
Transmission Tower ◽  
Acceleration Sensors ◽  
Novel Method ◽  
Vital Component ◽  
Sensor Configuration

AbstractProper monitor planning is a vital component of structural health monitoring (SHM) project. An extremely important part of the monitor planning is the placement of sensors, usually in the form of acceleration sensors. For the placement of three-dimensional acceleration sensors, the state of practice is to select the sensor configuration by previous experiences. However, this results in a waste of many sensors. A novel method called siege ant colony algorithm (SAC) is proposed in this paper. This method is built on the previous ant colony optimization (ACO) in the direction of improving efficiency and accuracy when applied to optimal sensor placement (OSP) problems in large-scale structure monitoring. This method is applied and compared with standard approaches using the Hanjiang transmission tower.

A Different Approach for Solving the Shortest Path Problem Under Mixed Fuzzy Environment

2020 ◽  
Vol 9 (2) ◽  
pp. 132-161 ◽  
Author(s):  
Ranjan Kumar ◽  
Sripati Jha ◽  
Ramayan Singh
Keyword(s):  
Linear Programming ◽  
Shortest Path ◽  
Path Length ◽  
Real Life ◽  
Shortest Path Problem ◽  
Fuzzy Linear Programming ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Membership Functions ◽  
Fuzzy Environment

The authors present a new algorithm for solving the shortest path problem (SPP) in a mixed fuzzy environment. With this algorithm, the authors can solve the problems with different sets of fuzzy numbers e.g., normal, trapezoidal, triangular, and LR-flat fuzzy membership functions. Moreover, the authors can solve the fuzzy shortest path problem (FSPP) with two different membership functions such as normal and a fuzzy membership function under real-life situations. The transformation of the fuzzy linear programming (FLP) model into a crisp linear programming model by using a score function is also investigated. Furthermore, the shortcomings of some existing methods are discussed and compared with the algorithm. The objective of the proposed method is to find the fuzzy shortest path (FSP) for the given network; however, this is also capable of predicting the fuzzy shortest path length (FSPL) and crisp shortest path length (CSPL). Finally, some numerical experiments are given to show the effectiveness and robustness of the new model. Numerical results show that this method is superior to the existing methods.

An Improved Ant Colony Algorithm for the Shortest Path in City’s Road Network

2011 ◽  
Vol 121-126 ◽  
pp. 1296-1300 ◽  
Author(s):  
Jun Bi ◽  
Jie Zhang ◽  
Wen Le Xu
Keyword(s):  
Shortest Path ◽  
Road Network ◽  
Ant Colony Algorithm ◽  
Road Traffic ◽  
Global Optimum ◽  
Ant Colony ◽  
Selection Strategy ◽  
Ant Colony System ◽  
Local Optimum ◽  
Information Quantity

The shortest path between the start node and end node plays an important role in city’s road traffic network analysis system. The basic ant colony system algorithm which is a novel simulated evolutionary algorithm is studied to solve the shortest path problem. But the basic ant colony system algorithm is easy to run into the local optimum solution for shortest path. In order to solve the problem, the improved ant colony system algorithm is proposed. The improvement methods for selection strategy, local search, and information quantity modification of basic ant colony system are discussed in detail. The experiments are done in Beijing road network in China. The results of experiments show that comparing with the basic ant colony algorithm, the improved algorithm can easily converge at the global optimum for the shortest path.

Wireless Sensor Network Mobile Agent Routing Based on the Improved Ant Colony Algorithm

2014 ◽  
Vol 587-589 ◽  
pp. 2339-2345
Author(s):  
Jia Yan Li ◽  
Jun Ping Wang
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Mobile Agent ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Routing Algorithm ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Communication Overhead ◽  
Redundant Data

This paper proposes a new wireless sensor routing algorithm by combining the ant colony algorithm with the mobile agent technology. This algorithm considers the distance and path energy overhead among nodes and residual node energy, equalizes the energy overhead in the network, improves the update rule of the ant colony information elements and speeds up convergence of the ant colony algorithm to get the optimal values. The simulation results indicate that this algorithm can improve the globalization and convergence speed, effectively reduce redundant data transmission and communication overhead, extend the network lifecycle and be very suitable for a large-scale wireless sensor network compared to other mobile agent routing algorithms.

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