scholarly journals Heuristic Function Influence to the Global Optimum Value in Shortest Path Problem

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Shortest Path ◽  
Search Algorithm ◽  
Daily Life ◽  
Global Optimum ◽  
Shortest Path Problem ◽  
Manhattan Distance ◽  
Evaluation Function ◽  
Heuristic Function ◽  
Optimum Route

Determination of the optimum route is often encountered in daily life. The purpose of the optimum route itself is to find the best trajectory of the two pairs of vertices contained in a map or graph. The search algorithm applied is A*. This algorithm has the evaluation function to assist the search. The function is called heuristic. Two methods which have been introduced as a step to obtain the value of heuristic function are by using Euclidean and Manhattan distance. Both of these methods create the optimum distance in shortest path problem, but these functions gain the different results. This research performs the development of the heuristic function using Euclidean, Manhattan, Euclidean Square and the author method (Andysah).

scholarly journals Learning Optimal Bayesian Networks: A Shortest Path Perspective

2013 ◽  
Vol 48 ◽  
pp. 23-65 ◽  
Author(s):  
C. Yuan ◽  
B. Malone
Keyword(s):  
Bayesian Network ◽  
Shortest Path ◽  
Network Structure ◽  
Search Algorithm ◽  
Scoring Function ◽  
Solution Space ◽  
Shortest Path Problem ◽  
Heuristic Function ◽  
Research Issues ◽  
Bayesian Network Structure

In this paper, learning a Bayesian network structure that optimizes a scoring function for a given dataset is viewed as a shortest path problem in an implicit state-space search graph. This perspective highlights the importance of two research issues: the development of search strategies for solving the shortest path problem, and the design of heuristic functions for guiding the search. This paper introduces several techniques for addressing the issues. One is an A* search algorithm that learns an optimal Bayesian network structure by only searching the most promising part of the solution space. The others are mainly two heuristic functions. The first heuristic function represents a simple relaxation of the acyclicity constraint of a Bayesian network. Although admissible and consistent, the heuristic may introduce too much relaxation and result in a loose bound. The second heuristic function reduces the amount of relaxation by avoiding directed cycles within some groups of variables. Empirical results show that these methods constitute a promising approach to learning optimal Bayesian network structures.

Research on the Shortest Path Problem Based on Green Transportation

2014 ◽  
Vol 1010-1012 ◽  
pp. 1858-1861
Author(s):  
Bao You Liu ◽  
Ya Ru Liu
Keyword(s):  
Shortest Path ◽  
Operations Research ◽  
Optimization Problem ◽  
Daily Life ◽  
Mathematical Optimization ◽  
Shortest Path Problem ◽  
Hebei Province ◽  
Research Knowledge ◽  
Green Transportation ◽  
Mathematical Optimization Problem

The shortest path problem is a typical mathematical optimization problem which often encountered in the production field and daily life. From the perspective of green transportation, in this paper, the shortest path problem in Hebei Province was put forward that applied the operations research knowledge, and solved the analysis by lingo11.0. The results showed that the shortest path is 2230.00 km that started from Shijiazhuang through each prefecture-level city, then back to Shijiazhuang. The shortest path from Shijiazhuang to Qinhuangdao is 589.00 km.

Determination of Optimal Public Transportation Routes Using Firefly and Tabu Search Algorithms

2020 ◽  
Vol 4 (5) ◽  
pp. 884-891
Author(s):  
Salwa Salsabila Mansur ◽  
Sri Widowati ◽  
Mahmud Imrona
Keyword(s):  
Tabu Search ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Search Algorithm ◽  
Urban Transportation ◽  
The Public ◽  
Comparison Results ◽  
Congestion Problems ◽  
Transportation Routes

Traffic congestion problems generally caused by the increasing use of private vehicles and public transportations. In order to overcome the situation, the optimization of public transportation’s route is required particularly the urban transportation. In this research, the performance analysis of Firefly and Tabu Search algorithm is conducted to optimize eleven public transportation’s routes in Bandung. This optimization aims to increase the dispersion of public transportation’s route by expanding the scope of route that are crossed by public transportation so that it can reach the entire Bandung city and increase the driver’s income by providing the passengers easier access to public transportations in order to get to their destinations. The optimal route is represented by the route with most roads and highest number of incomes. In this research, the comparison results between the reference route and the public transportation’s optimized route increasing the dispersion of public transportation’s route to 60,58% and increasing the driver’s income to 20,03%.

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