Bidirectional Search Algorithm Based-On Index for Shortest Path Problem

2010 ◽  
Author(s):  
Baolin Yi ◽  
Fan Yang ◽  
Haiquan Qiao ◽  
Chenwei Xu
Keyword(s):  
Shortest Path ◽  
Search Algorithm ◽  
Shortest Path Problem ◽  
Bidirectional Search

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).

Iterative Deepening Dynamically Improved Bounds Bidirectional Search

2021 ◽  
Author(s):  
John A. Pavlik ◽  
Edward C. Sewell ◽  
Sheldon H. Jacobson
Keyword(s):  
Shortest Path ◽  
Operations Research ◽  
Search Algorithm ◽  
Desktop Computer ◽  
Shortest Path Problems ◽  
Tile Problem ◽  
Bidirectional Search ◽  
Rubik's Cube ◽  
Rubik’S Cube ◽  
Core Problem

This paper presents a new bidirectional search algorithm to solve the shortest path problem. The new algorithm uses an iterative deepening technique with a consistent heuristic to improve lower bounds on path costs. The new algorithm contains a novel technique of filtering nodes to significantly reduce the memory requirements. Computational experiments on the pancake problem, sliding tile problem, and Rubik’s cube show that the new algorithm uses significantly less memory and executes faster than A* and other state-of-the-art bidirectional algorithms. Summary of Contribution: Quickly solving single-source shortest path problems on graphs is important for pathfinding applications and is a core problem in both artificial intelligence and operations research. This paper attempts to solve large problems that do not easily fit into the available memory of a desktop computer, such as finding the optimal shortest set of moves to solve a Rubik’s cube, and solve them faster than existing algorithms.

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.

Application of Genetic Algorithm in modeling of Shortest Path problem

2020 ◽  
Author(s):  
Zhihui Yang ◽  
Huiwen Xia ◽  
Fuwen Su ◽  
Jiayu Zhao ◽  
Fan Feng
Keyword(s):  
Genetic Algorithm ◽  
Shortest Path ◽  
Shortest Path Problem

Distributionally robust maximum probability shortest path problem

2021 ◽  
Author(s):  
Rashed Khanjani-Shiraz ◽  
Ali Babapour-Azar ◽  
Zohreh Hosseini-Noudeh ◽  
Panos M. Pardalos
Keyword(s):  
Shortest Path ◽  
Shortest Path Problem ◽  
Maximum Probability ◽  
Distributionally Robust

scholarly journals A Comparison of Genetic Representations and Initialisation Methods for the Multi-objective Shortest Path Problem on Multigraphs

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Lilla Beke ◽  
Michal Weiszer ◽  
Jun Chen
Keyword(s):  
Shortest Path ◽  
Time Budget ◽  
Exact Algorithm ◽  
Simple Graph ◽  
Shortest Path Problem ◽  
Future Application ◽  
Multi Objective ◽  
Trade Offs ◽  
Conflicting Objectives ◽  
Problem Instances

AbstractThis paper compares different solution approaches for the multi-objective shortest path problem (MSPP) on multigraphs. Multigraphs as a modelling tool are able to capture different available trade-offs between objectives for a given section of a route. For this reason, they are increasingly popular in modelling transportation problems with multiple conflicting objectives (e.g., travel time and fuel consumption), such as time-dependent vehicle routing, multi-modal transportation planning, energy-efficient driving, and airport operations. The multigraph MSPP is more complex than the NP-hard simple graph MSPP. Therefore, approximate solution methods are often needed to find a good approximation of the true Pareto front in a given time budget. Evolutionary algorithms have been successfully applied for the simple graph MSPP. However, there has been limited investigation of their applications to the multigraph MSPP. Here, we extend the most popular genetic representations to the multigraph case and compare the achieved solution qualities. Two heuristic initialisation methods are also considered to improve the convergence properties of the algorithms. The comparison is based on a diverse set of problem instances, including both bi-objective and triple objective problems. We found that the metaheuristic approach with heuristic initialisation provides good solutions in shorter running times compared to an exact algorithm. The representations were all found to be competitive. The results are encouraging for future application to the time-constrained multigraph MSPP.

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