scholarly journals Enhanced Methods of Path Finding Based on An Abstract Graph with Extension of Search Space

2011 ◽  
Vol 15 (1) ◽  
pp. 157-162 ◽  
Author(s):  
Dae-Soo Cho
Keyword(s):  
Search Space ◽  
Path Finding ◽  
Abstract Graph

scholarly journals Symmetry-Breaking Constraints for Grid-Based Multi-Agent Path Finding

2019 ◽  
Vol 33 ◽  
pp. 6087-6095 ◽  
Author(s):  
Jiaoyang Li ◽  
Daniel Harabor ◽  
Peter J. Stuckey ◽  
Hang Ma ◽  
Sven Koenig
Keyword(s):  
Symmetry Breaking ◽  
State Of The Art ◽  
Search Space ◽  
Single Step ◽  
Path Finding ◽  
Current Path ◽  
Rate Versus ◽  
Multi Agent ◽  
Specialized Technique ◽  
A Current

We describe a new way of reasoning about symmetric collisions for Multi-Agent Path Finding (MAPF) on 4-neighbor grids. We also introduce a symmetry-breaking constraint to resolve these conflicts. This specialized technique allows us to identify and eliminate, in a single step, all permutations of two currently assigned but incompatible paths. Each such permutation has exactly the same cost as a current path, and each one results in a new collision between the same two agents. We show that the addition of symmetry-breaking techniques can lead to an exponential reduction in the size of the search space of CBS, a popular framework for MAPF, and report significant improvements in both runtime and success rate versus CBSH and EPEA* – two recent and state-of-the-art MAPF algorithms.

scholarly journals A Gradual Approach for Multimodel Journey Planning: A Case Study in Izmir, Turkey

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Feriştah Dalkılıç ◽  
Yunus Doğan ◽  
Derya Birant ◽  
Recep Alp Kut ◽  
Reyat Yılmaz
Keyword(s):  
Public Transportation ◽  
Search Space ◽  
Planning System ◽  
Path Finding ◽  
Service Oriented ◽  
Node Routing ◽  
Gradual Approach ◽  
User Friendly ◽  
Transportation Agencies

Planning a journey by integrating route and timetable information from diverse sources of transportation agencies such as bus, ferry, and train can be complicated. A user-friendly, informative journey planning system may simplify a plan by providing assistance in making better use of public transportation. In this study, we presented the service-oriented, multimodel Intelligent Journey Planning System, which we developed to assist travelers in journey planning. We selected Izmir, Turkey, as the pilot city for this system. The multicriteria problem is one of the well-known problems in transportation networks. Our study proposes a gradual path-finding algorithm to solve this problem by considering transfer count and travel time. The algorithm utilizes the techniques of efficient algorithms including round based public transit optimized router, transit node routing, and contraction hierarchies on transportation graph. We employed Dijkstra’s algorithm after the first stage of the path-finding algorithm by applying stage specific rules to reduce search space and runtime. The experimental results show that our path-finding algorithm takes 0.63 seconds of processing time on average, which is acceptable for the user experience.

A Path Finding Heuristic Algorithm for A Road Network Hierarchy

2009 ◽  
Vol 11 (2) ◽  
pp. 151-156 ◽  
Author(s):  
Song GAO ◽  
Feng LU
Keyword(s):  
Heuristic Algorithm ◽  
Road Network ◽  
Path Finding ◽  
Network Hierarchy

A Loop-Free Path-Finding Algorithm: Specification, Verification and Complexity

1995 ◽  
Author(s):  
J. J. Garcia-Luna-Aceves ◽  
Shree Murthy
Keyword(s):  
Free Path ◽  
Path Finding

Feature Selection Algorithm for High-dimensional Biomedical Data Using Information Gain and Improved Chemical Reaction Optimization

2021 ◽  
Vol 15 (8) ◽  
pp. 912-926
Author(s):  
Ge Zhang ◽  
Pan Yu ◽  
Jianlin Wang ◽  
Chaokun Yan
Keyword(s):  
Feature Selection ◽  
Chemical Reaction ◽  
Information Gain ◽  
Feature Selection Method ◽  
Search Space ◽  
Neighborhood Search ◽  
Biomedical Data ◽  
Chemical Reaction Optimization ◽  
Search Mechanism ◽  
Reaction Optimization

Background: There have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. However, these datasets usually involve thousands of features and include much irrelevant or redundant information, which leads to confusion during diagnosis. Feature selection is a solution that consists of finding the optimal subset, which is known to be an NP problem because of the large search space. Objective: For the issue, this paper proposes a hybrid feature selection method based on an improved chemical reaction optimization algorithm (ICRO) and an information gain (IG) approach, which called IGICRO. Methods: IG is adopted to obtain some important features. The neighborhood search mechanism is combined with ICRO to increase the diversity of the population and improve the capacity of local search. Results: Experimental results of eight public available data sets demonstrate that our proposed approach outperforms original CRO and other state-of-the-art approaches.

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