Parameter-less late acceptance hill-climbing

2017 ◽  
Author(s):  
Mosab Bazargani ◽  
Fernando G. Lobo
Keyword(s):  
Hill Climbing ◽  
Late Acceptance Hill Climbing

Late acceptance hill-climbing for high school timetabling

2015 ◽  
Vol 19 (4) ◽  
pp. 453-465 ◽  
Author(s):  
George H. G. Fonseca ◽  
Haroldo G. Santos ◽  
Eduardo G. Carrano
Keyword(s):  
High School ◽  
Hill Climbing ◽  
High School Timetabling ◽  
School Timetabling ◽  
Late Acceptance Hill Climbing

scholarly journals Late Acceptance Hill Climbing Based Social Ski Driver Algorithm for Feature Selection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 75393-75408 ◽  
Author(s):  
Bitanu Chatterjee ◽  
Trinav Bhattacharyya ◽  
Kushal Kanti Ghosh ◽  
Pawan Kumar Singh ◽  
Zong Woo Geem ◽  
...  
Keyword(s):  
Feature Selection ◽  
Hill Climbing ◽  
Late Acceptance Hill Climbing

scholarly journals Path Planning for Indoor UAV Using A* and Late Acceptance Hill Climbing Algorithms Utilizing Probabilistic Roadmap

2020 ◽  
Vol 9 (4) ◽  
pp. 857
Author(s):  
Jacob Hopkins ◽  
Forrest Joy ◽  
Alaa Sheta ◽  
Hamza Turabieh ◽  
Dulal Kar
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Real Life ◽  
Search Space ◽  
Hill Climbing ◽  
Specific Criterion ◽  
Indoor Space ◽  
Probabilistic Roadmap ◽  
Package Delivery ◽  
Late Acceptance Hill Climbing

The main objective of an unmanned aerial vehicle (UAV) path planning is to generate a flight path that links a start point to an endpoint in an indoor space avoiding obstacles.  Path planning is essential for many real-life applications such as an autonomous car, surveillance mission, farming robots, unmanned aerial vehicles package delivery, space exploration, and many others. To create an optimal path, we need to adopt a specific criterion to minimize the distance the UAV must travel such as the Euclidean distance. In this paper, we provide our initial idea of creating an optimal path for indoor UAV using both A* and the Late Acceptance Hill Climbing (LAHC) algorithms. We are adopting an indoor search environment with various complexity and utilize the Probabilistic Roadmap algorithm (PRM) as a search space for both algorithms. The basic idea following PRM is to generate random sample points in the space and search these points for an optimal path. The developed results show that the LAHC algorithm outperforms the A* algorithm.

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