Evolutionary Deployment and Hill Climbing-Based Movements of Multi-UAV Networks in Disaster Scenarios

2018 ◽  
pp. 63-95 ◽  
Author(s):  
D. G. Reina ◽  
T. Camp ◽  
A. Munjal ◽  
S. L. Toral ◽  
H. Tawfik
Keyword(s):  
Hill Climbing ◽  
Multi Uav

Four-Dimensional Trajectory Generation for UAVs Based on Multi-Agent Q Learning

2020 ◽  
Vol 73 (4) ◽  
pp. 874-891
Author(s):  
Wenjie Zhao ◽  
Zhou Fang ◽  
Zuqiang Yang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Arrival Time ◽  
Trajectory Generation ◽  
Hill Climbing ◽  
Q Learning ◽  
Continuous State ◽  
Multi Agent ◽  
Multiple Unmanned Aerial Vehicles ◽  
Simulation Results ◽  
Multi Uav

A distributed four-dimensional (4D) trajectory generation method based on multi-agent Q learning is presented for multiple unmanned aerial vehicles (UAVs). Based on this method, each vehicle can intelligently generate collision-free 4D trajectories for time-constrained cooperative flight tasks. For a single UAV, the 4D trajectory is generated by the bionic improved tau gravity guidance strategy, which can synchronously guide the position and velocity to the desired values at the arrival time. Furthermore, to optimise trajectory parameters, the continuous state and action wire fitting neural network Q (WFNNQ) learning method is applied. For multi-UAV applications, the learning is organised by the win or learn fast-policy hill climbing (WoLF-PHC) algorithm. Dynamic simulation results show that the proposed method can efficiently provide 4D trajectories for the multi-UAV system in challenging simultaneous arrival tasks, and the fully trained method can be used in similar trajectory generation scenarios.

Privacy-Preserving Fingerprint Authentication Resistant to Hill-Climbing Attacks

2018 ◽  
Vol E101.A (1) ◽  
pp. 138-148 ◽  
Author(s):  
Haruna HIGO ◽  
Toshiyuki ISSHIKI ◽  
Kengo MORI ◽  
Satoshi OBANA
Keyword(s):  
Privacy Preserving ◽  
Hill Climbing

Autonomous Hierarchical Multi-Level Clustering for Multi-UAV Systems

2021 ◽  
Author(s):  
Jonathan Ponniah ◽  
Mirco Theile ◽  
Or Dantsker ◽  
Marco Caccamo
Keyword(s):  
Multi Level ◽  
Multi Uav

CAUSE Multi-UAV Simulation Demonstration

2011 ◽  
Author(s):  
Herbert E. Viggh ◽  
Christopher Weed ◽  
Michael T. Chan ◽  
Daniel J. Van Hook
Keyword(s):  
Multi Uav

ALGORITMA STEEPEST ASCENT HILL CLIMBING UNTUK MENYELESAIKAN MASALAH PENUGASAN

2011 ◽  
Vol 4 (1) ◽  
Author(s):  
Daisy Ika Christiana ◽  
Gloria Virginia ◽  
Joko Purwadi
Keyword(s):  
Operation Research ◽  
Hill Climbing ◽  
Steepest Ascent

Penugasan adalah penetapan tugas pada tiap pekefia dan tugasnyaagil mendapat hasilyang paling baik, yaitu dengan hasil akhir maksimal dan waktu yang minimal. Masalahpenugasan memiliki syarat satu pekerja hanya mengerjakan satu tugas dan sebaliknya satutugas hanya dikerjakan oleh satu pekerja. Penugasan adalah bagian dari bidang ilmu RisetOperasi (Operation Research ), yang biasa digunakan untuk mencari hasil maksimal atauminimal. Sistem yang akan dibuat ini didukung oleh dua bidang ilmu, yaitu riset operasi dankecerdasan buatan.Dalam bidang ilmu kecerdasan buatan terdapat algoritma yang bisadigunakan untuk memecahkan masalah maksimalisasi yaitu Algoritma Sfeepesf Ascent HillClimbing.Sistem yang dibuat digunakan untukn mencari hasil akhir yang maksimal dari kapasitassuatu produksi. Masukkan berupa jumlah mesin, jumlah pekerja, dan jumlah produksi (matrikkapsitas), prosesnya dilakukan dengan mengkombinasikan hasil perhitungan matrik kapasitasmenggunakan teori penugasan, dan mencari hasil terbaik dengan algoritma Steepest AscentHill Climbing. Hasil perhitungan ditampilkan dalam bentuk rekap iterasi, perhitungan waktuproses, dan komposisi pekerja dan tugasnya. Hasil perhitungan penugasan menggunakanAlgoritma Steepest Ascent Hill Climbing telah diujicoba dengan perhitungan secara sistem danmanual, dan mendapatkan hasil akhir yang sama, jadi bisa dikatakan sistem cukup akurat.

An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

2020 ◽  
Vol 16 (4) ◽  
pp. 296-306 ◽  
Author(s):  
Laith Mohammad Abualigah ◽  
Essam Said Hanandeh ◽  
Ahamad Tajudin Khader ◽  
Mohammed Abdallh Otair ◽  
Shishir Kumar Shandilya
Keyword(s):  
Optimization Technique ◽  
Document Clustering ◽  
Text Clustering ◽  
Hill Climbing ◽  
Text Documents ◽  
Clustering Problem ◽  
Text Document ◽  
Text Information ◽  
Amount Of Knowledge ◽  
The Hill

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

Some consequences of body size

1984 ◽  
Vol 247 (4) ◽  
pp. H495-H507 ◽  
Author(s):  
L. E. Ford
Keyword(s):  
Heart Rate ◽  
Body Weight ◽  
Metabolic Rate ◽  
Muscle Power ◽  
Weight Ratio ◽  
Single Species ◽  
Hill Climbing ◽  
Proper Size ◽  
Higher Power ◽  
Metabolic Indices

The question of the proper size denominator for metabolic indices is addressed. Metabolic rate among different species is proportional to the 3/4 power of body weight, not surface area. Muscle power also varies with the 3/4 power of weight, suggesting that metabolic rate is determined mainly by muscle power. Power-to-weight ratio, specific metabolic rate, and a number of metabolic periods, including heart rate, all vary inversely with the 1/4 power of body weight. Thus the relative times required for physiological and pathological processes in different species may be estimated from the average resting heart rate for the species. There are not many small humans among athletic record holders in events involving acceleration and hill climbing, as would be expected if they had higher power-to-weight ratios. Thus the relationship between size and metabolic rate in different species should not be applied within the single species of humans. Evidence is reviewed showing that basal metabolic rate in humans is determined mainly by lean body mass.

scholarly journals A smart hill-climbing algorithm for application server configuration

2004 ◽  
Author(s):  
Bowei Xi ◽  
Zhen Liu ◽  
Mukund Raghavachari ◽  
Cathy H. Xia ◽  
Li Zhang
Keyword(s):  
Hill Climbing ◽  
Application Server ◽  
Hill Climbing Algorithm
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