Artificial Intelegence PENERAPAN KASUS ALGORITMA ASCENT HILL CLIMBING DALAM PERMAINAN PUZZLE 8

2021 ◽  
Vol 14 (2) ◽  
pp. 325-331
Author(s):  
Yosdarso Afero
Keyword(s):  
Heuristic Method ◽  
Hill Climbing ◽  
Initial State ◽  
Hill Climbing Algorithm ◽  
Correct Sequence ◽  
Optimal Value ◽  
Numerical Order ◽  
Puzzle Games ◽  
Time Required ◽  
User Knowledge

Puzzle game is a game that shifts numbers from a box consisting of nine boxes. Eight boxes must have values arranged in numerical order starting from numbers 1 to 8. Puzzle games can produce the correct sequence according to the initial state provided that they follow the rules established rules. Completion of this game using a heuristic method, using the Ascent hill Climbing algorithm. The working process of the Ascent hill Climbing method is a process of looking for several possible solutions in order to get the optimal value for solving the problem by arranging the values from the position of the smallest value to the position of the largest value. The problem that is often experienced in this case is a lack of user knowledge in the concept of puzzle game rules so that search results are difficult to find,with this method it can make it easier to solve puzzle game cases by following the game rules and done systematically so that Goals are quickly found. The Goal results obtained are in the form of steps in the process of finding a solution and calculating the time required in the search to find a solution.

scholarly journals Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3936
Author(s):  
Yannis Spyridis ◽  
Thomas Lagkas ◽  
Panagiotis Sarigiannidis ◽  
Vasileios Argyriou ◽  
Antonios Sarigiannidis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heuristic Method ◽  
Sensor Nodes ◽  
Convolutional Network ◽  
Received Signal Strength Indicator ◽  
Anchor Nodes ◽  
Time Required ◽  
Rf Signal ◽  
Deep Learning Model

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.

scholarly journals A Hybrid Flower Pollination with β-Hill Climbing Algorithm for Global Optimization

2021 ◽  
Author(s):  
Zaid Abdi Alkareem Alyasseri ◽  
Mohammed Azmi Al-Betar ◽  
Mohammed A. Awadallah ◽  
Sharif Naser Makhadmeh ◽  
Ammar Kamal Abasi ◽  
...  
Keyword(s):  
Global Optimization ◽  
Hill Climbing ◽  
Hill Climbing Algorithm ◽  
Flower Pollination

scholarly journals RMHC-MR: Instance selection by random mutation hill climbing algorithm with MapReduce in big data

2017 ◽  
Vol 111 ◽  
pp. 252-259 ◽  
Author(s):  
Lu Si ◽  
Jie Yu ◽  
Wuyang Wu ◽  
Jun Ma ◽  
Qingbo Wu ◽  
...  
Keyword(s):  
Big Data ◽  
Hill Climbing ◽  
Instance Selection ◽  
Random Mutation ◽  
Hill Climbing Algorithm

scholarly journals Development of Intelligent Drone Battery Charging System Based on Wireless Power Transmission Using Hill Climbing Algorithm

2018 ◽  
Vol 1 (4) ◽  
pp. 44 ◽  
Author(s):  
Ali Rohan ◽  
Mohammed Rabah ◽  
Muhammad Talha ◽  
Sung-Ho Kim
Keyword(s):  
Power Transmission ◽  
Inductive Coupling ◽  
Hill Climbing ◽  
Wireless Power ◽  
Wireless Power Transmission ◽  
Battery Charging ◽  
Charging System ◽  
Charging Station ◽  
Hill Climbing Algorithm ◽  
The Poor

In this work, an advanced drone battery charging system is developed. The system is composed of a drone charging station with multiple power transmitters and a receiver to charge the battery of a drone. A resonance inductive coupling-based wireless power transmission technique is used. With limits of wireless power transmission in inductive coupling, it is necessary that the coupling between a transmitter and receiver be strong for efficient power transmission; however, for a drone, it is normally hard to land it properly on a charging station or a charging device to get maximum coupling for efficient wireless power transmission. Normally, some physical sensors such as ultrasonic sensors and infrared sensors are used to align the transmitter and receiver for proper coupling and wireless power transmission; however, in this system, a novel method based on the hill climbing algorithm is proposed to control the coupling between the transmitter and a receiver without using any physical sensor. The feasibility of the proposed algorithm was checked using MATLAB. A practical test bench was developed for the system and several experiments were conducted under different scenarios. The system is fully automatic and gives 98.8% accuracy (achieved under different test scenarios) for mitigating the poor landing effect. Also, the efficiency η of 85% is achieved for wireless power transmission. The test results show that the proposed drone battery charging system is efficient enough to mitigate the coupling effect caused by the poor landing of the drone, with the possibility to land freely on the charging station without the worry of power transmission loss.

scholarly journals Hybrid of Hill Climbing and SAT Solving for Air Traffic Controller Shift Scheduling

2016 ◽  
Vol 10 (2) ◽  
Author(s):  
Mirko Stojadinović
Keyword(s):  
Heuristic Method ◽  
Optimal Solution ◽  
Air Traffic ◽  
Hill Climbing ◽  
Hybrid Technique ◽  
Sat Solving ◽  
Shift Scheduling ◽  
Exact Methods ◽  
Air Traffic Controller ◽  
Three Phases

Modern computers solve many problems by using exact methods, heuristic methods and very often by using their combination. Air Traffic Controller Shift Scheduling Problem has been successfully solved by using SAT technology (reduction to logical formulas) and several models of the problem exist. We present a technique for solving this problem that is a combination of SAT solving and meta-heuristic method hill climbing, and consists of three phases. First, SAT solver is used to generate feasible solution. Then, the hill climbing is used to improve this solution, in terms of number of satisfied wishes of controllers. Finally, SAT solving is used to further improve the found solution by fixing some parts of the solution. Three phases are repeated until optimal solution is found. Usage of exact method (SAT solving) guarantees that the found solution is optimal; usage of meta-heuristic (hill climbing) increases the efficiency in finding good solutions. By using these essentially different ways of solving, we aim to use the best from both worlds. Results indicate that this hybrid technique outperforms previously most efficient developed techniques.

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