Parallel Niche Genetic Algorithm for UAV Fleet Stealth Coverage 3D Corridors Real-Time Planning

2013 ◽  
Vol 846-847 ◽  
pp. 1189-1196
Author(s):  
Ping Chuan He ◽  
Shu Ling Dai
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Virtual Environments ◽  
Real Time ◽  
Hill Climbing ◽  
Mutation Operator ◽  
Hill Climbing Algorithm ◽  
Area Of Interest ◽  
Time Planning ◽  
Neighborhood Mutation

This paper presents a parallel improved niche genetic algorithm (PINGA) for 3D stealth coverage corridors real-time planning of unmanned aerial vehicles (UAVs) operating in a threat rich environment. 3D corridor was suggested to meet the diversity kinematics constraints of UAVs. Niche genetic algorithm (NGA) was improved by merging neighborhood mutation operator and hill climbing algorithm, and performed in parallel. Additionally, the crowding strategy based on high value targets was used to generate coverage trajectories in the area of interest (AOI). Preliminary results in virtual environments show that the approach for UAVs high quality flight corridors planning is real-time and effective.

scholarly journals Hierarchical System Decomposition Using Genetic Algorithm for Future Sustainable Computing

2020 ◽  
Vol 12 (6) ◽  
pp. 2177
Author(s):  
Jun-Ho Huh ◽  
Jimin Hwa ◽  
Yeong-Seok Seo
Keyword(s):  
Genetic Algorithm ◽  
Software Architecture ◽  
Large Scale ◽  
Structural Characteristics ◽  
Optimal Solution ◽  
Hill Climbing ◽  
Software Systems ◽  
Small Scale ◽  
Hill Climbing Algorithm ◽  
Architecture Management

A Hierarchical Subsystem Decomposition (HSD) is of great help in understanding large-scale software systems from the software architecture level. However, due to the lack of software architecture management, HSD documentations are often outdated, or they disappear in the course of repeated changes of a software system. Thus, in this paper, we propose a new approach for recovering HSD according to the intended design criteria based on a genetic algorithm to find an optimal solution. Experiments are performed to evaluate the proposed approach using two open source software systems with the 14 fitness functions of the genetic algorithm (GA). The HSDs recovered by our approach have different structural characteristics according to objectives. In the analysis on our GA operators, crossover contributes to a relatively large improvement in the early phase of a search. Mutation renders small-scale improvement in the whole search. Our GA is compared with a Hill-Climbing algorithm (HC) implemented by our GA operators. Although it is still in the primitive stage, our GA leads to higher-quality HSDs than HC. The experimental results indicate that the proposed approach delivers better performance than the existing approach.

Genetic Algorithm With Hill Climbing for Correspondences Discovery in Ontology Mapping

2019 ◽  
Vol 12 (4) ◽  
pp. 153-170 ◽  
Author(s):  
Guefrouchi Ryma ◽  
Kholladi Mohamed-Khireddine
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Ontology Mapping ◽  
Hill Climbing ◽  
Computational Time ◽  
Mutation Operator ◽  
Mapping Process ◽  
The Hill

Meta-heuristics are used as a tool for ontology mapping process in order to improve their performance in mapping quality and computational time. In this article, ontology mapping is resolved as an optimization problem. It aims at optimizing correspondences discovery between similar concepts of source and target ontologies. For better guiding and accelerating the concepts correspondences discovery, the article proposes a meta-heuristic hybridization which incorporates the Hill Climbing method within the mutation operator in the genetic algorithm. For test concerns, syntactic and lexical similarities are used to validate correspondences in candidate mappings. The obtained results show the effectiveness of the proposition for improving mapping performances in quality and computational time even for large OAEI ontologies.

scholarly journals Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4067 ◽  
Author(s):  
Fabio A. A. Andrade ◽  
Anthony Hovenburg ◽  
Luciano Netto de de Lima ◽  
Christopher Dahlin Rodin ◽  
Tor Arne Johansen ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Predictive Control ◽  
Kinematic Model ◽  
Search And Rescue ◽  
International Standards ◽  
Area Of Interest ◽  
Aerial Vehicles ◽  
Human Operators

Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place of a manned mission, especially because of its limitations on the costs, human resources, and mental and perception of the human operators. In this work, a real-time path-planning solution using multiple cooperative UAVs for SAR missions is proposed. The technique of Particle Swarm Optimization is used to solve a Model Predictive Control (MPC) problem that aims to perform search in a given area of interest, following the directive of international standards of SAR. A coordinated turn kinematic model for level flight in the presence of wind is included in the MPC. The solution is fully implemented to be embedded in the UAV on-board computer with DUNE, an on-board navigation software. The performance is evaluated using Ardupilot’s Software-In-The-Loop with JSBSim flight dynamics model simulations. Results show that, when employing three UAVs, the group reaches 50% Probability of Success 2.35 times faster than when a single UAV is employed.

scholarly journals Optimization of Airport Shuttle Bus Routes Based on Travel Time Reliability

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Danwen Bao ◽  
Jiayu Gu ◽  
Zhiwei Di ◽  
Tianxuan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Travel Time ◽  
Mutation Rate ◽  
Hybrid Genetic Algorithm ◽  
Estimation Method ◽  
Hill Climbing ◽  
Optimization Process ◽  
Travel Time Reliability ◽  
Hill Climbing Algorithm ◽  
Crossover Rate

An optimization model of airport shuttle bus routes is constructed by taking operational reliability maximization as a main goal in this paper. Also, a hybrid genetic algorithm is designed to solve this problem. Then the theoretical method is applied to the case of Nanjing Lukou International Airport. During the research, a travel time reliability estimation method is proposed based on back propagation (BP) neural network. Absolute error and regression fitting methods are used to test the measurement results. It is proved that this method has higher accuracy and is applicable to calculate airport bus routes reliability. In algorithm design, the hill-climbing algorithm with strong local search ability is integrated into genetic algorithm. Initial solution is determined by hill-climbing algorithm so as to avoid the search process falling into a local optimal solution, which makes the accuracy of calculation result improved. However, the calculation results show that the optimization process of hybrid genetic algorithm is greatly affected by both the crossover rate and mutation rate. A higher mutation rate or lower crossover rate will decrease the stability of the optimization process. Multiple trials are required to determine the optimal crossover rate and mutation rate. The proposed method provides a scientific basis for optimizing the airport bus routes and improving the efficiency of airport’s external transportation services.

The Optimization Method to Transfer Bus Routes for Rail Transit Terminal Station

2012 ◽  
Vol 253-255 ◽  
pp. 1869-1875
Author(s):  
Sheng Zhang ◽  
Wu Sheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Hill Climbing ◽  
Rail Transit ◽  
Hill Climbing Algorithm ◽  
Variation Rate ◽  
Encoding Method ◽  
The Hill ◽  
Terminal Station

The optimization model is framed with a goal to minimize overall consumption of travel time for passengers. A variety of constrains are considered, including time, capacity, stop number, profit and so on. According to the features of the model, the hill-climbing algorithm is adopted to obtain the initial solution, which reduces the time of optimization. Meanwhile, direct order encoding method, namely node method, is introduced for encoding, construct a Hybrid Genetic Algorithm for the solution. The results show that adapter value is more steady and the model result is preferable when the variation rate is increased while the number of iteration is decreased.

Application of a Hybrid Algorithm Based on Genetic Algorithm and Hill-Climbing Algorithm to Tool Path Optimization in CNC Machining

2010 ◽  
Vol 102-104 ◽  
pp. 681-685 ◽  
Author(s):  
Hai Qing Du ◽  
Ji Bao Qi
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Tool Path ◽  
Cnc Machining ◽  
Path Optimization ◽  
Hill Climbing ◽  
Tool Path Optimization ◽  
Tool Paths ◽  
Hill Climbing Algorithm ◽  
Accelerate Convergence

The efficiency of CNC machining is greatly influenced by the tool path. A new hybrid algorithm for tool path optimization in CNC varied-shape grinding is presented in this paper. The algorithm was constructed by adding hill-climbing algorithm to nature genetic algorithm. In the new algorithm, the crossover operator and mutation operator were redesigned to enhance the local search capability and to accelerate convergence. Verification experiment demonstrated that the algorithm can reduce non-cutting movement of tool paths and improve machining efficiency significantly.

Smooth gait optimization of a fish robot using the genetic-hill climbing algorithm

Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 257-278 ◽  
Author(s):  
Tuong Quan Vo ◽  
Hyoung Seok Kim ◽  
Byung Ryong Lee
Keyword(s):  
Genetic Algorithm ◽  
Hill Climbing ◽  
Optimal Parameters ◽  
Fish Robot ◽  
Hill Climbing Algorithm ◽  
Smooth Motion ◽  
Simulation Results ◽  
The Hill ◽  
Gait Optimization ◽  
Input Torque

SUMMARYThis paper presents a model of a three-joint (four links) carangiform fish robot. The smooth gait or smooth motion of a fish robot is optimized by using a combination of the Genetic Algorithm (GA) and the Hill Climbing Algorithm (HCA) with respect to its dynamic system. Genetic algorithm is used to create an initial set of optimal parameters for the two input torque functions of the system. This set is then optimized by using HCA to ensure that the final set of optimal parameters is a “near” global optimization result. Finally, the simulation results are presented in order to demonstrate that the proposed method is effective.

scholarly journals Simulated Real-Time Controller for Tuning Algorithm Using Modified Hill Climbing Approach Based on Model Reference Adaptive Control System

2020 ◽  
Author(s):  
Ahmed Abdulelah Ahmed ◽  
Azura Che Soh ◽  
Mohd Khair Hassan ◽  
Samsul Bahari Mohd Noor ◽  
Hafiz Rashidi Harun
Keyword(s):  
Adaptive Control ◽  
Real Time ◽  
Error Function ◽  
Model Reference Adaptive Control ◽  
Hill Climbing ◽  
System Response ◽  
Model Reference ◽  
Hill Climbing Algorithm ◽  
Processing Power ◽  
Model Reference Adaptive

In this chapter, an intelligent algorithmic tuning technique suitable for real-time system tuning based on hill climbing optimization algorithm and model reference adaptive control (MRAC) system technique is proposed. Although many adaptive control tuning methodologies depend partially or completely on online plant system identification, the proposed method uses only the model that is used to design the original controller, leading to simplified calculations that do not require neither high processing power nor long processing time, as opposed to identification technique calculations. Additionally, a modified hill climbing algorithm that is developed in this research is specifically designed, configured and tailored for the automatic tuning of control systems. The modified hill climbing algorithm uses a systematic movement when searching for new solution candidates. The algorithm measures the quality of the solution candidate based on error function. The error function is generated by comparing the system response with a desired reference response. The algorithm tests new solution candidates using step signals iteratively. The results showed the algorithm effectiveness to drive the system response. The simulation results illustrate that the method schemes proposed in this study show a viable and versatile solution to deal with controller tuning for systems with model inaccuracies as well as controller real-time calibration problem.

scholarly journals Simulated real time controller using modified hill climbing algorithm on fixed wing airplane

2015 ◽  
Author(s):  
A. Abdulelah ◽  
A. Che Soh ◽  
N. A. Abdullah ◽  
M. K. Hassan ◽  
S. B. Mohd Noor
Keyword(s):  
Real Time ◽  
Hill Climbing ◽  
Hill Climbing Algorithm
Sign in / Sign up

Export Citation Format

Share Document