Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning

2013 ◽  
Vol 9 (1) ◽  
pp. 132-141 ◽  
Author(s):  
Vincent Roberge ◽  
Mohammed Tarbouchi ◽  
Gilles Labonte
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Path Planning ◽  
Real Time ◽  
Particle Swarm ◽  
Parallel Genetic Algorithm ◽  
Swarm Optimization

A Comparison of Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing in Real-Time Task Scheduling on CMP

2013 ◽  
Vol 679 ◽  
pp. 77-81 ◽  
Author(s):  
Song Chai ◽  
Yu Bai Li ◽  
Chang Wu ◽  
Jian Wang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Real Time ◽  
Schedule Problem ◽  
Particle Swarm ◽  
Full Potential ◽  
Swarm Optimization ◽  
Task Schedule ◽  
Time Task

Real-time task schedule problem in Chip-Multiprocessor (CMP) receives wide attention in recent years. It is partly because the increasing demand for CMP solutions call for better schedule algorithm to exploit the full potential of hardware, and partly because of the complexity of schedule problem, which itself is an NP-hard problem. To address this task schedule problem, various of heuristics have been studied, among which, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) are the most popular ones. In this paper, we implement these 3 schedule heuristics, and compare their performance under the context of real-time tasks scheduling on CMP. According to the results of our intensive simulations, PSO has the best fitness optimization of these 3 algorithms, and SA is the most efficient algorithm.

scholarly journals A Real-Time Path Planning Algorithm for AUV in Unknown Underwater Environment Based on Combining PSO and Waypoint Guidance

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 20 ◽  
Author(s):  
Zheping Yan ◽  
Jiyun Li ◽  
Yi Wu ◽  
Gengshi Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Time Path ◽  
Optimization Parameters ◽  
Planning Algorithm

It is a challengeable task to plan multi-objective optimization paths for autonomous underwater vehicles (AUVs) in an unknown environments, which involves reducing travel time, shortening path length, keeping navigation safety, and smoothing trajectory. To address the above challenges, a real-time path planning approach combining particle swarm optimization and waypoint guidance is proposed for AUV in unknown oceanic environments in this paper. In this algorithm, a multi-beam forward looking sonar (FLS) is utilized to detect obstacles and the output data of FLS are used to produce those obstacles’ outlines (polygons). Particle swarm optimization is used to search for appropriate temporary waypoints, in which the optimization parameters of path planning are taken into account. Subsequently, an optimal path is automatically generated under the guidance of the destination and these temporary waypoints. Finally, three algorithms, including artificial potential field and genic algorithm, are adopted in the simulation experiments. The simulation results show that the proposed algorithm can generate the optimal paths compared with the other two algorithms.

Comparative Study of Path Planning by Particle Swarm Optimization and Genetic Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 1420-1424
Author(s):  
Hai Tao Han ◽  
Wan Feng Ji ◽  
Yao Qing Zhang ◽  
De Peng Sha
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Performance ◽  
Operational Environment ◽  
Swarm Optimization ◽  
Vehicle Path ◽  
Swarm Intelligence Algorithm

Two main requirements of the optimization problems are included: one is finding the global minimum, the other is obtaining fast convergence speed. As heuristic algorithm and swarm intelligence algorithm, both particle swarm optimization and genetic algorithm are widely used in vehicle path planning because of their favorable search performance. This paper analyzes the characteristics and the same and different points of two algorithms as well as making simulation experiment under the same operational environment and threat states space. The result shows that particle swarm optimization is superior to genetic algorithm in searching speed and convergence.

Vehicle Path Planning Method Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 468-471 ◽  
pp. 2745-2748
Author(s):  
Sheng Long Yu ◽  
Yu Ming Bo ◽  
Zhi Min Chen ◽  
Kai Zhu
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Swarm Optimization ◽  
Vehicle Path

A particle swarm optimization algorithm (PSO) is presented for vehicle path planning in the paper. Particle swarm optimization proposed by Kennedy and Eberhart is derived from the social behavior of the birds foraging. Particle swarm optimization algorithm a kind of swarm-based optimization method.The simulation experiments performed in this study show the better vehicle path planning ability of PSO than that of adaptive genetic algorithm and genetic algorithm. The experimental results show that the vehicle path planning by using PSO algorithm has the least cost and it is indicated that PSO algorithm has more excellent vehicle path planning ability than adaptive genetic algorithm,genetic algorithm.

scholarly journals Research on performance seeking control based on Beetle Antennae Search algorithm

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1440-1445
Author(s):  
Qiangang Zheng ◽  
Dewei Xiang ◽  
Juan Fang ◽  
Yong Wang ◽  
Haibo Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Real Time ◽  
Control Method ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Engine Performance ◽  
Inlet Temperature ◽  
Swarm Optimization ◽  
Time Performance

A novel performance seeking control) method based on Beetle Antennae Search algorithm is proposed to improve the real-time performance of performance seeking control. The Beetle Antennae Search imitates the function of antennae of beetle. The Beetle Antennae Search has better real-time performance because of the objective function only calculated twice in Beetle Antennae Search at each iteration. Moreover, the Beetle Antennae Search has global search ability. The performance seeking control simulations based on Beetle Antennae Search, Genetic Algorithm and particle swarm optimization are carried out. The simulations show that the Beetle Antennae Search has much better real-time performance than the conventional probability-based algorithms Genetic Algorithm and particle swarm optimization. The simulations also show that these three probability-based algorithms can get better engine performance, such as more thrust, less specific fuel consumption and less turbine inlet temperature.

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