Study on the real-time task assignment of multi-UCAV in uncertain environments based on genetic algorithm

2011 ◽  
Author(s):  
Xia Chen ◽  
Ting Tang ◽  
Lin Li
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Task Assignment ◽  
Uncertain Environments ◽  
The Real ◽  
Time Task

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 heuristic based real time task assignment algorithm for heterogeneous multiprocessors

2014 ◽  
Vol 11 (3) ◽  
pp. 20130975-20130975 ◽  
Author(s):  
M Poongothai ◽  
A Rajeswari ◽  
V Kanishkan
Keyword(s):  
Real Time ◽  
Task Assignment ◽  
Heterogeneous Multiprocessors ◽  
Time Task ◽  
Assignment Algorithm

Research on Improved Genetic Algorithm

2014 ◽  
Vol 635-637 ◽  
pp. 1760-1763
Author(s):  
Xiao Yu Wang ◽  
Yong Hui Yang ◽  
Shuo Li ◽  
Chuang Gao
Keyword(s):  
Genetic Algorithm ◽  
Embedded System ◽  
Real Time ◽  
Cost Reduction ◽  
Function Optimization ◽  
Improved Genetic Algorithm ◽  
The Real ◽  
Crossover Rate ◽  
Time Requirements ◽  
Improved Algorithm

An improved genetic algorithm for the function optimization of multi-core embedded system is proposed. A number of chromosomes that distribute uniformly in space are generated by the algorithm randomly. Each chromosome is randomly coded and a new one will be generated by mutual calculation. After continuous elimination and circulation, the optimized chromosomes can be selected. The improved algorithm makes the mutation offspring have the opportunity to be the next parent with the increase of mutation. It enhances the parent diversity, increases the crossover rate, activates crossover between the parents and has chance to access to the best solution. The efficiency and cost reduction performance are improved. The different tasks will be distributed in parallel to available processors so as to meet the real-time requirements.

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