Novel efficient particle swarm optimization algorithms for solving QoS-demanded bag-of-tasks scheduling problems with profit maximization on hybrid clouds

2017 ◽  
Vol 29 (21) ◽  
pp. e4249 ◽  
Author(s):  
Yi Zhang ◽  
Jin Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithms ◽  
Profit Maximization ◽  
Particle Swarm ◽  
Scheduling Problems ◽  
Swarm Optimization ◽  
Hybrid Clouds ◽  
Tasks Scheduling

scholarly journals Constrained path planning of autonomous underwater vehicle using selectively-hybridized particle swarm optimization algorithms

2019 ◽  
Vol 52 (21) ◽  
pp. 315-322 ◽  
Author(s):  
Hui Sheng Lim ◽  
Shuangshuang Fan ◽  
Christopher K.H. Chin ◽  
Shuhong Chai ◽  
Neil Bose ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Underwater Vehicle ◽  
Swarm Optimization

scholarly journals Anti-synchronization of discrete-time chaotic systems using optimization algorithms

2011 ◽  
pp. 143-147
Author(s):  
Mahdieh Adeli ◽  
Hassan Zarabadipoor
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Control ◽  
Pid Controller ◽  
Chaotic Systems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Modified Particle Swarm Optimization

In this paper, anti-synchronization of discrete chaotic system based on optimization algorithms are investigated. Different controllers have been used for anti-synchronization of two identical discrete chaotic systems. A proportional-integral-derivative (PID) control is used and its parameters is tuned by the four optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), modified particle swarm optimization (MPSO) and improved particle swarm optimization (IPSO). Simulation results of these optimization methods to determine the PID controller parameters to anti-synchronization of two chaotic systems are compared. Numerical results show that the improved particle swarm optimization has the best result.

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