FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem（SLAM）. However, in this framework there are two important potential limitatio...FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem（SLAM）. However, in this framework there are two important potential limitations, the particle depletion problem and the linear approximations of the nonlinear functions. To overcome these two drawbacks, this paper proposes a new FastSLAM algorithm based on revised genetic resampling and square root unscented particle filter（SR-UPF）. Double roulette wheels as the selection operator, and fast Metropolis-Hastings（MH） as the mutation operator and traditional crossover are combined to form a new resampling method. Amending the particle degeneracy and keeping the particle diversity are both taken into considerations in this method. As SR-UPF propagates the sigma points through the true nonlinearity, it decreases the linearization errors. By directly transferring the square root of the state covariance matrix, SR-UPF has better numerical stability. Both simulation and experimental results demonstrate that the proposed algorithm can improve the diversity of particles, and perform well on estimation accuracy and consistency.展开更多
The mechanism of particle swarm optimization algorithm is studied, and one can draw the conclusion that the best particle found by the swarm falling into local minima is one of the main reasons for premature convergen...The mechanism of particle swarm optimization algorithm is studied, and one can draw the conclusion that the best particle found by the swarm falling into local minima is one of the main reasons for premature convergence. Therefore, an improved particle swarm optimization algorithm is proposed. This algorithm selects the best particle with roulette wheel selection method, so premature converging to local optima is avoided. At last, the improved particle swarm optimization algorithm is applied to optimization of time-sharing power supply for zinc electrolytic process. Simulation and practical results show that the global search ability of IPSO is improved greatly and optimization of time-sharing power supply for zinc electrolytic process can bring about outstanding economic benefit for plant.展开更多
Purpose-Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism.This research aims to propose the optimization of makespan,energy ...Purpose-Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism.This research aims to propose the optimization of makespan,energy consumption and data transfer time(DTT)by considering the priority tasks.The research work is concentrated on the multi-objective approach based on the genetic algorithm(GA)and energy aware model to increase the efficiency of the task scheduling.Design/methodology/approach-Cloud computing is the recent advancement of the distributed and cluster computing.Cloud computing offers different services to the clients based on their requirements,and it works on the environment of virtualization.Cloud environment contains the number of data centers which are distributed geographically.Major challenges faced by the cloud environment are energy consumption of the data centers.Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan.This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm.This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines.The energy model is developed for picking the task based on the fitness function.The simulation results show the performance of the multi-objective model with respect to makespan,DTT and energy consumption.Findings-The energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine.The directed acyclic graph is used to represent the task dependencies.The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms.The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.Originality/value-This paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm.The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing.The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection.The energy model is used as fitness function to theGAfor selecting the tasks to perform the scheduling.展开更多
基金supported by National Natural Science Foundation of China(No.61101197)Research Fund for the Doctoral Program of Higher Education of China(No.20093219120025)
文摘FastSLAM is a popular framework which uses a Rao-Blackwellized particle filter to solve the simultaneous localization and mapping problem（SLAM）. However, in this framework there are two important potential limitations, the particle depletion problem and the linear approximations of the nonlinear functions. To overcome these two drawbacks, this paper proposes a new FastSLAM algorithm based on revised genetic resampling and square root unscented particle filter（SR-UPF）. Double roulette wheels as the selection operator, and fast Metropolis-Hastings（MH） as the mutation operator and traditional crossover are combined to form a new resampling method. Amending the particle degeneracy and keeping the particle diversity are both taken into considerations in this method. As SR-UPF propagates the sigma points through the true nonlinearity, it decreases the linearization errors. By directly transferring the square root of the state covariance matrix, SR-UPF has better numerical stability. Both simulation and experimental results demonstrate that the proposed algorithm can improve the diversity of particles, and perform well on estimation accuracy and consistency.
文摘The mechanism of particle swarm optimization algorithm is studied, and one can draw the conclusion that the best particle found by the swarm falling into local minima is one of the main reasons for premature convergence. Therefore, an improved particle swarm optimization algorithm is proposed. This algorithm selects the best particle with roulette wheel selection method, so premature converging to local optima is avoided. At last, the improved particle swarm optimization algorithm is applied to optimization of time-sharing power supply for zinc electrolytic process. Simulation and practical results show that the global search ability of IPSO is improved greatly and optimization of time-sharing power supply for zinc electrolytic process can bring about outstanding economic benefit for plant.
文摘Purpose-Majority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism.This research aims to propose the optimization of makespan,energy consumption and data transfer time(DTT)by considering the priority tasks.The research work is concentrated on the multi-objective approach based on the genetic algorithm(GA)and energy aware model to increase the efficiency of the task scheduling.Design/methodology/approach-Cloud computing is the recent advancement of the distributed and cluster computing.Cloud computing offers different services to the clients based on their requirements,and it works on the environment of virtualization.Cloud environment contains the number of data centers which are distributed geographically.Major challenges faced by the cloud environment are energy consumption of the data centers.Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan.This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm.This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines.The energy model is developed for picking the task based on the fitness function.The simulation results show the performance of the multi-objective model with respect to makespan,DTT and energy consumption.Findings-The energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine.The directed acyclic graph is used to represent the task dependencies.The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms.The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.Originality/value-This paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm.The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing.The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection.The energy model is used as fitness function to theGAfor selecting the tasks to perform the scheduling.
