03-05-2017, 07:11 AM
Would like to access the binary particle swarm optimization Mat-lab code for energy load balancing please.
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discrete particle swarm optimization matlab code
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Would like to access the binary particle swarm optimization Mat-lab code for energy load balancing please.
03-05-2017, 12:19 PM
Programming is one of the fundamental steps to efficiently exploit the capabilities of emerging computer systems such as the network. The grid environment is dynamic, heterogeneous and unpredictable sharing different services among many different users. Due to the heterogeneous and dynamic nature of the network, the methods used in traditional systems could not be applied to grid programming and therefore new methods should be sought. This work represents a discrete approach to particle swarm optimization (DPSO) for scheduling network jobs.
PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and schooling of fish. Particles fly into the problem-finding space to find optimal or near optimal solutions. In this work, the programmer aims to minimize makespan and flowtime simultaneously in the grid environment. Experimental studies illustrate that the proposed method is more efficient and outperforms the metaheuristic algorithms reported for this problem.
As a new evolutionary technique, particle swarm optimization (PSO) has received increasing attention and wide applications in a variety of fields. As far as we know, this work investigates the first application of the PSO algorithm to address the problem of parallel machine programming. By proposing equations analogous to those of the classical PSO equations, a discrete PSO algorithm (DPSO) is presented to minimize the makespan (Cmax) criterion.
The effectiveness of the DPSO algorithm through hybridization with an efficient local search heuristic. To verify the performance of the DPSO algorithm and its hybridized version (HDPSO), comparisons are made using a simulation annealing algorithm recently proposed for the problem, treated in the literature, as a comparison algorithm. Computational results mean that the proposed DPSO algorithm is very competitive and can be quickly guided when it is hybridized to a local search heuristic.
PSO is a population-based search algorithm based on the simulation of the social behavior of bird flocking and schooling of fish. Particles fly into the problem-finding space to find optimal or near optimal solutions. In this work, the programmer aims to minimize makespan and flowtime simultaneously in the grid environment. Experimental studies illustrate that the proposed method is more efficient and outperforms the metaheuristic algorithms reported for this problem.
As a new evolutionary technique, particle swarm optimization (PSO) has received increasing attention and wide applications in a variety of fields. As far as we know, this work investigates the first application of the PSO algorithm to address the problem of parallel machine programming. By proposing equations analogous to those of the classical PSO equations, a discrete PSO algorithm (DPSO) is presented to minimize the makespan (Cmax) criterion.
The effectiveness of the DPSO algorithm through hybridization with an efficient local search heuristic. To verify the performance of the DPSO algorithm and its hybridized version (HDPSO), comparisons are made using a simulation annealing algorithm recently proposed for the problem, treated in the literature, as a comparison algorithm. Computational results mean that the proposed DPSO algorithm is very competitive and can be quickly guided when it is hybridized to a local search heuristic.
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