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Method for parallel execution of particle swarm optimization algorithm on multiple computers

A particle swarm algorithm and computer technology, applied in the direction of concurrent instruction execution, machine execution device, calculation, etc., can solve the problems of long calculation time, reduce the practical application value of particle swarm algorithm, and low calculation efficiency, so as to reduce calculation time consumption, The effect of improving parallel computing efficiency and increasing complexity

Inactive Publication Date: 2010-09-01
ZHEJIANG UNIV
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Problems solved by technology

[0004] However, when optimizing complex problems, such as training a large-scale neural network, as the number of neurons increases, the number of parameters to be optimized also increases, resulting in a sharp increase in the search space for evolutionary computing. Running on a single CPU usually requires Very long calculation time, low calculation efficiency, and sometimes even several days of running time, which greatly reduces the practical application value of the particle swarm optimization algorithm

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  • Method for parallel execution of particle swarm optimization algorithm on multiple computers
  • Method for parallel execution of particle swarm optimization algorithm on multiple computers
  • Method for parallel execution of particle swarm optimization algorithm on multiple computers

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[0033] The specific operation steps of the particle swarm optimization parallel acceleration platform are as follows:

[0034] The first step: Mining parallel program segments.

[0035] The calculation process of particle swarm algorithm is as follows: figure 1 . During the calculation process of mining, the calculation of different particle data and the code segments that adjust them, the host makes a mark before running the program. The principles that need to be marked for parallel acceleration operations are: a large number of repeated calculations; a large number of unrelated loops; unrelated calculation systems; multiplication of matrix calculations, etc. By observing figure 1 , the two steps of calculating the fitness value of each individual and adjusting the speed of each individual can be parallelized, and the result after parallelization is as follows figure 2 .

[0036] Step 2: Build OpenMP+MPI local interface.

[0037] MPI is a universal interface based on ...

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Abstract

The invention discloses a method for parallel execution of particle swarm optimization algorithm on multiple computers. The method comprises the initialization step, the evaluation and adjustment step, the step of judging termination conditions and the termination and output step, wherein the evaluation and adjustment is the part for realizing parallel computation through parallel programming of MPI plus OpenMP. The method carries out parallelization on the operations of updating particles and evaluating particles in the particle swarm optimization algorithm by combining with the existing MPI plus OpenMP multi-core programming method according to the independence before and after updating the particle swarm optimization algorithm. The invention adopts a master-slave parallel programming mode for solving the problem of too slow speed of running the particle swarm optimization algorithm on the single computer in the past and accelerating the speed of the particle swarm optimization algorithm, thereby greatly expanding the application value and the application field of the particle swarm optimization algorithm.

Description

technical field [0001] The invention relates to the particle swarm algorithm technology of parallel computing, in particular to a method for parallel execution of the particle swarm algorithm on multiple machines. Background technique [0002] With the development of society, people need to deal with more and more complex problems, such as the optimization problems often encountered in the process of industrial control. These problems cannot be optimally solved by mathematical methods. At the same time, due to the huge scale and the complexity rising at a factorial speed, exhaustive methods are also infeasible. Inspired by nature, people have found out from the laws of nature. Many ways to solve practical problems, these methods are called heuristic algorithms (heuristic algorithm). In recent years, evolutionary algorithm, ant colony algorithm, anthropomorphic algorithm and particle swarm algorithm have emerged one after another, setting off a climax of research on heuristi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00G06F9/38G06F9/50
Inventor 陈天洲袁辉施青松胡威蒋冠军李敬贤
Owner ZHEJIANG UNIV
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