Parallel asynchronous particle swarm optimization method and system and electronic equipment

A particle swarm optimization and particle technology, which is applied in design optimization/simulation, instruments, calculation models, etc., can solve problems such as large amount of calculation, low optimization ability, and poor robustness, so as to improve calculation efficiency, reduce calculation load, The effect of improving the optimization performance and robustness

Active Publication Date: 2021-06-11
SHANGHAI JIAO TONG UNIV
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  • Claims
  • Application Information

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Problems solved by technology

[0005] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a parallel asynchronous particle swarm optimization method, system a

Method used

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  • Parallel asynchronous particle swarm optimization method and system and electronic equipment
  • Parallel asynchronous particle swarm optimization method and system and electronic equipment
  • Parallel asynchronous particle swarm optimization method and system and electronic equipment

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Experimental program
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Embodiment 1

[0046] Specifically, as figure 1 As shown, this embodiment provides a parallel asynchronous particle swarm optimization method, the parallel asynchronous particle swarm optimization method includes:

[0047] Step S100, establishing a fitness function for the target to be optimized to measure the decision variable.

[0048] Step S200, grouping the particle clusters, and randomly initializing the initial position, optimal value and optimization parameters of the diversity of the particles in each particle group;

[0049] Step S300, establishing an information sharing mechanism for each particle group, for sharing the optimal value of each particle group;

[0050] Step S400, arrange each particle group on different CPU cores for distributed parallel iterative calculation, and asynchronously update the historical optimal value of the particle group according to the information sharing mechanism;

[0051] Step S500, when the global iteration number is greater than or equal to the...

Embodiment 2

[0099] Such as Image 6 As shown, this embodiment provides a parallel asynchronous particle swarm optimization system 100, the parallel asynchronous particle swarm optimization system 100 includes: a function establishment module 110, an initialization module 120, an information sharing module 130, an iterative calculation module 140 and a result output module 140.

[0100] In this embodiment, the function establishment module 110 is used to establish a fitness function for the target to be optimized, which is used to measure the decision variable;

[0101] In this embodiment, the initialization module 120 is used to group the particle clusters, and randomly initialize the initial position, optimal value and optimization parameters of the diversity of the particles in each particle group;

[0102] The optimal value includes: the historical optimal value of each particle, the historical optimal value of each particle group, and the historical optimal value of particle clusters...

Embodiment 3

[0115] Such as Figure 8 As shown, this embodiment also provides an electronic device 10, which is, but not limited to, a smart phone, a tablet, a smart wearable device, a personal desktop computer, a notebook computer, a server, a server cluster, and the like.

[0116] The electronic device 10 includes a memory 102 for storing a computer program; a processor 101 for running the computer program to implement the steps of the parallel asynchronous particle swarm optimization method as described in Embodiment 1.

[0117] The memory 102 is connected to the processor 101 through the device bus and completes mutual communication, the memory 102 is used to store computer programs, and the processor 101 is used to run the computer programs, so that the electronic device 10 executes the parallel asynchronous particle swarm optimization method. The parallel asynchronous particle swarm optimization method has been described in Embodiment 1, and will not be repeated here.

[0118]It sh...

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Abstract

The invention provides a parallel asynchronous particle swarm optimization method and system and electronic equipment, and the method comprises the steps: building a fitness function for a to-be-optimized target, and enabling the fitness function to be used for measuring a decision variable; grouping the particle groups, and randomly initializing initial positions, optimal values and diversity optimization parameters of particles in each particle group; establishing an information sharing mechanism for each particle group, wherein the information sharing mechanism is used for sharing the optimal value of each particle group; arranging the particle groups on different CPU cores for distributed parallel iterative calculation, and asynchronously updating historical optimal values of the particle groups according to an information sharing mechanism; and when the global number of iterations is greater than or equal to the threshold value of the number of iterations, ending the iterative update of each particle group, and outputting the optimal value of the particle group as a final optimization result. Compared with a traditional particle swarm algorithm, the method has the advantages that the optimization performance and robustness of the algorithm are improved, the calculation amount of the algorithm is reduced, the operation efficiency of the algorithm is improved, and the method can be suitable for various complex optimization scenes.

Description

technical field [0001] The invention relates to the field of optimization, in particular to the field of optimization of non-convex, non-continuous and non-derivable functions. Background technique [0002] Optimization technology is crucial to the development of various fields of society. For example, in the field of engine design, through the optimization design of various parts of the engine, the efficiency of the engine can be made higher and the emission lower; Optimizing the power output combination of multiple generators can improve energy utilization and increase economic benefits. [0003] Optimization techniques can be mainly divided into the following two types: optimization algorithms based on gradient information, such as stochastic gradient descent algorithm; intelligent optimization algorithms not based on gradient information, such as particle swarm optimization algorithm, genetic algorithm, simulated annealing algorithm, etc. Optimization algorithms based o...

Claims

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Application Information

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IPC IPC(8): G06F30/25G06N3/00
CPCG06F30/25G06N3/006
Inventor 辛靖豪于丽英李柠
Owner SHANGHAI JIAO TONG UNIV
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