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Particle swarm optimization method and system integrated with reverse learning and heuristic perception

A technology of particle swarm optimization and reverse learning, applied in biological models, program startup/switching, resource allocation, etc., can solve problems such as poor results, achieve the effect of improving quality and enhancing local optimization capabilities

Active Publication Date: 2019-10-01
CHANGSHA UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] It is very sensitive to the initial particle swarm, and the result of task scheduling has a strong dependence on the quality of the initial population in the algorithm. When the quality of the initial particle swarm generated by the random method is better, the result is better. Otherwise, you will get the result is bad

Method used

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  • Particle swarm optimization method and system integrated with reverse learning and heuristic perception
  • Particle swarm optimization method and system integrated with reverse learning and heuristic perception
  • Particle swarm optimization method and system integrated with reverse learning and heuristic perception

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Embodiment 1

[0041] Such as figure 1 As shown, this embodiment provides a particle swarm optimization method incorporating reverse learning and heuristic perception, including the following steps:

[0042] S1: Initialize primitive particles with a scale of n, and form primitive groups according to primitive particles;

[0043] S2: Use the reverse learning method to generate counter particles of n original particles, select the better one from the original particles and counter particles, and update the original group to obtain the initial particle swarm;

[0044] S4: Generate q probes around each particle in the initial particle swarm for sensing whether there is a better position than the current particle, and optimize each particle in the initial particle swarm according to the probe to obtain the optimal particle swarm.

[0045] The above-mentioned particle swarm optimization method incorporating inverse learning and heuristic perception uses the inverse learning strategy to optimize the initia...

Embodiment 2

[0080] Experimental verification. In this embodiment, experiments are conducted to verify the optimization effect of the particle swarm optimization method incorporating inverse learning and heuristic perception in the foregoing embodiment 1.

[0081] This experiment uses CloudeSim 3.3.2 as the cloud computing simulation platform environment. The test environment of the experiment is Intel(R)Core(TM)i5 Dual-Core 3.4GHz, memory 4GB, operating system is windows7, java virtual machine is jdk1.8 version. The particle swarm optimization method (OBL-TP-PSO) proposed by the present invention integrated with reverse learning and heuristic perception is compared and analyzed with Min-Min algorithm, Max-Min algorithm and particle swarm algorithm (PSO).

[0082] After adjusting the parameters of PSO several times, it is found that when w=0.5 and c1=c2=1, PSO can obtain a more accurate solution faster. At this time, set the parameters of the BL-TP-PSO algorithm to be consistent with the para...

Embodiment 3

[0103] Corresponding to the foregoing method embodiments, this embodiment provides a particle swarm optimization system incorporating back learning and heuristic perception, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the above program, the steps of the above method are realized.

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Abstract

The invention relates to the field of cloud computing task scheduling, and discloses a particle swarm optimization method and system integrated with reverse learning and heuristic perception, so as torealize better optimization of a particle swarm and further enhance the local optimization capability of an algorithm. The method comprises: initializing original particles with the scale being n, and forming an original group according to the original particles; generating reverse particles of the n original particles by adopting a reverse learning method, selecting a better one from the original particles and the reverse particles, and updating the original group to obtain an initial particle swarm; sequentially generating q probes used for sensing whether the position is better than that of the current particle or not around each particle in the initial particle swarm, and optimizing each particle in the initial particle swarm according to the probes to obtain an optimal particle swarm.

Description

Technical field [0001] The invention relates to the field of cloud computing task scheduling, in particular to a particle swarm optimization method and system incorporating reverse learning and trial perception. Background technique [0002] The main idea of ​​cloud computing is to use grid computing, distributed computing, parallel processing and other related technologies to fuse heterogeneous computer network resources scattered in various places into a huge computer resource pool for users to use on demand. After the user submits tasks to the computer resource pool, these tasks are systematically allocated to each computer resource in the resource pool, and its powerful computing power is used to process the tasks and return the results to the users, so that people can use lower cost Invest in higher computing power. In the cloud computing environment, task scheduling is one of the very important modules. The result of task scheduling directly affects the overall computing ...

Claims

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Application Information

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IPC IPC(8): G06F9/48G06F9/50G06N3/00
CPCG06F9/4843G06F9/5083G06N3/006
Inventor 周舟李方敏
Owner CHANGSHA UNIVERSITY
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