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A Particle Swarm Optimization Method and System Integrating Reverse Learning and Heuristic Perception

A particle swarm optimization and reverse learning technology, applied in biological models, multi-programming devices, instruments, etc., can solve problems such as poor results, and achieve the effect of improving quality and enhancing local optimization capabilities.

Active Publication Date: 2020-06-19
CHANGSHA UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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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  • A Particle Swarm Optimization Method and System Integrating Reverse Learning and Heuristic Perception
  • A Particle Swarm Optimization Method and System Integrating Reverse Learning and Heuristic Perception
  • A Particle Swarm Optimization Method and System Integrating 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 that incorporates reverse learning and heuristic perception, including the following steps:

[0042] S1: Initialize the original particles with a scale of n, and form the original group according to the original particles;

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

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

[0045] The above-mentioned particle swarm optimization method that incorporates reverse learning and trial perception uses reverse learning strategies t...

Embodiment 2

[0080] Experimental verification. In this embodiment, the optimization effect of the particle swarm optimization method incorporating reverse learning and heuristic perception in the above-mentioned embodiment 1 is verified through experiments.

[0081] This experiment uses CloudeSim3.3.2 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 version jdk1.8. The particle swarm optimization method (OBL-TP-PSO) proposed by the present invention, which is integrated into reverse learning and trial perception, is compared 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, c1=c2=1, PSO can obtain a more accurate solution faster. At this time, the parameters of the BL-TP-PSO algorithm are set to be consistent with the p...

Embodiment 3

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

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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 related technologies such as grid computing, distributed computing and parallel processing to integrate 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 assigned to each computer resource in the resource pool, and the results are returned to the user after the task is processed and executed by using its powerful computing power, so that people can use lower cost Invest in higher computing power. In cloud computing environment, task scheduling is one of the very important modules. The quality of task scheduling results di...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/48G06F9/50G06N3/00
CPCG06F9/4843G06F9/5083G06N3/006
Inventor 周舟李方敏
Owner CHANGSHA UNIVERSITY
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