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Adoption of computer particle swarm optimization algorithm under thermodynamic motion mechanism

ABSTRACT
In order to improve the stability and sensitivity of particle swarm optimization algorithm and to solve the problem of premature convergence, in this research, a computer particle swarm optimization algorithm based on thermodynamic motion mechanism is proposed based on the principle of thermodynamic motion mechanism. Firstly, the thermodynamic motion phenomenon, the diffusion law in kinematics and the standard particle swarm optimization algorithm are introduced. Then, according to the basic idea of thermodynamic motion mechanism, the standardized particle swarm optimization algorithm is optimized and its optimization process is introduced. Finally, the experimental results are analyzed by setting the test function. The results show that among the five test functions, the computer particle swarm optimization algorithm based on thermodynamic motion mechanism has a higher probability of jumping out of the local optimal solution. Its robustness and stability are much better than standard particle swarm optimization algorithms. The evolution ability of the computer particle swarm optimization algorithm based on thermodynamic motion mechanism is better than that of the standard particle swarm optimization algorithm. The standard particle swarm optimization algorithm is superior because it is based on thermodynamic motion mechanism. The research in this paper can provide good guidance for improving the performance of particle swarm optimization algorithm.
KEYWORDS
PAPER SUBMITTED: 2019-09-21
PAPER REVISED: 2019-11-05
PAPER ACCEPTED: 2019-11-25
PUBLISHED ONLINE: 2020-01-19
DOI REFERENCE: https://doi.org/10.2298/TSCI190921005L
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