## THERMAL SCIENCE

International Scientific Journal

### ADOPTION OF COMPUTER PARTICLE SWARM OPTIMIZATION ALGORITHM UNDER THERMODYNAMIC MOTION MECHANISM

**ABSTRACT**

In order to improve the stability and sensitivity of particle swarm optimization (PSO) algorithm and to solve the problem of premature convergence, in this re-search, a computer PSO 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 PSO algorithm are introduced. Then, according to the basic idea of thermodynamic motion mechanism, the standardized PSO algorithm is optimized and its optimization process is introduced. Finally, the experimental results are analysed by setting the test function. The results show that among the five test functions, the computer PSO 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 PSO algorithms. The evolution ability of the computer PSO algorithm based on thermodynamic motion mechanism is better than that of the standard PSO algorithm. The standard PSO 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 PSO algorithm.

**KEYWORDS**

PAPER SUBMITTED: 2019-09-21

PAPER REVISED: 2019-11-05

PAPER ACCEPTED: 2019-11-25

PUBLISHED ONLINE: 2020-01-19

**THERMAL SCIENCE** YEAR

**2020**, VOLUME

**24**, ISSUE

**Issue 5**, PAGES [2707 - 2715]

- Singh, H., et al., A New Meta-Heuristic Algorithm Based on Chemical Reactions for Partitional Clustering Problems, Evolutionary Intelligence, 12 (2019), 2, pp. 241-252.
- Song, S., et al., Adoption of an improved PSO to explore a compound multi-objective energy function in protein structure prediction, Applied Soft Computing, 72 (2018), pp. 539-551.
- Furman, D., et al., Enhanced Particle Swarm Optimization Algorithm: Efficient Training of Reaxff Reactive Force Fields, Journal of chemical theory and computation, 14 (2018), pp. 3100-3112.
- Haznedar, B., Kalinli, A., Training ANFIS Structure Using Simulated Annealing Algorithm for Dynamic Systems Identification, Neurocomputing, 302 (2018), pp. 66-74.
- Pradeepmon, T., et al., Development of Modified Discrete Particle Swarm Optimization Algorithm for Quadratic Assignment Problems, International Journal of Industrial Engineering Computations, 9 (2018), 4, pp. 491-508.
- Arjun, Sharma., et al., Finite time thermodynamic analysis and optimization of solar-dish stirling heat engine with regenerative losses, Thermal Science, 15 (2018), 4, pp. 995-1009.
- Guha, D., et al., Application of Backtracking Search Algorithm in Load Frequency Control of Multi-Area Interconnected Power System, Ain Shams Engineering Journal, 9 (2018), 2, pp. 257-276.
- Arunarani, A. R., et al., Task scheduling techniques in cloud computing: A literature survey, Future Generation Computer Systems, 91 (2019), pp. 407-415.
- Deng, W., et al., An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem, IEEE access, 7 (2019), pp. 20281-20292.
- Liu, Y., et al., Parameter optimization of Depressurization-to-Hot-Water-Flooding in Heterogeneous Hydrate Bearing Layers Based on The Particle Swarm Optimization Algorithm, Journal of Natural Gas Science and Engineering, 53 (2018), pp. 403-415.
- Zhao, X., et al., Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system, Renewable energy, 134 (2019), pp. 681-697.
- de Vasconcelos Segundo, E. H., et al., Design of Heat Exchangers Using Falcon Optimization Algorithm, Applied Thermal Engineering, 156 (2019), pp. 119-144.