Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm
The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The pr...
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| Format: | Article |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/20/9772 |
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| author | Xueli Shen Daniel C. Ihenacho |
| author_facet | Xueli Shen Daniel C. Ihenacho |
| author_sort | Xueli Shen |
| collection | DOAJ |
| description | The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The primary concerns of design engineers are that the traditional technique used in the design process of a gas cyclone utilizes complex mathematical formulas and a sensitivity approach to obtain relevant optimal design parameters. The motivation of this research effort is based on the desire to simplify complex mathematical models and the sensitivity approach for gas cyclone design with the use of an objective function, which is of the minimization type. The process makes use of the initial population generated by the DE algorithm, and the stopping criterion of DE is set as the fitness value. When the fitness value is not less than the current global best, the DE population is taken over by PSO. For each iteration, the new velocity and position are updated in every generation until the optimal solution is achieved. When using PSO independently, the adoption of a hybridised particle swarm optimization method for the design of an optimum gas cyclone produced better results, with an overall efficiency of 0.70, and with a low cost at the rate of 230 cost/s. |
| first_indexed | 2024-03-10T06:44:36Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:44:36Z |
| publishDate | 2021-10-01 |
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| spelling | doaj.art-b5ed2db778d04ba1a0b7a82aa40819d32023-11-22T17:24:10ZengMDPI AGApplied Sciences2076-34172021-10-011120977210.3390/app11209772Design of Gas Cyclone Using Hybrid Particle Swarm Optimization AlgorithmXueli Shen0Daniel C. Ihenacho1Department of Software Engineering, Liaoning Technical University, Huludao 125000, ChinaDepartment of Software Engineering, Liaoning Technical University, Huludao 125000, ChinaThe method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and is relatively straightforward to comprehend. The primary concerns of design engineers are that the traditional technique used in the design process of a gas cyclone utilizes complex mathematical formulas and a sensitivity approach to obtain relevant optimal design parameters. The motivation of this research effort is based on the desire to simplify complex mathematical models and the sensitivity approach for gas cyclone design with the use of an objective function, which is of the minimization type. The process makes use of the initial population generated by the DE algorithm, and the stopping criterion of DE is set as the fitness value. When the fitness value is not less than the current global best, the DE population is taken over by PSO. For each iteration, the new velocity and position are updated in every generation until the optimal solution is achieved. When using PSO independently, the adoption of a hybridised particle swarm optimization method for the design of an optimum gas cyclone produced better results, with an overall efficiency of 0.70, and with a low cost at the rate of 230 cost/s.https://www.mdpi.com/2076-3417/11/20/9772particle swarm optimization (PSO)differential evolution (DE)gas cyclonehybridised particle swarm optimizationevolutionary algorithm |
| spellingShingle | Xueli Shen Daniel C. Ihenacho Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm Applied Sciences particle swarm optimization (PSO) differential evolution (DE) gas cyclone hybridised particle swarm optimization evolutionary algorithm |
| title | Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm |
| title_full | Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm |
| title_fullStr | Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm |
| title_full_unstemmed | Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm |
| title_short | Design of Gas Cyclone Using Hybrid Particle Swarm Optimization Algorithm |
| title_sort | design of gas cyclone using hybrid particle swarm optimization algorithm |
| topic | particle swarm optimization (PSO) differential evolution (DE) gas cyclone hybridised particle swarm optimization evolutionary algorithm |
| url | https://www.mdpi.com/2076-3417/11/20/9772 |
| work_keys_str_mv | AT xuelishen designofgascycloneusinghybridparticleswarmoptimizationalgorithm AT danielcihenacho designofgascycloneusinghybridparticleswarmoptimizationalgorithm |