Research and Application of Multi-Objective Particle Swarm Optimization Algorithm Based on α-Stable Distribution
The multi-objective particle swarm optimization algorithm (MOPSO) has been applied and modified for optimal designs in various engineering fields. However, the intimal algorithm is still has the problems of low accuracy and poor diversity of solutions as it is easy to fall into local optimum in the...
| Format: | Article |
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| Language: | zho |
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The Northwestern Polytechnical University
2019-04-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2019/02/jnwpu2019372p232/jnwpu2019372p232.html |
| Summary: | The multi-objective particle swarm optimization algorithm (MOPSO) has been applied and modified for optimal designs in various engineering fields. However, the intimal algorithm is still has the problems of low accuracy and poor diversity of solutions as it is easy to fall into local optimum in the later evolution stage. A new dynamic mutation operator has been established based on the α-stable distribution theory and incorporated with multi-objective particle swarm optimization algorithm(ASMOPSO). By using random Numbers which generated by the α-stable distribution, the population of PSO algorithm was mutated. And this mutate operation increases the diversity of the population. Because the stability coefficient in the ASMOPSO algorithm can change the range and amplitude of the mutation. This operation makes the new algorithm has the ability to balance the calculation accuracy and global optimization. Several benchmark functions test show that the ASMOPSO algorithm has fast global optimization ability. The proposed algorithm is applied to the multi-objective aerodynamic optimization design of RAE2822 transonic airfoil. The comparison results also show that ASMOPSO algorithm is more excellent than the basic MOPSO algorithm. |
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| ISSN: | 1000-2758 2609-7125 |