Application of Improved Quantum Genetic Algorithm in Optimization for Surface to Air Anti-Radiation Hybrid Group Force Deployment
| Autor: | Chang'an Shang, Jiale Gao, Junliang Ji, Minle Wang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science surface to air ati-radiation q-gate General Engineering Stability (learning theory) quantum genetic algorithm Particle swarm optimization TL1-4050 Jamming 02 engineering and technology Interference (wave propagation) force deployment 020901 industrial engineering & automation Rate of convergence Software deployment Control theory hybrid group 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Global optimization Rotation (mathematics) Motor vehicles. Aeronautics. Astronautics |
| Zdroj: | Xibei Gongye Daxue Xuebao, Vol 37, Iss 5, Pp 992-999 (2019) |
| ISSN: | 2609-7125 1000-2758 |
| DOI: | 10.1051/jnwpu/20193750992 |
| Popis: | In this paper, the concept and force deployment needs for anti-electronic jamming and defensing air of surface to air anti-radiation hybrid group was presented, the relationship between shield angle, deployment distance and effective electronic interference etc, were analyzed in the background of air raid battle which is with electronic support, force deployment optimization model of surface to air anti-radiation hybrid group was built based on the kill zone target function. In terms of the characteristic of hybrid group force deployment, quantum genetic algorithm (QGA) was improved with self-adaption rotation angle, the problem which was based on a living example was solved with improved QGA. By contrast, the improved QGA is better in the respects of global optimization, rate of convergence and stability than QGA, particle swarm optimization algorithm and quantum vortex algorithm in the problem of optimization for surface to air anti-radiation hybrid group force deployment. |
| Databáze: | OpenAIRE |
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