An Efficient and Effective Algorithm for Large Scale Global Optimization Problems
| العنوان: | An Efficient and Effective Algorithm for Large Scale Global Optimization Problems |
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| المؤلفون: | Kanchao Lian, Aijia Ouyang, Xu-Yu Peng |
| المصدر: | International Journal of Pattern Recognition and Artificial Intelligence. 29:1559006 |
| بيانات النشر: | World Scientific Pub Co Pte Lt, 2015. |
| سنة النشر: | 2015 |
| مصطلحات موضوعية: | Mathematical optimization, Meta-optimization, business.industry, Particle swarm optimization, Local optimum, Artificial Intelligence, Derivative-free optimization, Computer Vision and Pattern Recognition, Artificial intelligence, Multi-swarm optimization, business, Metaheuristic, Global optimization, Software, Premature convergence, Mathematics |
| الوصف: | Invasive weed optimization (IWO) algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm are inclined to fall into local optimum with lower convergence accuracy when separately used to deal with large scale global optimization (LSGO) problems. In order to fully utilize the advantages of these two intelligent algorithms and complement each other, following the idea of portfolio optimization, this paper correspondingly adjusts and improves the quantum models of IWO and QPSO, organically integrates the two algorithms, and proposes the quantum-behaved invasive weed optimization (QIWO) algorithm. This mixed algorithm can achieve the purpose of information exchange and cooperative search through alternate search enables the make algorithm converge to the optimal solution quickly, properly overcoming the defects of falling into local optimum and premature convergence. Test results of 20 LSGO functions show that compared with other algorithms, QIWO has stronger global optimization capability, faster convergence speed and higher convergence accuracy. |
| تدمد: | 1793-6381 0218-0014 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_________::76b2af6d204ce8c9bd8caceb110d38ecTest https://doi.org/10.1142/s0218001415590065Test |
| رقم الانضمام: | edsair.doi...........76b2af6d204ce8c9bd8caceb110d38ec |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17936381 02180014 |
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