| Autor: |
Zhaojuan ZHANG, Wanliang WANG, Jijun TANG |
| Jazyk: |
čínština |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Tongxin xuebao, Vol 41, Pp 22-31 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
1000-436X |
| DOI: |
10.11959/j.issn.1000-436x.2020173 |
| Popis: |
To address the large-scale discrete optimization problem,a cooperative optimization algorithm called IDQPSO-SA was proposed.First,a strategy by applying two selections on the averaging fitness values to update the mean best position was presented,which could overcome the deficiency that QPSO was not applicable for discrete problems.Second,the double cut joining (DCJ) sorting strategy was incorporated into IDQPSO-SA,since the DCJ sorting strategy could considerably reduce the search space.Finally,the probability jumping ability of simulated annealing (SA) was combined with the parallel search of QPSO,and the global search was carried out collaboratively.By comparing with existing algorithms,the experimental results show that IDQPSO-SA further improves the search efficiency and has a comparable performance when faced with large-scale discrete optimization problems. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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