A Phoenix++ Based New Genetic Algorithm Involving Mechanism of Simulated Annealing
Genetic algorithm is easy to fall into local optimal solution. Simulated annealing algorithm may accept nonoptimal solution at a certain probability to jump out of local optimal solution. On the other hand, lack of communication among genes in MapReduce platform based genetic algorithm, the high-per...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2015-08-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/806708 |
| _version_ | 1797713844068417536 |
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| author | Luokai Hu Jin Liu Chao Liang Fuchuan Ni Hang Chen |
| author_facet | Luokai Hu Jin Liu Chao Liang Fuchuan Ni Hang Chen |
| author_sort | Luokai Hu |
| collection | DOAJ |
| description | Genetic algorithm is easy to fall into local optimal solution. Simulated annealing algorithm may accept nonoptimal solution at a certain probability to jump out of local optimal solution. On the other hand, lack of communication among genes in MapReduce platform based genetic algorithm, the high-performance distributed computing technologies or platforms can further increase the execution efficiency of these traditional genetic algorithms. To this end, we propose a novel Phoenix++ based new genetic algorithm involving mechanism of simulated annealing. Simulated annealing genetic algorithm has two distinctive characteristics. First, it is the synthesis of the conventional genetic algorithm and the simulated annealing algorithm. This characteristic guarantees our proposed algorithm has a higher probability of getting the global optimal solution than traditional genetic algorithms. The other is that our algorithm is a parallel algorithm running on the high-performance parallel platform Phoenix++ instead of a conventional serial genetic algorithm. Phoenix++ implements the MapReduce programming model that processes and generates large data sets with our parallel, distributed algorithm on a cluster. The experiments indicate that the convergence speed of GA algorithm is significantly faster after adding the simulated annealing algorithm on Phoenix++ platform. |
| first_indexed | 2024-03-12T07:42:52Z |
| format | Article |
| id | doaj.art-a8203922e4444c7398684636569576d7 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T07:42:52Z |
| publishDate | 2015-08-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-a8203922e4444c7398684636569576d72023-09-02T21:11:51ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772015-08-011110.1155/2015/806708806708A Phoenix++ Based New Genetic Algorithm Involving Mechanism of Simulated AnnealingLuokai Hu0Jin Liu1Chao Liang2Fuchuan Ni3Hang Chen4 Lenovo Mobile Communication Technology Co., Ltd., China State Key Laboratory of Software Engineering, Computer School, Wuhan University, Wuhan, China Lenovo Mobile Communication Technology Co., Ltd., China Department of Computer Science, Huazhong Agricultural University, China Department of Computer Science, Huazhong Agricultural University, ChinaGenetic algorithm is easy to fall into local optimal solution. Simulated annealing algorithm may accept nonoptimal solution at a certain probability to jump out of local optimal solution. On the other hand, lack of communication among genes in MapReduce platform based genetic algorithm, the high-performance distributed computing technologies or platforms can further increase the execution efficiency of these traditional genetic algorithms. To this end, we propose a novel Phoenix++ based new genetic algorithm involving mechanism of simulated annealing. Simulated annealing genetic algorithm has two distinctive characteristics. First, it is the synthesis of the conventional genetic algorithm and the simulated annealing algorithm. This characteristic guarantees our proposed algorithm has a higher probability of getting the global optimal solution than traditional genetic algorithms. The other is that our algorithm is a parallel algorithm running on the high-performance parallel platform Phoenix++ instead of a conventional serial genetic algorithm. Phoenix++ implements the MapReduce programming model that processes and generates large data sets with our parallel, distributed algorithm on a cluster. The experiments indicate that the convergence speed of GA algorithm is significantly faster after adding the simulated annealing algorithm on Phoenix++ platform.https://doi.org/10.1155/2015/806708 |
| spellingShingle | Luokai Hu Jin Liu Chao Liang Fuchuan Ni Hang Chen A Phoenix++ Based New Genetic Algorithm Involving Mechanism of Simulated Annealing International Journal of Distributed Sensor Networks |
| title | A Phoenix++ Based New Genetic Algorithm Involving Mechanism of Simulated Annealing |
| title_full | A Phoenix++ Based New Genetic Algorithm Involving Mechanism of Simulated Annealing |
| title_fullStr | A Phoenix++ Based New Genetic Algorithm Involving Mechanism of Simulated Annealing |
| title_full_unstemmed | A Phoenix++ Based New Genetic Algorithm Involving Mechanism of Simulated Annealing |
| title_short | A Phoenix++ Based New Genetic Algorithm Involving Mechanism of Simulated Annealing |
| title_sort | phoenix based new genetic algorithm involving mechanism of simulated annealing |
| url | https://doi.org/10.1155/2015/806708 |
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