Analysis and Improvement of Fireworks Algorithm
The Fireworks Algorithm is a recently developed swarm intelligence algorithm to simulate the explosion process of fireworks. Based on the analysis of each operator of Fireworks Algorithm (FWA), this paper improves the FWA and proves that the improved algorithm converges to the global optimal solutio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-02-01
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| Series: | Algorithms |
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| Online Access: | http://www.mdpi.com/1999-4893/10/1/26 |
| _version_ | 1818610487700488192 |
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| author | Xi-Guang Li Shou-Fei Han Chang-Qing Gong |
| author_facet | Xi-Guang Li Shou-Fei Han Chang-Qing Gong |
| author_sort | Xi-Guang Li |
| collection | DOAJ |
| description | The Fireworks Algorithm is a recently developed swarm intelligence algorithm to simulate the explosion process of fireworks. Based on the analysis of each operator of Fireworks Algorithm (FWA), this paper improves the FWA and proves that the improved algorithm converges to the global optimal solution with probability 1. The proposed algorithm improves the goal of further boosting performance and achieving global optimization where mainly include the following strategies. Firstly using the opposition-based learning initialization population. Secondly a new explosion amplitude mechanism for the optimal firework is proposed. In addition, the adaptive t-distribution mutation for non-optimal individuals and elite opposition-based learning for the optimal individual are used. Finally, a new selection strategy, namely Disruptive Selection, is proposed to reduce the running time of the algorithm compared with FWA. In our simulation, we apply the CEC2013 standard functions and compare the proposed algorithm (IFWA) with SPSO2011, FWA, EFWA and dynFWA. The results show that the proposed algorithm has better overall performance on the test functions. |
| first_indexed | 2024-12-16T15:15:13Z |
| format | Article |
| id | doaj.art-92a18ca82d94403c8ef73ac0d1526b44 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-12-16T15:15:13Z |
| publishDate | 2017-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-92a18ca82d94403c8ef73ac0d1526b442022-12-21T22:26:50ZengMDPI AGAlgorithms1999-48932017-02-011012610.3390/a10010026a10010026Analysis and Improvement of Fireworks AlgorithmXi-Guang Li0Shou-Fei Han1Chang-Qing Gong2School of Computer, Shenyang Aerospace University, Shenyang 110136, ChinaSchool of Computer, Shenyang Aerospace University, Shenyang 110136, ChinaSchool of Computer, Shenyang Aerospace University, Shenyang 110136, ChinaThe Fireworks Algorithm is a recently developed swarm intelligence algorithm to simulate the explosion process of fireworks. Based on the analysis of each operator of Fireworks Algorithm (FWA), this paper improves the FWA and proves that the improved algorithm converges to the global optimal solution with probability 1. The proposed algorithm improves the goal of further boosting performance and achieving global optimization where mainly include the following strategies. Firstly using the opposition-based learning initialization population. Secondly a new explosion amplitude mechanism for the optimal firework is proposed. In addition, the adaptive t-distribution mutation for non-optimal individuals and elite opposition-based learning for the optimal individual are used. Finally, a new selection strategy, namely Disruptive Selection, is proposed to reduce the running time of the algorithm compared with FWA. In our simulation, we apply the CEC2013 standard functions and compare the proposed algorithm (IFWA) with SPSO2011, FWA, EFWA and dynFWA. The results show that the proposed algorithm has better overall performance on the test functions.http://www.mdpi.com/1999-4893/10/1/26fireworks algorithmopposition-based learningt-distributiondisruptive selectionexplosion amplitude |
| spellingShingle | Xi-Guang Li Shou-Fei Han Chang-Qing Gong Analysis and Improvement of Fireworks Algorithm Algorithms fireworks algorithm opposition-based learning t-distribution disruptive selection explosion amplitude |
| title | Analysis and Improvement of Fireworks Algorithm |
| title_full | Analysis and Improvement of Fireworks Algorithm |
| title_fullStr | Analysis and Improvement of Fireworks Algorithm |
| title_full_unstemmed | Analysis and Improvement of Fireworks Algorithm |
| title_short | Analysis and Improvement of Fireworks Algorithm |
| title_sort | analysis and improvement of fireworks algorithm |
| topic | fireworks algorithm opposition-based learning t-distribution disruptive selection explosion amplitude |
| url | http://www.mdpi.com/1999-4893/10/1/26 |
| work_keys_str_mv | AT xiguangli analysisandimprovementoffireworksalgorithm AT shoufeihan analysisandimprovementoffireworksalgorithm AT changqinggong analysisandimprovementoffireworksalgorithm |