Chaotic Adaptive Quantum Firefly Algorithm
In order to improve the search performance of quantum firefly algorithm(QFA) and solve the problem that it is easy to fall into local optimality when facing some problems,an improved QFA with chaotic map,neighborhood search and adaptive random disturbance is proposed,named chaos adaptive quantum fir...
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| Autor*in: |
LIU Xiaonan, AN Jiale, HE Ming, SONG Huichao [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Chinesisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
quantum firefly algorithm|swarm intelligence|global optimization|chaotic map|test functions |
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| Übergeordnetes Werk: |
In: Jisuanji kexue - Editorial office of Computer Science, 2021, 50(2023), 4, Seite 204-211 |
|---|---|
| Übergeordnetes Werk: |
volume:50 ; year:2023 ; number:4 ; pages:204-211 |
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10.11896/jsjkx.220100242 |
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DOAJ089192257 |
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| 520 | |a In order to improve the search performance of quantum firefly algorithm(QFA) and solve the problem that it is easy to fall into local optimality when facing some problems,an improved QFA with chaotic map,neighborhood search and adaptive random disturbance is proposed,named chaos adaptive quantum firefly algorithm(CAQFA).In this algorithm,chaotic map is applied to the initialization stage of the population to improve the quality of the initial population.In the update stage,the neighborhood search is carried out for the optimal individual of the current population to enhance the ability of the algorithm to jump out of the local optimization.The introduction of adaptive random disturbance to other individuals increases the randomness of the algorithm and achieves a balance between the exploration and development of search space,so as to improve the performance of the algorithm.Eighteen different types of benchmark functions are selected to test the performance of the algorithm.The test results show that CAQFA has better search ability,stability and strong competitiveness compared with firefly algorithm(FA),QFA and quantum particle swarm optimization(QPSO). | ||
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10.11896/jsjkx.220100242 doi (DE-627)DOAJ089192257 (DE-599)DOAJ6c1f47abbbfc4e55a475e51b8415fc9e DE-627 ger DE-627 rakwb chi QA76.75-76.765 T1-995 LIU Xiaonan, AN Jiale, HE Ming, SONG Huichao verfasserin aut Chaotic Adaptive Quantum Firefly Algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the search performance of quantum firefly algorithm(QFA) and solve the problem that it is easy to fall into local optimality when facing some problems,an improved QFA with chaotic map,neighborhood search and adaptive random disturbance is proposed,named chaos adaptive quantum firefly algorithm(CAQFA).In this algorithm,chaotic map is applied to the initialization stage of the population to improve the quality of the initial population.In the update stage,the neighborhood search is carried out for the optimal individual of the current population to enhance the ability of the algorithm to jump out of the local optimization.The introduction of adaptive random disturbance to other individuals increases the randomness of the algorithm and achieves a balance between the exploration and development of search space,so as to improve the performance of the algorithm.Eighteen different types of benchmark functions are selected to test the performance of the algorithm.The test results show that CAQFA has better search ability,stability and strong competitiveness compared with firefly algorithm(FA),QFA and quantum particle swarm optimization(QPSO). quantum firefly algorithm|swarm intelligence|global optimization|chaotic map|test functions Computer software Technology (General) In Jisuanji kexue Editorial office of Computer Science, 2021 50(2023), 4, Seite 204-211 (DE-627)DOAJ078619254 1002137X nnns volume:50 year:2023 number:4 pages:204-211 https://doi.org/10.11896/jsjkx.220100242 kostenfrei https://doaj.org/article/6c1f47abbbfc4e55a475e51b8415fc9e kostenfrei https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-4-204.pdf kostenfrei https://doaj.org/toc/1002-137X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 50 2023 4 204-211 |
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10.11896/jsjkx.220100242 doi (DE-627)DOAJ089192257 (DE-599)DOAJ6c1f47abbbfc4e55a475e51b8415fc9e DE-627 ger DE-627 rakwb chi QA76.75-76.765 T1-995 LIU Xiaonan, AN Jiale, HE Ming, SONG Huichao verfasserin aut Chaotic Adaptive Quantum Firefly Algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the search performance of quantum firefly algorithm(QFA) and solve the problem that it is easy to fall into local optimality when facing some problems,an improved QFA with chaotic map,neighborhood search and adaptive random disturbance is proposed,named chaos adaptive quantum firefly algorithm(CAQFA).In this algorithm,chaotic map is applied to the initialization stage of the population to improve the quality of the initial population.In the update stage,the neighborhood search is carried out for the optimal individual of the current population to enhance the ability of the algorithm to jump out of the local optimization.The introduction of adaptive random disturbance to other individuals increases the randomness of the algorithm and achieves a balance between the exploration and development of search space,so as to improve the performance of the algorithm.Eighteen different types of benchmark functions are selected to test the performance of the algorithm.The test results show that CAQFA has better search ability,stability and strong competitiveness compared with firefly algorithm(FA),QFA and quantum particle swarm optimization(QPSO). quantum firefly algorithm|swarm intelligence|global optimization|chaotic map|test functions Computer software Technology (General) In Jisuanji kexue Editorial office of Computer Science, 2021 50(2023), 4, Seite 204-211 (DE-627)DOAJ078619254 1002137X nnns volume:50 year:2023 number:4 pages:204-211 https://doi.org/10.11896/jsjkx.220100242 kostenfrei https://doaj.org/article/6c1f47abbbfc4e55a475e51b8415fc9e kostenfrei https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-4-204.pdf kostenfrei https://doaj.org/toc/1002-137X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 50 2023 4 204-211 |
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10.11896/jsjkx.220100242 doi (DE-627)DOAJ089192257 (DE-599)DOAJ6c1f47abbbfc4e55a475e51b8415fc9e DE-627 ger DE-627 rakwb chi QA76.75-76.765 T1-995 LIU Xiaonan, AN Jiale, HE Ming, SONG Huichao verfasserin aut Chaotic Adaptive Quantum Firefly Algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In order to improve the search performance of quantum firefly algorithm(QFA) and solve the problem that it is easy to fall into local optimality when facing some problems,an improved QFA with chaotic map,neighborhood search and adaptive random disturbance is proposed,named chaos adaptive quantum firefly algorithm(CAQFA).In this algorithm,chaotic map is applied to the initialization stage of the population to improve the quality of the initial population.In the update stage,the neighborhood search is carried out for the optimal individual of the current population to enhance the ability of the algorithm to jump out of the local optimization.The introduction of adaptive random disturbance to other individuals increases the randomness of the algorithm and achieves a balance between the exploration and development of search space,so as to improve the performance of the algorithm.Eighteen different types of benchmark functions are selected to test the performance of the algorithm.The test results show that CAQFA has better search ability,stability and strong competitiveness compared with firefly algorithm(FA),QFA and quantum particle swarm optimization(QPSO). quantum firefly algorithm|swarm intelligence|global optimization|chaotic map|test functions Computer software Technology (General) In Jisuanji kexue Editorial office of Computer Science, 2021 50(2023), 4, Seite 204-211 (DE-627)DOAJ078619254 1002137X nnns volume:50 year:2023 number:4 pages:204-211 https://doi.org/10.11896/jsjkx.220100242 kostenfrei https://doaj.org/article/6c1f47abbbfc4e55a475e51b8415fc9e kostenfrei https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-4-204.pdf kostenfrei https://doaj.org/toc/1002-137X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ AR 50 2023 4 204-211 |
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In order to improve the search performance of quantum firefly algorithm(QFA) and solve the problem that it is easy to fall into local optimality when facing some problems,an improved QFA with chaotic map,neighborhood search and adaptive random disturbance is proposed,named chaos adaptive quantum firefly algorithm(CAQFA).In this algorithm,chaotic map is applied to the initialization stage of the population to improve the quality of the initial population.In the update stage,the neighborhood search is carried out for the optimal individual of the current population to enhance the ability of the algorithm to jump out of the local optimization.The introduction of adaptive random disturbance to other individuals increases the randomness of the algorithm and achieves a balance between the exploration and development of search space,so as to improve the performance of the algorithm.Eighteen different types of benchmark functions are selected to test the performance of the algorithm.The test results show that CAQFA has better search ability,stability and strong competitiveness compared with firefly algorithm(FA),QFA and quantum particle swarm optimization(QPSO). |
| abstractGer |
In order to improve the search performance of quantum firefly algorithm(QFA) and solve the problem that it is easy to fall into local optimality when facing some problems,an improved QFA with chaotic map,neighborhood search and adaptive random disturbance is proposed,named chaos adaptive quantum firefly algorithm(CAQFA).In this algorithm,chaotic map is applied to the initialization stage of the population to improve the quality of the initial population.In the update stage,the neighborhood search is carried out for the optimal individual of the current population to enhance the ability of the algorithm to jump out of the local optimization.The introduction of adaptive random disturbance to other individuals increases the randomness of the algorithm and achieves a balance between the exploration and development of search space,so as to improve the performance of the algorithm.Eighteen different types of benchmark functions are selected to test the performance of the algorithm.The test results show that CAQFA has better search ability,stability and strong competitiveness compared with firefly algorithm(FA),QFA and quantum particle swarm optimization(QPSO). |
| abstract_unstemmed |
In order to improve the search performance of quantum firefly algorithm(QFA) and solve the problem that it is easy to fall into local optimality when facing some problems,an improved QFA with chaotic map,neighborhood search and adaptive random disturbance is proposed,named chaos adaptive quantum firefly algorithm(CAQFA).In this algorithm,chaotic map is applied to the initialization stage of the population to improve the quality of the initial population.In the update stage,the neighborhood search is carried out for the optimal individual of the current population to enhance the ability of the algorithm to jump out of the local optimization.The introduction of adaptive random disturbance to other individuals increases the randomness of the algorithm and achieves a balance between the exploration and development of search space,so as to improve the performance of the algorithm.Eighteen different types of benchmark functions are selected to test the performance of the algorithm.The test results show that CAQFA has better search ability,stability and strong competitiveness compared with firefly algorithm(FA),QFA and quantum particle swarm optimization(QPSO). |
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Chaotic Adaptive Quantum Firefly Algorithm |
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https://doi.org/10.11896/jsjkx.220100242 https://doaj.org/article/6c1f47abbbfc4e55a475e51b8415fc9e https://www.jsjkx.com/fileup/1002-137X/PDF/1002-137X-2023-50-4-204.pdf https://doaj.org/toc/1002-137X |
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2024-07-03T21:47:29.097Z |
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