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元启发算法

元啟發算法(英文:metaheuristic), 又稱 萬能啟發式演算法萬用啟發式演算法。在计算机科学和数学优化中,元启发是一种高级的程序或启发式算法,专门用于搜索、生成或选取一个启发式结果(局部搜索算法),该结果可以为一个最优化问题提供足够好的求解,尤其适用于信息不完备或者计算能力受限时的最优化问题。

特色 编辑

元啟發算法(metaheuristic),meta 代表其比一般啟發式演算法在搜尋能力上更為高階。而 heuristic 則代表其算法能夠在一個合理的計算成本內找到一個接近真實最佳解的解,但啟發式演算法並不能夠保證其解的可行性與最佳性。[1] 式通常是使用大量的試誤以在龐大的解空間中搜尋最佳解。

元啟發算法皆在全域搜索與區域搜索中取得權衡,若算法著重區域搜索能力則容易落入區域最佳解陷阱,若著重全域搜索則可能無法收斂解。

演算法 编辑

  • 模擬退火法 (Simulated annealing algorithm, SA)
  • 社會認知算法 (Social cognitive optimization, SCO)
  • 簡化群體演算法 (Simplified swarm optimizatiom, SSO)[2] [3]
  • 調和搜尋演算法 (Harmony search, HS) [4]
  • 水循環算法 (Water cycle algorithm, WCA)[5]
  • 汽車跟蹤最佳化演算法 (Car tracking optimization algorithm)[6]
  • 細菌覓食法 (bacterial foraging algorithm)[7]

仿生元啟發式演算法 编辑

該類型演算法以生物的習性或群體生物行為作為靈感加以發展成為演算法。

  • 基因演算法 (Genetic algorithm, GA)
  • 細菌覓食法 (bacterial foraging algorithm)[7]
  • 粒子群演算法 (Particle swarm optimization, PSO)
  • 蟻群演算法 (Ant colony optimization, ACO)
  • 布穀鳥搜索算法 (Cuckoo Search, CS) [8]
  • 蝙蝠算法 (Bat algorithm, BA) [9]
  • 螢火蟲算法 (Firefly algorithm, FA) [10]
  • 猴群演算法 (Monkey algorithm) [11]
  • 獅子演算法 (Lion optimization algorithm, LOA)[12]
  • 人工蜂群演算法 (Artificial bee colony, ABC)[13]
  • 病毒最佳化演算法 (Virus Optimization Algorithm, VOA)[14]
  • 飛蛾搜尋演算法 (Moth search algorithm)[15]
  • 鯊魚氣味演算法 (Shark smell optimization, SSO)[16]
  • 蚯蚓最佳化演算法 (Earthworm optimization algorithm, EWA)[17]
  • 帝王企鵝演算法 (Emperor Penguins Colony, EPC)[18]
  • 抹香鲸算法 (Sperm whale algorithm,SWA)[19]
  • 人類精神搜索 (Human mental search, HMS)[20]
  • 海洋掠食者算法 (Marine Predators Algorithm, MPA)[21]
  • 狩獵搜索 (Hunting search, HuS)[22]
  • 遷徙鳥類最佳化 (Migrating birds optimization, MBO)[23]

參考文獻 编辑

  1. ^ Zahra Beheshti; Siti Mariyam Hj. Shamsuddin. A Review of Population-based Meta-Heuristic Algorithm (PDF). Int. J. Advance. Soft Comput. Appl. March,2013, 5 (1): 1–35. 
  2. ^ Yeh, Wei-Chang. A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems. Expert Systems with Applications. 2009-07-01, 36 (5): 9192–9200. ISSN 0957-4174. doi:10.1016/j.eswa.2008.12.024 (英语). 
  3. ^ Yeh, Wei-Chang. An improved simplified swarm optimization. Knowledge-Based Systems. 2015-07-01, 82: 60–69. ISSN 0950-7051. doi:10.1016/j.knosys.2015.02.022 (英语). 
  4. ^ Geem, Z. W.; Kim, J. H.; Loganathan, G. V. A new heuristic optimization algorithm: harmony search. simulation. 2001, 76 (2): 60–68 [2021-03-15]. (原始内容于2020-10-17). 
  5. ^ Eskandar, Hadi; Sadollah, Ali; Bahreininejad, Ardeshir; Hamdi, Mohd. Water cycle algorithm – A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures. 2012-11-01,. 110-111: 151–166. ISSN 0045-7949. doi:10.1016/j.compstruc.2012.07.010 (英语). 
  6. ^ Chen, Jian; Cai, Hui; Wang, Wei. A new metaheuristic algorithm: car tracking optimization algorithm. Soft Computing. 2018-06-01, 22 (12): 3857–3878. ISSN 1433-7479. doi:10.1007/s00500-017-2845-7 (英语). 
  7. ^ 7.0 7.1 Pang, Shinsiong; Chen, Mu-Chen. Optimize railway crew scheduling by using modified bacterial foraging algorithm. Computers & Industrial Engineering. 2023-06-01, 180. ISSN 0360-8352. doi:10.1016/j.cie.2023.109218 (英语). 
  8. ^ Yang, X. S.; Deb, S. Cuckoo search via Lévy flights. IEEE. 2009: 210–214. doi:10.1109/NABIC.2009.5393690. 
  9. ^ Yang, X. S. A New Metaheuristic Bat-Inspired Algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). 2010: 65–74 [2021-03-15]. (原始内容于2021-03-08). 
  10. ^ Xin-She Yang. Nature-inspired Metaheuristic Algorithms. Luniver Press. 2010: 5–. ISBN 978-1-905986-28-6. 
  11. ^ Ruiqing Zhao; Wansheng Tang. Monkey algorithm for global numerical optimization. Journal of Uncertain Systems. 2008, 2 (3): 165–176. 
  12. ^ Yazdani, Maziar; Jolai, Fariborz. Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm. Journal of Computational Design and Engineering. 2016-01-01, 3 (1): 24–36. ISSN 2288-5048. doi:10.1016/j.jcde.2015.06.003. 
  13. ^ D Karaboga. An idea based on honey bee swarm for numerical optimization. Technical report-tr06. 2005, 200: 1–10. 
  14. ^ Liang, Yun-Chia; Josue Rodolfo Cuevas Juarez. A novel metaheuristic for continuous optimization problems: Virus optimization algorithm. Engineering Optimization. 2016, 48 (1): 73–93. 
  15. ^ Wang, Gai-Ge. Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing. 2018-06-01, 10 (2): 151–164. ISSN 1865-9292. doi:10.1007/s12293-016-0212-3 (英语). 
  16. ^ Abedinia, Oveis; Amjady, Nima; Ghasemi, Ali. A new metaheuristic algorithm based on shark smell optimization. Complexity. 2016, 21 (5): 97–116. ISSN 1099-0526. doi:10.1002/cplx.21634 (英语). 
  17. ^ Wang, Gai-Ge; Deb, Suash; Coelho, Leandro Dos Santos. Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems. International Journal of Bio-Inspired Computation. 2018-01-01, 12 (1): 1–22. ISSN 1758-0366. doi:10.1504/IJBIC.2018.093328. 
  18. ^ Harifi, Sasan; Khalilian, Madjid; Mohammadzadeh, Javad; Ebrahimnejad, Sadoullah. Emperor Penguins Colony: a new metaheuristic algorithm for optimization. Evolutionary Intelligence. 2019-06-01, 12 (2): 211–226. ISSN 1864-5917. doi:10.1007/s12065-019-00212-x (英语). 
  19. ^ Ebrahimi, A.; Khamehchi, E. Sperm whale algorithm: An effective metaheuristic algorithm for production optimization problems. Journal of Natural Gas Science and Engineering. 2016-02-01, 29: 211–222. ISSN 1875-5100. doi:10.1016/j.jngse.2016.01.001 (英语). 
  20. ^ Mousavirad, Seyed Jalaleddin; Ebrahimpour-Komleh, Hossein. Human mental search: a new population-based metaheuristic optimization algorithm. Applied Intelligence. 2017-10-01, 47 (3): 850–887. ISSN 1573-7497. doi:10.1007/s10489-017-0903-6 (英语). 
  21. ^ Faramarzi, Afshin; Heidarinejad, Mohammad; Mirjalili, Seyedali; Gandomi, Amir H. Marine Predators Algorithm: A nature-inspired metaheuristic. Expert Systems with Applications. 2020-08-15, 152: 113377. ISSN 0957-4174. doi:10.1016/j.eswa.2020.113377 (英语). 
  22. ^ A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search. Computers & Mathematics with Applications. 2010-10-01, 60 (7): 2087–2098 [2021-03-22]. ISSN 0898-1221. doi:10.1016/j.camwa.2010.07.049. (原始内容于2021-04-23) (英语). 
  23. ^ Duman, Ekrem; Uysal, Mitat; Alkaya, Ali Fuat. Migrating Birds Optimization: A new metaheuristic approach and its performance on quadratic assignment problem. Information Sciences. 2012-12-25, 217: 65–77 [2021-03-22]. ISSN 0020-0255. doi:10.1016/j.ins.2012.06.032. (原始内容于2012-12-19) (英语). 

元启发算法, 此條目可参照英語維基百科相應條目来扩充, 2019年6月11日, 若您熟悉来源语言和主题, 请协助参考外语维基百科扩充条目, 请勿直接提交机械翻译, 也不要翻译不可靠, 低品质内容, 依版权协议, 译文需在编辑摘要注明来源, 或于讨论页顶部标记, href, template, translated, page, html, title, template, translated, page, translated, page, 标签, 元啟發算法, 英文, metaheuristic, 又稱, 萬能. 此條目可参照英語維基百科相應條目来扩充 2019年6月11日 若您熟悉来源语言和主题 请协助参考外语维基百科扩充条目 请勿直接提交机械翻译 也不要翻译不可靠 低品质内容 依版权协议 译文需在编辑摘要注明来源 或于讨论页顶部标记 a href Template Translated page html title Template Translated page Translated page a 标签 元啟發算法 英文 metaheuristic 又稱 萬能啟發式演算法 萬用啟發式演算法 在计算机科学和数学优化中 元启发是一种高级的程序或启发式算法 专门用于搜索 生成或选取一个启发式结果 局部搜索算法 该结果可以为一个最优化问题提供足够好的求解 尤其适用于信息不完备或者计算能力受限时的最优化问题 目录 1 特色 2 演算法 2 1 仿生元啟發式演算法 3 參考文獻特色 编辑元啟發算法 metaheuristic meta 代表其比一般啟發式演算法在搜尋能力上更為高階 而 heuristic 則代表其算法能夠在一個合理的計算成本內找到一個接近真實最佳解的解 但啟發式演算法並不能夠保證其解的可行性與最佳性 1 式通常是使用大量的試誤以在龐大的解空間中搜尋最佳解 元啟發算法皆在全域搜索與區域搜索中取得權衡 若算法著重區域搜索能力則容易落入區域最佳解陷阱 若著重全域搜索則可能無法收斂解 演算法 编辑模擬退火法 Simulated annealing algorithm SA 社會認知算法 Social cognitive optimization SCO 簡化群體演算法 Simplified swarm optimizatiom SSO 2 3 調和搜尋演算法 Harmony search HS 4 水循環算法 Water cycle algorithm WCA 5 汽車跟蹤最佳化演算法 Car tracking optimization algorithm 6 細菌覓食法 bacterial foraging algorithm 7 仿生元啟發式演算法 编辑 該類型演算法以生物的習性或群體生物行為作為靈感加以發展成為演算法 基因演算法 Genetic algorithm GA 細菌覓食法 bacterial foraging algorithm 7 粒子群演算法 Particle swarm optimization PSO 蟻群演算法 Ant colony optimization ACO 布穀鳥搜索算法 Cuckoo Search CS 8 蝙蝠算法 Bat algorithm BA 9 螢火蟲算法 Firefly algorithm FA 10 猴群演算法 Monkey algorithm 11 獅子演算法 Lion optimization algorithm LOA 12 人工蜂群演算法 Artificial bee colony ABC 13 病毒最佳化演算法 Virus Optimization Algorithm VOA 14 飛蛾搜尋演算法 Moth search algorithm 15 鯊魚氣味演算法 Shark smell optimization SSO 16 蚯蚓最佳化演算法 Earthworm optimization algorithm EWA 17 帝王企鵝演算法 Emperor Penguins Colony EPC 18 抹香鲸算法 Sperm whale algorithm SWA 19 人類精神搜索 Human mental search HMS 20 海洋掠食者算法 Marine Predators Algorithm MPA 21 狩獵搜索 Hunting search HuS 22 遷徙鳥類最佳化 Migrating birds optimization MBO 23 參考文獻 编辑 Zahra Beheshti Siti Mariyam Hj Shamsuddin A Review of Population based Meta Heuristic Algorithm PDF Int J Advance Soft Comput Appl March 2013 5 1 1 35 引文使用过时参数coauthors 帮助 请检查 date 中的日期值 帮助 Yeh Wei Chang A two stage discrete particle swarm optimization for the problem of multiple multi level redundancy allocation in series systems Expert Systems with Applications 2009 07 01 36 5 9192 9200 ISSN 0957 4174 doi 10 1016 j eswa 2008 12 024 英语 Yeh Wei Chang An improved simplified swarm optimization Knowledge Based Systems 2015 07 01 82 60 69 ISSN 0950 7051 doi 10 1016 j knosys 2015 02 022 英语 Geem Z W Kim J H Loganathan G V A new heuristic optimization algorithm harmony search simulation 2001 76 2 60 68 2021 03 15 原始内容存档于2020 10 17 Eskandar Hadi Sadollah Ali Bahreininejad Ardeshir Hamdi Mohd Water cycle algorithm A novel metaheuristic optimization method for solving constrained engineering optimization problems Computers amp Structures 2012 11 01 110 111 151 166 ISSN 0045 7949 doi 10 1016 j compstruc 2012 07 010 英语 Chen Jian Cai Hui Wang Wei A new metaheuristic algorithm car tracking optimization algorithm Soft Computing 2018 06 01 22 12 3857 3878 ISSN 1433 7479 doi 10 1007 s00500 017 2845 7 英语 7 0 7 1 Pang Shinsiong Chen Mu Chen Optimize railway crew scheduling by using modified bacterial foraging algorithm Computers amp Industrial Engineering 2023 06 01 180 ISSN 0360 8352 doi 10 1016 j cie 2023 109218 英语 Yang X S Deb S Cuckoo search via Levy flights IEEE 2009 210 214 doi 10 1109 NABIC 2009 5393690 Yang X S A New Metaheuristic Bat Inspired Algorithm Nature Inspired Cooperative Strategies for Optimization NICSO 2010 2010 65 74 2021 03 15 原始内容存档于2021 03 08 Xin She Yang Nature inspired Metaheuristic Algorithms Luniver Press 2010 5 ISBN 978 1 905986 28 6 Ruiqing Zhao Wansheng Tang Monkey algorithm for global numerical optimization Journal of Uncertain Systems 2008 2 3 165 176 Yazdani Maziar Jolai Fariborz Lion Optimization Algorithm LOA A nature inspired metaheuristic algorithm Journal of Computational Design and Engineering 2016 01 01 3 1 24 36 ISSN 2288 5048 doi 10 1016 j jcde 2015 06 003 D Karaboga An idea based on honey bee swarm for numerical optimization Technical report tr06 2005 200 1 10 Liang Yun Chia Josue Rodolfo Cuevas Juarez A novel metaheuristic for continuous optimization problems Virus optimization algorithm Engineering Optimization 2016 48 1 73 93 引文使用过时参数coauthors 帮助 Wang Gai Ge Moth search algorithm a bio inspired metaheuristic algorithm for global optimization problems Memetic Computing 2018 06 01 10 2 151 164 ISSN 1865 9292 doi 10 1007 s12293 016 0212 3 英语 Abedinia Oveis Amjady Nima Ghasemi Ali A new metaheuristic algorithm based on shark smell optimization Complexity 2016 21 5 97 116 ISSN 1099 0526 doi 10 1002 cplx 21634 英语 Wang Gai Ge Deb Suash Coelho Leandro Dos Santos Earthworm optimisation algorithm a bio inspired metaheuristic algorithm for global optimisation problems International Journal of Bio Inspired Computation 2018 01 01 12 1 1 22 ISSN 1758 0366 doi 10 1504 IJBIC 2018 093328 Harifi Sasan Khalilian Madjid Mohammadzadeh Javad Ebrahimnejad Sadoullah Emperor Penguins Colony a new metaheuristic algorithm for optimization Evolutionary Intelligence 2019 06 01 12 2 211 226 ISSN 1864 5917 doi 10 1007 s12065 019 00212 x 英语 Ebrahimi A Khamehchi E Sperm whale algorithm An effective metaheuristic algorithm for production optimization problems Journal of Natural Gas Science and Engineering 2016 02 01 29 211 222 ISSN 1875 5100 doi 10 1016 j jngse 2016 01 001 英语 Mousavirad Seyed Jalaleddin Ebrahimpour Komleh Hossein Human mental search a new population based metaheuristic optimization algorithm Applied Intelligence 2017 10 01 47 3 850 887 ISSN 1573 7497 doi 10 1007 s10489 017 0903 6 英语 Faramarzi Afshin Heidarinejad Mohammad Mirjalili Seyedali Gandomi Amir H Marine Predators Algorithm A nature inspired metaheuristic Expert Systems with Applications 2020 08 15 152 113377 ISSN 0957 4174 doi 10 1016 j eswa 2020 113377 英语 A novel meta heuristic optimization algorithm inspired by group hunting of animals Hunting search Computers amp Mathematics with Applications 2010 10 01 60 7 2087 2098 2021 03 22 ISSN 0898 1221 doi 10 1016 j camwa 2010 07 049 原始内容存档于2021 04 23 英语 Duman Ekrem Uysal Mitat Alkaya Ali Fuat Migrating Birds Optimization A new metaheuristic approach and its performance on quadratic assignment problem Information Sciences 2012 12 25 217 65 77 2021 03 22 ISSN 0020 0255 doi 10 1016 j ins 2012 06 032 原始内容存档于2012 12 19 英语 取自 https zh wikipedia org w index php title 元启发算法 amp oldid 77356412, 维基百科,wiki,书籍,书籍,图书馆,

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