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元啟發算法

維基百科，自由的百科全書

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。在計算機科學和數學優化中，元啟發是一種高級的程序或啟發式算法，專門用於搜索、生成或選取一個啟發式結果（局部搜索算法），該結果可以為一個最優化問題提供足夠好的求解，尤其適用於信息不完備或者計算能力受限時的最優化問題。

特色

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

仿生元啟發式演算法

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。

參考文獻

  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 （英語）. 
  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 （英語）. 
  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]. （原始內容存檔於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 （英語）. 
  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 （英語）. 
  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 （英語）. 
  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]. （原始內容存檔於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 （英語）. 
  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 （英語）. 
  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 （英語）. 
  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 （英語）. 
  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 （英語）. 
  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 （英語）. 
  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. （原始內容存檔於2021-04-23） （英語）. 
  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. （原始內容存檔於2012-12-19） （英語）. 
取自「https://zh.wikipedia.org/w/index.php?title=元启发算法&oldid=82709200」