元启发算法：修订间差异

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==演算法==
==演算法==
⚫
*[[模擬退火|模擬退火法]] (Simulated annealing algorithm, SA)
⚫
*[[社会认知优化|社會認知算法]] (Social cognitive optimization, SCO)
*[[簡化群體演算法]] (Simplified swarm optimizatiom, SSO)<ref>{{Cite journal|title=A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems|url=https://www.sciencedirect.com/science/article/pii/S0957417408008890|last=Yeh|first=Wei-Chang|date=2009-07-01|journal=Expert Systems with Applications|issue=5|doi=10.1016/j.eswa.2008.12.024|volume=36|pages=9192–9200|language=en|issn=0957-4174}}</ref> <ref>{{Cite journal|title=An improved simplified swarm optimization|url=https://www.sciencedirect.com/science/article/pii/S0950705115000763|last=Yeh|first=Wei-Chang|date=2015-07-01|journal=Knowledge-Based Systems|doi=10.1016/j.knosys.2015.02.022|volume=82|pages=60–69|language=en|issn=0950-7051}}</ref>
*[[調和搜尋演算法]] (Harmony search, HS) <ref>{{cite journal|title=A new heuristic optimization algorithm: harmony search|url=https://journals.sagepub.com/doi/abs/10.1177/003754970107600201|date=2001|journal=simulation|issue=2|volume=76|pages=60-68|author3=Loganathan, G. V.|author1=Geem, Z. W.|author2=Kim, J. H.}}</ref>
*汽車跟蹤最佳化演算法(Car tracking optimization algorithm)<ref>{{Cite journal|title=A new metaheuristic algorithm: car tracking optimization algorithm|url=https://doi.org/10.1007/s00500-017-2845-7|last=Chen|first=Jian|last2=Cai|first2=Hui|date=2018-06-01|journal=Soft Computing|issue=12|doi=10.1007/s00500-017-2845-7|volume=22|pages=3857–3878|language=en|issn=1433-7479|last3=Wang|first3=Wei}}</ref>

=== 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。

*[[基因演算法]] (Genetic algorithm, GA)
*[[基因演算法]] (Genetic algorithm, GA)
*[[粒子群演算法]] (Particle swarm optimization, PSO)
*[[粒子群演算法]] (Particle swarm optimization, PSO)
*[[蟻群算法|蟻群演算法]] (Ant colony optimization, ACO)
*[[蟻群算法|蟻群演算法]] (Ant colony optimization, ACO)
⚫
*[[布谷鸟搜索算法|布穀鳥搜索算法]] (Cuckoo Search, CS) <ref>{{cite journal|title=Cuckoo search via Lévy flights|url=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5393690|date=2009|journal=IEEE|doi=10.1109/NABIC.2009.5393690|pages=210-214|author1=Yang, X. S.|author2=Deb, S}}</ref>
⚫
*[[模擬退火|模擬退火法]] (Simulated annealing algorithm, SA)
⚫
*[[蝙蝠算法]] (Bat algorithm, BA) <ref>{{cite journal|title=A New Metaheuristic Bat-Inspired Algorithm|url=https://link.springer.com/chapter/10.1007/978-3-642-12538-6_6|date=2010|journal=Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)|pages=65-74|author1=Yang, X. S.}}</ref>
⚫
*[[社会认知优化|社會認知算法]] (Social cognitive optimization, SCO)
⚫
*[[布谷鸟搜索算法|布穀鳥搜索算法]] (Cuckoo Search, CS) <ref>{{cite journal |author1=Yang, X. S. |author2=Deb, S |title=Cuckoo search via Lévy flights |journal=IEEE |date=2009 |pages=210-214 |doi=10.1109/NABIC.2009.5393690 |url=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5393690}}</ref>
⚫
*[[蝙蝠算法]] (Bat algorithm, BA) <ref>{{cite journal |author1=Yang, X. S. |title=A New Metaheuristic Bat-Inspired Algorithm |journal=Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) |date=2010 |pages=65-74 |url=https://link.springer.com/chapter/10.1007/978-3-642-12538-6_6}}</ref>
*[[萤火虫算法|螢火蟲算法]] (Firefly algorithm, FA) <ref name="Yang2010">{{cite book|author=Xin-She Yang|title=Nature-inspired Metaheuristic Algorithms|url=http://books.google.com/books?id=iVB_ETlh4ogC&pg=PR5|year=2010|publisher=Luniver Press|isbn=978-1-905986-28-6|pages=5–}}</ref>
*[[萤火虫算法|螢火蟲算法]] (Firefly algorithm, FA) <ref name="Yang2010">{{cite book|author=Xin-She Yang|title=Nature-inspired Metaheuristic Algorithms|url=http://books.google.com/books?id=iVB_ETlh4ogC&pg=PR5|year=2010|publisher=Luniver Press|isbn=978-1-905986-28-6|pages=5–}}</ref>
*[[猴群演算法]] (Monkey algorithm) <ref>{{Cite journal|title=Monkey algorithm for global numerical optimization|author=Ruiqing Zhao|url=https://www.researchgate.net/publication/228895335_Monkey_algorithm_for_global_numerical_optimization|journal=''Journal of Uncertain Systems''|issue=3|year=2008|volume=2|pages=165-176|author2=Wansheng Tang}}</ref>
*[[簡化群體演算法]] (Simplified swarm optimizatiom, SSO) <ref>{{cite journal |author1=Wei-Chang Yeh |title=An improved simplified swarm optimization |journal=''Knowledge-Based Systems'' |date=July 2015 |volume=82 |pages=60-69 |url=https://www.sciencedirect.com/science/article/pii/S0950705115000763?casa_token=nADHotZCIekAAAAA:TmenDw1paqhTmlbV8LW1zHnOOidhpD7aS4nqBYWipxjPrNh8bpoEPTLSpgMkVHTEp0WAo4BYM-Q}}</ref>
*[[獅子演算法]] (Lion optimization algorithm, LOA)<ref>{{Cite journal|title=Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm|url=https://doi.org/10.1016/j.jcde.2015.06.003|last=Yazdani|first=Maziar|last2=Jolai|first2=Fariborz|date=2016-01-01|journal=Journal of Computational Design and Engineering|issue=1|doi=10.1016/j.jcde.2015.06.003|volume=3|pages=24–36|issn=2288-5048}}</ref>
*[[調和搜尋演算法]] (Harmony search, HS) <ref>{{cite journal |author1=Geem, Z. W. |author2=Kim, J. H. |author3=Loganathan, G. V. |title=A new heuristic optimization algorithm: harmony search |journal=simulation |date=2001 |volume=76 |issue=2 |pages=60-68 |url=https://journals.sagepub.com/doi/abs/10.1177/003754970107600201}}</ref>
*[[猴群演算法]] (Monkey algorithm) <ref>{{Cite journal|author=Ruiqing Zhao|author2= Wansheng Tang |url= https://www.researchgate.net/publication/228895335_Monkey_algorithm_for_global_numerical_optimization |title= Monkey algorithm for global numerical optimization|journal= ''Journal of Uncertain Systems''|year= 2008 |volume= 2 |issue= 3 |pages= 165-176 }}</ref>
*[[人工蜂群演算法]] (Artificial bee colony, ABC)<ref>{{cite journal|title=An idea based on honey bee swarm for numerical optimization|url=https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.714.4934&rep=rep1&type=pdf|date=2005|journal=Technical report-tr06|volume=200|pages=1-10|author1=D Karaboga}}</ref>
*[[人工蜂群演算法]] (Artificial bee colony, ABC)<ref>{{cite journal |author1=D Karaboga |title=An idea based on honey bee swarm for numerical optimization |journal=Technical report-tr06 |date=2005 |volume=200 |pages=1-10 |url = https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.714.4934&rep=rep1&type=pdf}}</ref>
*[[病毒最佳化演算法]] (Virus Optimization Algorithm, VOA)<ref>{{cite journal|title=A novel metaheuristic for continuous optimization problems: Virus optimization algorithm|url=https://www.tandfonline.com/doi/abs/10.1080/0305215X.2014.994868|date=2016|journal=Engineering Optimization|issue=1|volume=48|pages=73-93|author1=Liang, Yun-Chia|coauthors=Josue Rodolfo Cuevas Juarez}}</ref>
*[[病毒最佳化演算法]] (Virus Optimization Algorithm, VOA)<ref>{{cite journal |author1=Liang, Yun-Chia |coauthors=Josue Rodolfo Cuevas Juarez |title=A novel metaheuristic for continuous optimization problems: Virus optimization algorithm |journal=Engineering Optimization |date=2016 |volume=48 |issue=1 |pages=73-93 |url=https://www.tandfonline.com/doi/abs/10.1080/0305215X.2014.994868}}</ref>
*[[飛蛾搜尋演算法]] (Moth search algorithm)<ref>{{Cite journal|title=Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems|url=https://doi.org/10.1007/s12293-016-0212-3|last=Wang|first=Gai-Ge|date=2018-06-01|journal=Memetic Computing|issue=2|doi=10.1007/s12293-016-0212-3|volume=10|pages=151–164|language=en|issn=1865-9292}}</ref>
*[[鯊魚氣味演算法]] (Shark smell optimization, SSO)<ref>{{Cite journal|title=A new metaheuristic algorithm based on shark smell optimization|url=https://onlinelibrary.wiley.com/doi/abs/10.1002/cplx.21634|last=Abedinia|first=Oveis|last2=Amjady|first2=Nima|date=2016|journal=Complexity|issue=5|doi=10.1002/cplx.21634|volume=21|pages=97–116|language=en|issn=1099-0526|last3=Ghasemi|first3=Ali}}</ref>
*[[蚯蚓最佳化演算法]] (Earthworm optimization algorithm, EWA)<ref>{{Cite journal|title=Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems|url=https://www.inderscienceonline.com/doi/abs/10.1504/IJBIC.2018.093328|last=Wang|first=Gai-Ge|last2=Deb|first2=Suash|date=2018-01-01|journal=International Journal of Bio-Inspired Computation|issue=1|doi=10.1504/IJBIC.2018.093328|volume=12|pages=1–22|issn=1758-0366|last3=Coelho|first3=Leandro Dos Santos}}</ref>
*[[帝王企鵝演算法]] (Emperor Penguins Colony, EPC)<ref>{{Cite journal|title=Emperor Penguins Colony: a new metaheuristic algorithm for optimization|url=https://doi.org/10.1007/s12065-019-00212-x|last=Harifi|first=Sasan|last2=Khalilian|first2=Madjid|date=2019-06-01|journal=Evolutionary Intelligence|issue=2|doi=10.1007/s12065-019-00212-x|volume=12|pages=211–226|language=en|issn=1864-5917|last3=Mohammadzadeh|first3=Javad|last4=Ebrahimnejad|first4=Sadoullah}}</ref>
*[[抹香鲸算法]] (Sperm whale algorithm,SWA)<ref>{{Cite journal|title=Sperm whale algorithm: An effective metaheuristic algorithm for production optimization problems|url=https://www.sciencedirect.com/science/article/pii/S1875510016300014|last=Ebrahimi|first=A.|last2=Khamehchi|first2=E.|date=2016-02-01|journal=Journal of Natural Gas Science and Engineering|doi=10.1016/j.jngse.2016.01.001|volume=29|pages=211–222|language=en|issn=1875-5100}}</ref>
*[[人類精神搜索]] (Human mental search, HMS)<ref>{{Cite journal|title=Human mental search: a new population-based metaheuristic optimization algorithm|url=https://doi.org/10.1007/s10489-017-0903-6|last=Mousavirad|first=Seyed Jalaleddin|last2=Ebrahimpour-Komleh|first2=Hossein|date=2017-10-01|journal=Applied Intelligence|issue=3|doi=10.1007/s10489-017-0903-6|volume=47|pages=850–887|language=en|issn=1573-7497}}</ref>
*[[海洋掠食者算法]] (Marine Predators Algorithm, MPA)<ref>{{Cite journal|title=Marine Predators Algorithm: A nature-inspired metaheuristic|url=https://www.sciencedirect.com/science/article/pii/S0957417420302025|last=Faramarzi|first=Afshin|last2=Heidarinejad|first2=Mohammad|date=2020-08-15|journal=Expert Systems with Applications|doi=10.1016/j.eswa.2020.113377|volume=152|pages=113377|language=en|issn=0957-4174|last3=Mirjalili|first3=Seyedali|last4=Gandomi|first4=Amir H.}}</ref>


== 參考文獻 ==
== 參考文獻 ==

2021年3月21日 (日) 17:36的版本

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

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] 啟發式算法獲得最佳解的方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. 
  5. ^ 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）. 
  6. ^ Yang, X. S.; Deb, S. Cuckoo search via Lévy flights. IEEE. 2009: 210–214. doi:10.1109/NABIC.2009.5393690. 
  7. ^ Yang, X. S. A New Metaheuristic Bat-Inspired Algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). 2010: 65–74. 
  8. ^ Xin-She Yang. Nature-inspired Metaheuristic Algorithms. Luniver Press. 2010: 5–. ISBN 978-1-905986-28-6. 
  9. ^ Ruiqing Zhao; Wansheng Tang. Monkey algorithm for global numerical optimization. Journal of Uncertain Systems. 2008, 2 (3): 165–176. 
  10. ^ 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. 
  11. ^ D Karaboga. An idea based on honey bee swarm for numerical optimization. Technical report-tr06. 2005, 200: 1–10. 
  12. ^ Liang, Yun-Chia; Josue Rodolfo Cuevas Juarez. A novel metaheuristic for continuous optimization problems: Virus optimization algorithm. Engineering Optimization. 2016, 48 (1): 73–93. 
  13. ^ 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）. 
  14. ^ 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）. 
  15. ^ 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. 
  16. ^ 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）. 
  17. ^ 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）. 
  18. ^ 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）. 
  19. ^ 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）. 
取自“https://zh.wikipedia.org/w/index.php?title=元启发算法&oldid=64868013”