Editing Computational intelligence (section)

=== Swarm intelligence ===
Swarm intelligence is based on the collective behavior of decentralized, self-organizing systems, typically consisting of a population of simple agents that interact locally with each other and with their environment. Despite the absence of a centralized control structure that dictates how the individual agents should behave, local interactions between such agents often lead to the emergence of global behavior.<ref>{{Cite book |last1=Siddique |first1=N. H. |title=Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks, and Evolutionary Computing |last2=Adeli |first2=Hojjat |date=2013 |publisher=John Wiley & Sons |isbn=978-1-118-33784-4 |location=Chichester, West Sussex, UK |pages=7–11 |language=en |chapter=Swarm Intelligence}}</ref><ref>{{Cite book |last1=Kennedy |first1=James |title=Swarm Intelligence |last2=Eberhart |first2=Russell C. |last3=Shi |first3=Yuhui |date=2001 |publisher=Morgan Kaufmann |isbn=978-1-55860-595-4 |edition= |series=The Morgan Kaufmann Series in Artificial Intelligence |location=San Francisco |language=en |doi=10.1016/B978-1-55860-595-4.X5000-1}}</ref><ref>{{Cite book |last1=Bonabeau |first1=Eric |title=Swarm Intelligence: From Natural to Artificial Systems |last2=Dorigo |first2=Marco |last3=Theraulaz |first3=Guy |date=1999 |publisher=Oxford University Press |isbn=978-0-19-513158-1 |location=New York |language=en}}</ref> Among the recognized representatives of algorithms based on swarm intelligence are [[particle swarm optimization]] and [[Ant colony optimization algorithms|ant colony optimization]].<ref>{{Cite book |last=Engelbrecht |first=Andries P. |title=Computational Intelligence: An Introduction |date=2007 |publisher=John Wiley & Sons |isbn=978-0-470-03561-0 |edition=2nd |location=Chichester, England ; Hoboken, NJ |page=9 |language=en |chapter=Swarm Intelligence |oclc=133465571}}</ref> Both are [[metaheuristic]] optimization algorithms that can be used to (approximately) solve difficult [[Mathematical optimization|numerical]] or complex [[combinatorial optimization]] tasks.<ref>{{Cite journal |last=Poli |first=Riccardo |date=January 2008 |editor-last=Vanneschi |editor-first=Leonardo |title=Analysis of the Publications on the Applications of Particle Swarm Optimisation |url= |journal=Journal of Artificial Evolution and Applications |language=en |volume=2008 |issue=1 |doi=10.1155/2008/685175 |issn=1687-6229 |doi-access=free}}</ref><ref>{{Cite journal |last1=Bhavya |first1=Ravinder |last2=Elango |first2=Lakshmanan |date=2023-04-27 |title=Ant-Inspired Metaheuristic Algorithms for Combinatorial Optimization Problems in Water Resources Management |journal=Water |language=en |volume=15 |issue=9 |pages=1712 |doi=10.3390/w15091712 |issn=2073-4441 |doi-access=free|bibcode=2023Water..15.1712B }}</ref><ref>{{Cite book |url= |title=Applications of Ant Colony Optimization and its Variants: Case Studies and New Developments |date=2024 |publisher=Springer Nature Singapore |isbn=978-981-99-7226-5 |editor-last=Dey |editor-first=Nilanjan |series=Springer Tracts in Nature-Inspired Computing |location=Singapore |language=en |doi=10.1007/978-981-99-7227-2}}</ref> Since both methods, like the evolutionary algorithms, are based on a population and also on local interaction, they can be easily parallelized<ref>{{Citation |last1=Li |first1=Bo |title=Parallelizing particle swarm optimization |date=2005 |work=IEEE Pacific Rim Conference on Communications, Computers and signal Processing (PACRIM 2005) |pages=288–291 |url=https://ieeexplore.ieee.org/document/1517282 |publisher=IEEE |doi=10.1109/PACRIM.2005.1517282 |isbn=978-0-7803-9195-6 |last2=Wada |first2=Koishi|url-access=subscription }}</ref><ref>{{Cite journal |last1=Randall |first1=Marcus |last2=Lewis |first2=Andrew |date=September 2002 |title=A Parallel Implementation of Ant Colony Optimization |url=https://linkinghub.elsevier.com/retrieve/pii/S074373150291854X |journal=Journal of Parallel and Distributed Computing |language=en |volume=62 |issue=9 |pages=1421–1432 |doi=10.1006/jpdc.2002.1854|hdl=10072/6633 |hdl-access=free }}</ref> and show comparable learning properties.<ref>{{Cite journal |last1=Zheng |first1=Rui-zhao |last2=Zhang |first2=Yong |last3=Yang |first3=Kang |date=2022-05-23 |title=A transfer learning-based particle swarm optimization algorithm for travelling salesman problem |url=https://academic.oup.com/jcde/article/9/3/933/6590609 |journal=Journal of Computational Design and Engineering |language=en |volume=9 |issue=3 |pages=933–948 |doi=10.1093/jcde/qwac039 |issn=2288-5048|doi-access=free }}</ref><ref>{{Cite journal |last1=Xing |first1=Li-Ning |last2=Chen |first2=Ying-Wu |last3=Wang |first3=Peng |last4=Zhao |first4=Qing-Song |last5=Xiong |first5=Jian |date=June 2010 |title=A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems |url=https://linkinghub.elsevier.com/retrieve/pii/S156849460900194X |journal=Applied Soft Computing |language=en |volume=10 |issue=3 |pages=888–896 |doi=10.1016/j.asoc.2009.10.006|url-access=subscription }}</ref>