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Journal articles on the topic 'Swerm intelligensie'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Aziz, Nor Azlina Ab, Marizan Mubin, Zuwairie Ibrahim, and Sophan Wahyudi Nawawi. "Statistical Analysis for Swarm Intelligence — Simplified." International Journal of Future Computer and Communication 4, no. 3 (2015): 193–97. http://dx.doi.org/10.7763/ijfcc.2015.v4.383.

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2

Takanobu, Hideaki, Masumi Iida, Kenji Suzuki, Hirofumi Miura, Masanao Futakami, Tomohiro Endo, and Yoshinobu Inada. "Swarm Intelligence Robot : 3D swarm motion by airship and mobile robots." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 61–66. http://dx.doi.org/10.1299/jsmeicam.2010.5.61.

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3

Sharma, Pallavi, and Rajesh Kochher. "Enhanced RZ-Leach using Swarm Intelligence Technique." International Journal of Trend in Scientific Research and Development Volume-2, Issue-2 (February 28, 2018): 693–700. http://dx.doi.org/10.31142/ijtsrd8315.

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4

Issayeva, G. B. Issayeva, M. S. Ibraev, A. K. Koishybekova, B. R. Absatarova, A. A. Aitkazina, Sh P. Sh.P. Zhumagulova, N. Vodolazkina, and Z. M. Ibraeva. "SWARM INTELLIGENCE." EurasianUnionScientists 6, no. 8(77) (September 13, 2020): 9–13. http://dx.doi.org/10.31618/esu.2413-9335.2020.6.77.998.

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This report investigates this discipline that deals with natural and artificial systems. In the past few years there has been a lot of research on the application of swarm intelligence. A large number of algorithms have been used in different spheres of our life. In this paper we give an overview of this research area. We identify one of the algorithms of swarm intelligence systems and we show how it is used to solve problems. In other words, we present Bee Algorithms, a general framework in which most swarm intelligence algorithms can be placed. After that, we give an extensive solution of existing problem, discussing algorithm’s advantages and disadvantages. We conclude with an overview of future research directions that we consider important for the further development of this field.

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5

Wanka, Rolf. "Swarm intelligence." it - Information Technology 61, no. 4 (August 27, 2019): 157–58. http://dx.doi.org/10.1515/itit-2019-0034.

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6

Dorigo, Marco, and Mauro Birattari. "Swarm intelligence." Scholarpedia 2, no. 9 (2007): 1462. http://dx.doi.org/10.4249/scholarpedia.1462.

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7

Tarasewich, Peter, and Patrick R. McMullen. "Swarm intelligence." Communications of the ACM 45, no. 8 (August 2002): 62–67. http://dx.doi.org/10.1145/545151.545152.

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8

Eichmann, Christian, and Carsten Mueller. "Team Formation Based on Nature-Inspired Swarm Intelligence." Journal of Software 10, no. 3 (March 2015): 344–54. http://dx.doi.org/10.17706/jsw.10.3.344-354.

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9

Shi, Yuhui. "Developmental Swarm Intelligence." International Journal of Swarm Intelligence Research 5, no. 1 (January 2014): 36–54. http://dx.doi.org/10.4018/ijsir.2014010102.

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In this paper, the necessity of having developmental learning embedded in a swarm intelligence algorithm is confirmed by briefly considering brain evolution, brain development, brainstorming process, etc. Several swarm intelligence algorithms are looked at from developmental learning perspective. Finally, a framework of a developmental swarm intelligence algorithm is given to help understand developmental swarm intelligence algorithms, and to guide to design and/or implement any new developmental swarm intelligence algorithm and/or any developmental evolutionary algorithm.

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10

Chittka, L., and A. Mesoudi. "Insect Swarm Intelligence." Science 331, no. 6016 (January 27, 2011): 401–2. http://dx.doi.org/10.1126/science.1199780.

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11

S, Revathi, Aniz Rizwan, and Anusha N. "OPTIMIZATION OF LOAD BALANCING IN CLOUD USING SWARM INTELLIGENCE: A SURVEY." International Journal of Current Engineering and Scientific Research 6, no. 6 (June 2019): 169–74. http://dx.doi.org/10.21276/ijcesr.2019.6.6.29.

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12

Goodarzi, Mohammad. "Swarm Intelligence for Chemometrics." NIR news 26, no. 7 (November 2015): 7–11. http://dx.doi.org/10.1255/nirn.1556.

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13

Sadiku, Matthew N. O., Mahamadou Tembely, and Sarhan M. Musa. "Swarm Intelligence: A Primer." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 5 (June 2, 2018): 100. http://dx.doi.org/10.23956/ijarcsse.v8i5.681.

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Swarm intelligence is the emergent collective intelligence of groups of simple agents. It belongs to the emerging field of bio-inspired soft computing. It is inspired from the biological entities such as birds, fish, ants, wasps, termites, and bees. Bio-inspired computation is a field of study that is closely related to artificial intelligence. This paper provides a brief introduction to swarm intelligence.

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14

Vesilind, P. A. "Swarm intelligence [Book Review]." IEEE Technology and Society Magazine 21, no. 1 (2002): 9. http://dx.doi.org/10.1109/mtas.2002.993594.

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15

Bogue, Robert. "Swarm intelligence and robotics." Industrial Robot: An International Journal 35, no. 6 (October 17, 2008): 488–95. http://dx.doi.org/10.1108/01439910810909475.

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16

Hinchey, Michael G., Roy Sterritt, and Chris Rouff. "Swarms and Swarm Intelligence." Computer 40, no. 4 (April 2007): 111–13. http://dx.doi.org/10.1109/mc.2007.144.

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17

Rolling, James Haywood. "Swarm Intelligence and Collaboration." Art Education 69, no. 5 (August 15, 2016): 4–6. http://dx.doi.org/10.1080/00043125.2016.1201400.

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18

Yao, Baozhen, Rui Mu, and Bin Yu. "Swarm Intelligence in Engineering." Mathematical Problems in Engineering 2013 (2013): 1–3. http://dx.doi.org/10.1155/2013/835251.

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19

Zahiri, Seyed-Hamid, and Seyed-Alireza Seyedin. "Swarm intelligence based classifiers." Journal of the Franklin Institute 344, no. 5 (August 2007): 362–76. http://dx.doi.org/10.1016/j.jfranklin.2005.12.006.

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20

Jiao, Wenpin. "A Generic Model for Swarm Intelligence and Its Validations." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 18 (August 11, 2021): 116–30. http://dx.doi.org/10.37394/23209.2021.18.14.

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The modeling of emergent swarm intelligence constitutes a major challenge and it has been tacked in a number of different ways. However, existing approaches fail to capture the nature of swarm intelligence and they are either too abstract for practical application or not generic enough to describe the various types of emergence phenomena. In this paper, a contradiction-centric model for swarm intelligence is proposed, in which individuals determine their behaviors based on their internal contradictions whilst they associate and in-teract to update their contradictions. The model hypothesizes that 1) the emergence of swarm intelligence is rooted in the development of individuals’ internal contradictions and the interactions taking place between in-dividuals and the environment, and 2) swarm intelligence is essentially a combinative reflection of the configu-rations of individuals’ internal contradictions and the distributions of these contradictions across individuals. The model is formally described and five swarm intelligence systems are studied to illustrate its broad applica-bility. The studies confirm the generic character of the model and its effectiveness for describing the emergence of various kinds of swarm intelligence; and they also demonstrate that the model is straightforward to apply, without the need for complicated computations.

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21

Vehlken, Sebastian. "Pervasive Intelligence." Digital Culture & Society 4, no. 1 (March 1, 2018): 107–32. http://dx.doi.org/10.14361/dcs-2018-0108.

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Abstract This article seeks to situate collective or swarm robotics (SR) on a conceptual pane which on the one hand sheds light on the peculiar form of AI which is at play in such systems, whilst on the other hand it considers possible consequences of a widespread use of SR with a focus on swarms of Unmanned Aerial Systems (Swarm UAS). The leading hypothesis of this article is that Swarm Robotics create a multifold “spatial intelligence”, ranging from the dynamic morphologies of such collectives via their robust self-organization in changing environments to representations of these environments as distributed 4D-sensor systems. As is shown on the basis of some generative examples from the field of UAS, robot swarms are imagined to literally penetrate space and control it. In contrast to classical forms of surveillance or even “sousveillance”, this procedure could be called perveillance.

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22

KANG, QI, JING AN, LEI WANG, and QIDI WU. "UNIFICATION AND DIVERSITY OF COMPUTATION MODELS FOR GENERALIZED SWARM INTELLIGENCE." International Journal on Artificial Intelligence Tools 21, no. 03 (June 2012): 1240012. http://dx.doi.org/10.1142/s021821301240012x.

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Swarm intelligence is a kind of nature-inspired heuristic optimization technique. Different computation models usually take on relative uniform characteristic though they usually have distinct extrinsic forms. These intelligent algorithms are coupling with deterministic and stochastic, the contradiction between necessity and accidental unity, which promotes the "evolution" of the inheritance and the creative process: "stochastic" is adopted to give creative ability to the implemented "intelligent system", and a succession of "certainty" is acted to ensure the system is converging. In this paper, a computing framework of generalized swarm intelligence is proposed based on the unifying idea. The unified hiberarchy model and formalization description for swarm intelligence are represented. Several typical swarm intelligence algorithms, such as ant colony system (ACS), particle swarm optimization (PSO), estimation of distribution algorithms (EDA) and artificial immune algorithms (AIA) are addressed to validate the uniform idea of swarm intelligence, respectively.

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23

Weichert, Stephan. "From Swarm Intelligence to Swarm Malice: An Appeal." Social Media + Society 2, no. 1 (January 6, 2016): 205630511664056. http://dx.doi.org/10.1177/2056305116640560.

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24

CUI, ZHIHUA, ZHONGZHI SHI, and RAJAN ALEX. "EDITORIAL." International Journal on Artificial Intelligence Tools 21, no. 03 (June 2012): 1202002. http://dx.doi.org/10.1142/s0218213012020022.

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Swarm intelligence is an umbrella for amount optimization algorithms. This discipline deals with natural and artificial systems composed of many individuals that coordinate their activities using decentralized control and self-organization. In general, multi-agent systems that use some swarm intelligence are said to be swarm intelligent systems. They are mostly used as search engines and optimization tools. The goal of this special issue has been to offer a wide spectrum of sample works throughout the world about innovative methodologies of swarm intelligence. The issue should be useful both for beginners and experienced researchers in the field of computational intelligence.

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25

Al-Obaidi, Ahmed T. Sadiq, Hasanen S. Abdullah, and Zied O. Ahmed. "Meerkat Clan Algorithm: A New Swarm Intelligence Algorithm." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (April 1, 2018): 354. http://dx.doi.org/10.11591/ijeecs.v10.i1.pp354-360.

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<p>Evolutionary computation and swarm intelligence meta-heuristics are exceptional instances that environment has been a never-ending source of creativeness. The behavior of bees, bacteria, glow-worms, fireflies and other beings have stirred swarm intelligence scholars to create innovative optimization algorithms. This paper proposes the Meerkat Clan Algorithm (MCA) that is a novel swarm intelligence algorithm resulting from watchful observation of the Meerkat (Suricata suricatta) in the Kalahari Desert in southern Africa. This animal shows an exceptional intelligence, tactical organizational skills, and remarkable directional cleverness in its traversal of the desert when searching for food. A Meerkat Clan Algorithm (MCA) proposed to solve the optimization problems through reach the optimal solution by efficient way comparing with another swarm intelligence. Traveling Salesman Problem uses as a case study to measure the capacity of the proposed algorithm through comparing its results with another swarm intelligence. MCA shows its capacity to solve the Traveling Salesman’s Problem. Its dived the solutions group to sub-group depend of meerkat behavior that gives a good diversity to reach an optimal solution. Paralleled with the current algorithms for resolving TSP by swarm intelligence, it has been displayed that the size of the resolved problems could be enlarged by adopting the algorithm proposed here.</p>

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26

Xu, Ze Sheng, Zhi Feng Ma, Xin Wen Di, Tao Luo, Hong Yun Guo, and Bao Chen Niu. "The Study on Electric Power System Based on Swarm Intelligence." Advanced Materials Research 442 (January 2012): 424–29. http://dx.doi.org/10.4028/www.scientific.net/amr.442.424.

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In this paper, we introduce the swarm intelligence computation and its applications in power system. Because swarm intelligence does not need any precondition of centralized control and global model, it is very suitable to solve large scale power system nonlinear optimization problems which are hard to establish effective formalized models and difficult to be solved by traditional methods. In order to apply swarm intelligence better in power system, we propose two central research directions in the future: (1) The mathematical basis of swarm intelligence is unsubstantial and it lacks profound and pervasive theoretical analysis, so we must analysis its convergence and selection of parameters, especially the parameter selection of large scale power system optimization problems. (2) Because swarm intelligence is internally parallel, we should realize it based on the parallel computation theory. This work will also be helpful for the real-time need of power system.

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27

Sobecki, Janusz. "Comparison of Selected Swarm Intelligence Algorithms in Student Courses Recommendation Application." International Journal of Software Engineering and Knowledge Engineering 24, no. 01 (February 2014): 91–109. http://dx.doi.org/10.1142/s0218194014500041.

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In this paper a comparison of a few swarm intelligence algorithms applied in recommendation of student courses is presented. Swarm intelligence algorithms are nowadays successfully used in many areas, especially in optimization problems. To apply each swarm intelligence algorithm in recommender systems a special representation of the problem space is necessary. Here we present the comparison of efficiency of grade prediction of several evolutionary algorithms, such as: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weed Optimization (IWO), Bee Colony Optimization (BCO) and Bat Algorithm (BA).

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28

Xiao, Heng, and Toshiharu Hatanaka. "Model Selecting PSO-FA Hybrid for Complex Function Optimization." International Journal of Swarm Intelligence Research 12, no. 3 (July 2021): 215–32. http://dx.doi.org/10.4018/ijsir.2021070110.

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Swarm intelligence is inspired by natural group behavior. It is one of the promising metaheuristics for black-box function optimization. Then plenty of swarm intelligence algorithms such as particle swarm optimization (PSO) and firefly algorithm (FA) have been developed. Since these swarm intelligence models have some common properties and inherent characteristics, model hybridization is expected to adjust a swarm intelligence model for the target problem instead of parameter tuning that needs some trial and error approach. This paper proposes a PSO-FA hybrid algorithm with a model selection strategy. An event-driven trigger based on the personal best update makes each individual do the model selection that focuses on the personal study process. By testing the proposed hybrid algorithm on some benchmark problems and comparing it with a simple PSO, the standard PSO 2011, FA, HFPSO to show how the proposed hybrid swarm averagely performs well in black-box optimization problems.

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29

Худов, Г. В., and І. А. Хижняк. "Comparative assessment of the optical-electronic images segmentation quality by the ant colony optimization and the artificial bee colony." Системи обробки інформації, no. 1(164) (March 17, 2021): 104–13. http://dx.doi.org/10.30748/soi.2021.164.11.

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The article discusses the methods of swarm intelligence, namely, an improved method based on the ant colony optimization and the method of an artificial bee colony. The goal of the work is to carry out a comparative assessment of the optical-electronic images segmentation quality by the ant colony optimization and the artificial bee colony. Segmentation of tonal optical-electronic images was carried out using the proposed methods of swarm intelligence. The results of the segmentation of optical-electronic images obtained from the spacecraft are presented. A visual assessment of the quality of segmentation results was carried out using improved methods. The classical errors of the first and second kind of segmentation of optoelectronic images are calculated for the proposed methods of swarm intelligence and for known segmentation methods. The features of using each of the proposed methods of swarm intelligence are determined. The tasks for which it is better to use each of the proposed methods of swarm intelligence are determined.

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30

Keerthi, S., Ashwini K, and Vijaykumar M.V. "Survey Paper on Swarm Intelligence." International Journal of Computer Applications 115, no. 5 (April 22, 2015): 8–12. http://dx.doi.org/10.5120/20145-2273.

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31

INOUE, Kazuya, and Mariko SUZUKI. "PARAMETER OPTIMIZATION USING SWARM INTELLIGENCE." Journal of Japan Society of Civil Engineers, Ser. A2 (Applied Mechanics (AM)) 74, no. 2 (2018): I_33—I_44. http://dx.doi.org/10.2208/jscejam.74.i_33.

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32

Moya, Eugenio. "Swarm intelligence, política y verdad." IDP Revista de Internet Derecho y Política, no. 28 (February 17, 2019): 71–84. http://dx.doi.org/10.7238/idp.v0i28.3178.

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Este artículo critica el deferencialismo científico. Este está basado en una errónea comprensión de la ciencia, pero ha fundamentado las críticas actuales a la democracia como proceso de toma de decisiones colectivas. Platón, uno de los primeros en ver la democracia como problema, pensó que la ignorancia e irracionalidad de la mayoría justificaba el gobierno de los sabios. Brennan, en Against Democracy (2016), ha defendido lo mismo. El autor ofrece, por el contrario, una alternativa innovadora basada en una concepción falibilista del conocimiento y la democracia, según la cual la autoridad y legitimidad de la democracia no dependen de su tendencia a tomar soluciones acertadas, sino de la tendencia de las multitudes inteligentes en la era de la información a evitar decisiones equivocadas.

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33

Khaleel, Shahbaa. "Image Compression Using Swarm Intelligence." International Journal of Intelligent Engineering and Systems 14, no. 1 (February 28, 2021): 257–69. http://dx.doi.org/10.22266/ijies2021.0228.25.

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As a result of the development in multimedia technology and direct dealing with it in social media, it has led to interest in the techniques of compacting color images because of their importance at present. Since image compression enables the representation of color image data with the fewest number of bits, which reduces transmission time in the network and increases transmission speed. To ensure the compression process is performed without loss of data, the lossless compression methods are used because no data is lost during the compression process. In this research, a new system was presented to compress the color images with efficiency and high quality. Where the swarm intelligent methods were used, as well as hybridizing it with fuzzy using the Gustafson kessel fuzzy method to improve the clustering process and create new clustering methods with fuzzy swarm intelligence to obtain the best results. Swarm algorithms were used to perform the process of clustering the image data to be compressed and then obtaining a clustered data for this image data. In contrast, a lossless compression method was used to perform the encoding of this clustered data where the huffman method was used for encoding. Four methods were applied in this research to different color and lighting images. The PSO swarm intelligent was used, which in turn was hybridized with the Gustafson kessel fuzzy method to produce a new method for fuzzy particle swarm (FPSO), as well as the grey wolf optimization method GWO, which was hybridized with Gustafson kessel and obtained a new method, which is the fuzzy grey wolf optimizer FGWO, and the results were graded efficiently from the first to the fourth method, where the FGWO method with the huffman was the most efficient depending on the standards measurement that were calculated for all methods, the compression ratio was high in this new method, in addition to the standards of MSE, RMSE, PSNR, etc. among the important measurements of the compressing process.

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34

L, Meghana, and Jaya R. "Swarm Intelligence Algorithms - A Survey." International Journal of Computer Sciences and Engineering 6, no. 2 (February 28, 2018): 184–88. http://dx.doi.org/10.26438/ijcse/v6i2.184188.

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35

A., Nathaniel, and Eva N. "Malaria Diagnosis using Swarm Intelligence." International Journal of Computer Applications 181, no. 9 (August 14, 2018): 24–28. http://dx.doi.org/10.5120/ijca2018917602.

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36

Denby, B., and S. Le Hégarat-Mascle. "Swarm intelligence in optimisation problems." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 502, no. 2-3 (April 2003): 364–68. http://dx.doi.org/10.1016/s0168-9002(03)00444-3.

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37

Baluška, František, Simcha Lev-Yadun, and Stefano Mancuso. "Swarm intelligence in plant roots." Trends in Ecology & Evolution 25, no. 12 (December 2010): 682–83. http://dx.doi.org/10.1016/j.tree.2010.09.003.

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38

Engelbrecht, Andries, Xiaodong Li, Martin Middendorf, and Luca Maria Gambardella. "Editorial Special Issue: Swarm Intelligence." IEEE Transactions on Evolutionary Computation 13, no. 4 (August 2009): 677–80. http://dx.doi.org/10.1109/tevc.2009.2022002.

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39

Liu, Yu, Lei Zhao, Mingli Li, and Changfei Zhao. "Swarm intelligence for molecular docking." International Journal of Modelling, Identification and Control 18, no. 4 (2013): 357. http://dx.doi.org/10.1504/ijmic.2013.053541.

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40

Zhang, Yudong, Praveen Agarwal, Vishal Bhatnagar, Saeed Balochian, and Jie Yan. "Swarm Intelligence and Its Applications." Scientific World Journal 2013 (2013): 1–3. http://dx.doi.org/10.1155/2013/528069.

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41

Chen, Gang, and Fang Zong. "Swarm intelligence in transportation engineering." Advances in Mechanical Engineering 11, no. 3 (March 2019): 168781401984083. http://dx.doi.org/10.1177/1687814019840831.

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42

Yu, Bin, and Gang Chen. "Swarm intelligence in mechanical engineering." Advances in Mechanical Engineering 8, no. 12 (December 2016): 168781401668359. http://dx.doi.org/10.1177/1687814016683595.

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43

Nachamada, Blamah. "A Reflective Swarm Intelligence Algorithm." IOSR Journal of Computer Engineering 14, no. 4 (2013): 44–48. http://dx.doi.org/10.9790/0661-1444448.

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44

Sharkey, Amanda J. C. "Robots, insects and swarm intelligence." Artificial Intelligence Review 26, no. 4 (December 2006): 255–68. http://dx.doi.org/10.1007/s10462-007-9057-y.

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45

Yao, Baozhen, Fang Zong, Bin Yu, and Rui Mu. "Swarm Intelligence in Engineering 2014." Mathematical Problems in Engineering 2015 (2015): 1–4. http://dx.doi.org/10.1155/2015/858901.

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46

松野, 文俊. "群知能（Swarm Intelligence）." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 31, no. 6 (December 15, 2019): 175. http://dx.doi.org/10.3156/jsoft.31.6_175_2.

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47

Khairuddin, Ismail Mohd, Amira Sarayati Ahmad Dahalan, Amar Faiz Zainal Abidin, Yee Yang Lai, Nur Anis Nordin, Siti Fatimah Sulaiman, Hazriq Izzuan Jaafar, Syahrul Hisham Mohamad, and Noor Hafizah Amer. "Modeling and Simulation of Swarm Intelligence Algorithms for Parameters Tuning of PID Controller in Industrial Couple Tank System." Advanced Materials Research 903 (February 2014): 321–26. http://dx.doi.org/10.4028/www.scientific.net/amr.903.321.

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Industrial tank system is widely used in consumer liquid processing and chemical processing industry. In liquid-based product manufacturing system, one of the main components consists of an industrial tank. This paper explores the applications of two swarm intelligence algorithms in optimizing the PID controller parameters. These swarm intelligence algorithms are Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). Each agent of the swarm intelligence will represent a possible solution of the problem where each dimension corresponds to the PID controllers parameters. Result obtained shows that there are potential in improving these algorithms to replace the conventional way of obtaining PID controllers parameters

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48

Kutsenok, Alex, and Victor Kutsenok. "Swarm AI: A General-purpose Swarm Intelligence Design Technique." Design Principles and Practices: An International Journal—Annual Review 5, no. 1 (2011): 7–16. http://dx.doi.org/10.18848/1833-1874/cgp/v05i01/37798.

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49

Apostolidis, Georgios K., and Leontios J. Hadjileontiadis. "Swarm decomposition: A novel signal analysis using swarm intelligence." Signal Processing 132 (March 2017): 40–50. http://dx.doi.org/10.1016/j.sigpro.2016.09.004.

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50

Yang, Xin-She. "Diversity and Mechanisms in Swarm Intelligence." International Journal of Swarm Intelligence Research 5, no. 2 (April 2014): 1–12. http://dx.doi.org/10.4018/ijsir.2014040101.

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Swarm intelligence based algorithms such as particle swarm optimization have become popular in the last two decades. Various new algorithms such as cuckoo search and bat algorithm also show promising efficiency. In all these algorithms, it is essential to maintain the balance of exploration and exploitation by controlling directly and indirectly the diversity of the population. Different algorithms may use different mechanisms to control such diversity. In this review paper, the author reviews and analyzes the roles of diversity and relevant mechanisms in swarm intelligence. The author also discuss parameter tuning and parameter control. In addition, the author highlights some key open questions in swarm intelligence.

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