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Introduction to Advanced Manufacturing Processes and Optimization Methodologies Read chapter Read first chapter

2021 | OriginalPaper | Chapter

3. Multi Cohort Intelligence Algorithm

Authors : Apoorva Shastri, Aniket Nargundkar, Anand J. Kulkarni

Published in: Socio-Inspired Optimization Methods for Advanced Manufacturing Processes

Publisher: Springer Singapore

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Abstract

Multi-Cohort Intelligence (Multi-CI) algorithm has been proposed by Shastri and Kulkarni in [14]. The algorithm implements intra-group and inter-group learning mechanisms. It focuses on the interaction amongst different cohorts. The performance of the algorithm was validated by solving 75 unconstrained test problems with dimensions up to 30.

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previous chapter A Brief Review of Socio-inspired Metaheuristics
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Literature
1.
Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219:8121–8144 MathSciNetMATH
2.
Geem ZW, Kim JH, Loganathan GV (2001) A new heuristicoptimization algorithm: harmony search. Simulation 76(2):60–68
3.
Igel C, Hansen N, Roth S (2007) Covariance matrix adaptationfor multi-objective optimization. Evol Comput 15(1):1–28
4.
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132 MathSciNetMATH
5.
Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39:459–471 MathSciNetMATH
6.
Kulkarni AJ, Baki MF, Chaouch BA (2016) Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems. Eur J Oper Res 250(2):427–447 MathSciNetMATH
7.
Kulkarni AJ, Durugkar IP, Kumar M (2013) Cohort intelligence: a self-supervised learning behavior. In IEEE international conference on systems, man, and cybernetics (SMC). pp 1396–1400
8.
Li X, Yao X (2012) Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans Evolut Comput 16(2):210–224
9.
Liu L, Zhong WM, Qian F (2010) An improved chaos-particle swarm optimization algorithm. J East China Univ Sci Technol (Natural Science Edition) 36:267–272
10.
Murugan R, Mohan MR (2012) Modified artificial bee colony algorithm for solving economic dispatch problem. ARPN J Eng ApplSci 7(10):1353–1366
11.
12.
Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. IEEE Trans Evolut Comput 1(3):1785–1791
13.
Selvi V, Umarani R (2010) Comparative analysis of ant colony and particle swarm optimization techniques. Int J Comput Appl 5(4):975–8887
14.
Shastri AS, Kulkarni AJ (2018) Multi-cohort intelligence algorithm: an intra-and inter-group learning behaviour based socio-inspired optimization methodology. Int J Parallel Emergent Distrib Syst 33(6):675–715
15.
16.
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11:341–359
17.
Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria forthe CEC 2005 special session on real-parameter optimization. Technical Report 1–50
18.
Metadata
Title
Multi Cohort Intelligence Algorithm
Authors
Apoorva Shastri
Aniket Nargundkar
Anand J. Kulkarni
Copyright Year
2021
Publisher
Springer Singapore
Book
Socio-Inspired Optimization Methods for Advanced Manufacturing Processes

Print ISBN: 978-981-15-7796-3

Electronic ISBN: 978-981-15-7797-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-7797-0_3

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