Abstract
Cohort intelligence is a socio-inspired self-organizing system that includes inherent, self-realized, and rational learning with self-control and ability to avoid obstacles (jumps out of ditches/local solutions), inherent ability to handle constraints, uncertainty by modular and scalable system and robust (immune to single point failure). In this method, a candidate self-supervises his/her behavior and adapts to the behavior of another better candidate, thus ultimately improving the behavior of the whole cohort. Selective assembly is a cost-effective approach to attaining necessary clearance variation in the resultant assembled product from the low precision elements. In this paper, the above-mentioned approach is applied to a problem of hole and shaft assemblies where the objective is to minimize the clearance variation and computational time. The algorithm was coded and run in MATLAB R2016b environment, and we were able to achieve convergence in less number of iterations and computational time compared to the other algorithms previously used to solve this problem.
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References
Chan KC, Linn RJ (1998) A grouping method for selective assembly of parts of dissimilar distributions. Qual Eng 11(2):221–234
Dhavle SV, Kulkarni AJ, Shastri A, Kale IR (2016) Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithm. Neural Comput Appl 1–15
Fang XD, Zhang Y (1995) A new algorithm for minimising the surplus parts in selective assembly. Comput Ind Eng 28(2):341–350
Kannan SM, Jayabalan V (2001) A new grouping method to minimize surplus parts in selective assembly for complex assemblies. Int J Prod Res 39(9):1851–1863
Kannan SM, Jayabalan V (2002) A new grouping method for minimizing the surplus parts in selective assembly. Qual Eng 14(1):67–75
Kannan SM, Asha A, Jayabalan V (2005) A new method in selective assembly to minimize clearance variation for a radial assembly using genetic algorithm. Qual Eng 17(4):595–607
Kannan SM, Jayabalan V, Jeevanantham K (2003) Genetic algorithm for minimizing assembly variation in selective assembly. Int J Prod Res 41(14):3301–3313
Krishnasamy G, Kulkarni AJ, Paramesran R (2014) A hybrid approach for data clustering based on modified cohort intelligence and K-means. Expert Syst Appl 41(13):6009–6016
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
Kulkarni AJ, Krishnasamy G, Abraham A (2017) Cohort intelligence: a socio-inspired optimization method. Intelligent Systems Reference Library, vol 114. https://doi.org/10.1007/978-3-319-44254-9
Kulkarni O, Kulkarni N, Kulkarni AJ, Kakandikar G (2016) Constrained cohort intelligence using static and dynamic penalty function approach for mechanical components design. Int J Parallel Emergent Distrib Syst 1–19
Kulkarni AJ, Shabir H (2016) Solving 0–1 knapsack problem using cohort intelligence algorithm. Int J Mach Learn Cybernet 7(3):427–441
Kulkarni AJ, Durugkar IP, Kumar M (2013) Cohort intelligence: a self-supervised learning behavior. In: 2013 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp. 1396–1400 (October)
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
Mansoor EM (1961) Selective assembly—its analysis and applications. Int J Prod Res 1(1):13–24
Patankar NS, Kulkarni AJ (2017) Variations of cohort intelligence. Soft Comput 1–17
Pugh GA (1986) Partitioning for selective assembly. Comput Ind Eng 11(1–4):175–179
Pugh GA (1992) Selective assembly with components of dissimilar variance. Comput Ind Eng 23(1–4):487–491
Sarmah DK, Kulkarni AJ (2017) Image Steganography Capacity Improvement Using Cohort Intelligence and Modified Multi-random start local search methods. Arab J Sci Eng 1–24
Shah P, Agashe S, Kulkarni AJ (2017) Design of fractional PID controller using cohort intelligence method. Front Inf Technol Electron, Eng
Shastri AS, Jadhav PS, Kulkarni AJ, Abraham A (2016) Solution to constrained test problems using cohort intelligence algorithm. In Innovations in bio-inspired computing and applications. Springer International Publishing, pp 427–435
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Nair, V.H., Acharya, V., Dhavle, S.V., Shastri, A.S., Patel, J. (2019). Minimization of Clearance Variation of a Radial Selective Assembly Using Cohort Intelligence Algorithm. In: Kulkarni, A., Satapathy, S., Kang, T., Kashan, A. (eds) Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-13-1610-4_17
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DOI: https://doi.org/10.1007/978-981-13-1610-4_17
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