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Titlebook: Cohort Intelligence: A Socio-inspired Optimization Method; Anand Jayant Kulkarni,Ganesh Krishnasamy,Ajith Abr Book 2017 Springer Internati

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發(fā)表于 2025-3-21 18:29:33 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Cohort Intelligence: A Socio-inspired Optimization Method
編輯Anand Jayant Kulkarni,Ganesh Krishnasamy,Ajith Abr
視頻videohttp://file.papertrans.cn/230/229269/229269.mp4
概述Presents the core and underlying principles and analysis of the different concepts associated with an emerging socio-inspired AI optimization tool referred to as Cohort Intelligence (CI).Discusses in
叢書名稱Intelligent Systems Reference Library
圖書封面Titlebook: Cohort Intelligence: A Socio-inspired Optimization Method;  Anand Jayant Kulkarni,Ganesh Krishnasamy,Ajith Abr Book 2017 Springer Internati
描述.This Volume discusses the underlying principles and analysis of the different concepts associated with an emerging socio-inspired optimization tool referred to as Cohort Intelligence (CI). CI algorithms have been coded in Matlab and are freely available from the link provided inside the book. The book demonstrates the ability of CI methodology for solving combinatorial problems such as Traveling Salesman Problem and Knapsack Problem in addition to real world applications from the healthcare, inventory, supply chain optimization and Cross-Border transportation. The inherent ability of handling constraints based on probability distribution is also revealed and proved using these problems...?.
出版日期Book 2017
關(guān)鍵詞Intelligent Systems; Computational Intelligence; Cohort Intelligence Methodology; Socio-inspired Optimi
版次1
doihttps://doi.org/10.1007/978-3-319-44254-9
isbn_softcover978-3-319-83022-3
isbn_ebook978-3-319-44254-9Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
copyrightSpringer International Publishing Switzerland 2017
The information of publication is updating

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發(fā)表于 2025-3-21 22:26:30 | 只看該作者
Socio-Inspired Optimization Using Cohort Intelligence,ulated annealing (SA), Tabu search, etc., have become popular due to their simplicity to implement and working based on rules. The GA is population based which is evolved using the operators such as selection, crossover, mutation, etc. According to Deb (Methods Appl Mech Eng, 186:311–338, 2000, [.])
板凳
發(fā)表于 2025-3-22 03:49:24 | 只看該作者
Cohort Intelligence for Constrained Test Problems,nstrained problems. There are a several traditional methods available such as feasibility-based methods, gradient projection method, reduced gradient method, Lagrange multiplier method, aggregate constraint method, feasible direction based method, penalty based method, etc. (Kulkarni and Tai in Int
地板
發(fā)表于 2025-3-22 06:47:40 | 只看該作者
Modified Cohort Intelligence for Solving Machine Learning Problems, more similar to each another than objects in the different cluster according to certain predefined criteria. K-means is simple yet an efficient method used in data clustering. However, K-means has a tendency to converge to local optima and depends on initial value of cluster centers. In the past, m
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Solution to a New Variant of the Assignment Problem Using Cohort Intelligence Algorithm,, 2016, [1]). The model has applications in healthcare systems and inventory management. The problem stems from an application in healthcare management. Specifically, a surgical scheduling in a hospital setting is a complex combinatorial problem. In addition, similar problem arises in minimizing the
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Conclusions and Future Directions,vation of the methodology is also discussed in detail. The methodology was successfully tested and validated by solving several unconstrained problems with different modalities and dimensions. The solution quality was quite promising and encouraging in terms of objective function, robustness, avoida
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發(fā)表于 2025-3-23 07:57:48 | 只看該作者
Socio-Inspired Optimization Using Cohort Intelligence,an communicate with one another either directly or indirectly. The techniques such as Particle Swarm Optimization (PSO) is inspired from the social behavior of bird flocking and school of fish searching for food.
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