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Record Nr. |
UNINA9910254347003321 |
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Autore |
Kulkarni Anand Jayant |
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Titolo |
Cohort Intelligence: A Socio-inspired Optimization Method / / by Anand Jayant Kulkarni, Ganesh Krishnasamy, Ajith Abraham |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XI, 134 p. 29 illus.) |
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Collana |
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Intelligent Systems Reference Library, , 1868-4394 ; ; 114 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Artificial intelligence |
Computational Intelligence |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Introduction To Optimization -- Socio-Inspired Optimization Using Cohort Intelligence -- Cohort Intelligence For Constrained Test Problems -- Modified Cohort Intelligence For Solving Machine Learning Problems -- Solution To 0-1 Knapsack Problem Using Cohort Intelligence Algorithm -- Cohort Intelligence For Solving Travelling Salesman Problems -- Solution To A New Variant Of The Assignment Problem Using Cohort Intelligence Algorithm -- Solution To Sea Cargo Mix (Scm) Problem Using Cohort Intelligence Algorithm -- Solution To The Selection Of Cross-Border Shippers (Scbs) Problem -- Conclusions And Future Directions. |
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Sommario/riassunto |
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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 |
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