Solution to 0–1 Knapsack Problem Using Cohort Intelligence Algorithm

The previous chapters discussed the algorithm Cohort Intelligence (CI) and its applicability solving several unconstrained and constrained problems. In addition CI was also applied for solving several clustering problems. This validated the learning and self supervising behavior of the cohort. This...

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Published inCohort Intelligence: A Socio-inspired Optimization Method Vol. 114; pp. 55 - 74
Main Authors Krishnasamy, Ganesh, Kulkarni, Anand Jayant, Abraham, Ajith
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 01.01.2017
Springer International Publishing
SeriesIntelligent Systems Reference Library
Subjects
Artificial intelligence
Harmony Search
Harmony Search Algorithm
Knapsack Problem
Nursing Personnel
Problem Size
Online AccessGet full text
ISBN3319442538
9783319442532
ISSN1868-4394
1868-4408
DOI10.1007/978-3-319-44254-9_5

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Summary:The previous chapters discussed the algorithm Cohort Intelligence (CI) and its applicability solving several unconstrained and constrained problems. In addition CI was also applied for solving several clustering problems. This validated the learning and self supervising behavior of the cohort. This chapter further tests the ability of CI by solving an NP-hard combinatorial problem such as Knapsack Problem (KP). Several cases of the 0–1 KP are solved. The effect of various parameters on the solution quality has been discussed. The advantages and limitations of the CI methodology are also discussed.
ISBN:3319442538
9783319442532
ISSN:1868-4394
1868-4408
DOI:10.1007/978-3-319-44254-9_5