International Journal of Machine Learning and Cybernetics

, Volume 7, Issue 3, pp 427–441

Solving 0–1 Knapsack Problem using Cohort Intelligence Algorithm

Original Article

DOI: 10.1007/s13042-014-0272-y

Cite this article as:
Kulkarni, A.J. & Shabir, H. Int. J. Mach. Learn. & Cyber. (2016) 7: 427. doi:10.1007/s13042-014-0272-y

Abstract

An emerging technique, inspired from the natural and social tendency of individuals to learn from each other referred to as Cohort Intelligence (CI) is presented. Learning here refers to a cohort candidate’s effort to self supervise its own behavior and further adapt to the behavior of the other candidate which it intends to follow. This makes every candidate improve/evolve its behavior and eventually the entire cohort behavior. This ability of the approach is tested 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.

Keywords

Cohort IntelligenceSelf Supervised LearningKnapsack ProblemCombinatorial Optimization

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Odette School of BusinessUniversity of WindsorWindsorCanada
  2. 2.Optimization and Agent Technology (OAT) Research LabMaharashtra Institute of TechnologyPuneIndia