Original Article

International Journal of Machine Learning and Cybernetics

pp 1-15

First online:

Solving 0–1 Knapsack Problem using Cohort Intelligence Algorithm

  • Anand J. KulkarniAffiliated withOdette School of Business, University of WindsorOptimization and Agent Technology (OAT) Research Lab, Maharashtra Institute of Technology Email author 
  • , Hinna ShabirAffiliated withOptimization and Agent Technology (OAT) Research Lab, Maharashtra Institute of Technology

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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.


Cohort Intelligence Self Supervised Learning Knapsack Problem Combinatorial Optimization