Solving 0–1 Knapsack Problem using Cohort Intelligence Algorithm

被引：0
作者：
Anand J. Kulkarni
Hinna Shabir
机构：
[1] University of Windsor,Odette School of Business
[2] Maharashtra Institute of Technology,Optimization and Agent Technology (OAT) Research Lab
来源：
International Journal of Machine Learning and Cybernetics | 2016年 / 7卷
关键词：
Cohort Intelligence; Self Supervised Learning; Knapsack Problem; Combinatorial Optimization;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：427 / 441
页数：14
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