Cohort Intelligence: A Socio-inspired Optimization Method by Anand Jayant Kulkarni Ganesh Krishnasamy & Ajith Abraham

Author:Anand Jayant Kulkarni, Ganesh Krishnasamy & Ajith Abraham
Language: eng
Format: epub
Publisher: Springer International Publishing, Cham

2.2.3.If there are both infeasible and feasible variations then the feasible variation with maximum profit is selected.

This makes the cohort available with C updated profits .

This process continues until saturation (convergence) i.e., every candidate has the same profit and it does not change for successive considerable number of learning attempts.

The above discussed procedure of solving the KP using CI algorithm is illustrated here with number of objects and knapsack capacity . The weights and profits associated with every object are illustrated in Fig. 5.2. Furthermore, the cohort is assumed to have three candidates, i.e. and number of variations .

Fig. 5.2Illustrative 0–1 KP example with

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