Abstract
The knapsack problem is a famous example of combinatorial optimization where it is required to maximize the value of the items in a knapsack subject to the maximum total capacity. These problems were handled by several classical approaches but they were unable to produce exact solutions in polynomial time. This task could be efficiently accomplished using heuristic algorithms. Cohort intelligence algorithm is inspired from the natural inclination toward observing and following the behavior of other individuals to learn from each other. The basic version of this algorithm along with feasibility-based rules was applied for the solution of 0–1 knapsack problem. The objective of the present study is to solve its small size variation using the same algorithm, but with an educated approach for the selection of the candidates. Optimal solution of each candidate is achieved by execution of different conditions of the approach.
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Sapre, M.S., Patel, H., Vaishnani, K., Thaker, R., Shastri, A.S. (2019). Solution to Small Size 0–1 Knapsack Problem Using Cohort Intelligence with Educated Approach. In: Kulkarni, A.J., Singh, P.K., Satapathy, S.C., Husseinzadeh Kashan, A., Tai, K. (eds) Socio-cultural Inspired Metaheuristics. Studies in Computational Intelligence, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-13-6569-0_7
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DOI: https://doi.org/10.1007/978-981-13-6569-0_7
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