Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

P. Agrawal, T. Ganesh, A.W. Mohamed

Complex & Intelligent Systems Vol. 8, nº. 1, pp. 43 - 63

Summary:

This article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

Keywords: Gaining sharing knowledge-based optimization algorithm · 0–1 Knapsack problem · Population reduction technique · Metaheuristic algorithms · Binary variables

JCR Impact Factor and WoS quartile: 5,800 - Q2 (2022)

DOI reference: https://doi.org/10.1007/s40747-021-00351-8

Published on paper: February 2022.

Published on-line: April 2021.

Citation:
P. Agrawal, T. Ganesh, A.W. Mohamed, Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm. Complex & Intelligent Systems. Vol. 8, nº. 1, pp. 43 - 63, February 2022. [Online: April 2021]