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.

本文提出了一种新近开发的基于增益共享知识的优化算法（GSK）的二进制版本来解决二进制优化问题。GSK算法基于人类在生命周期中如何获取和共享知识的概念。GSK的二进制版本称为新颖的基于知识共享的二进制增益共享优化算法（NBGSK），主要取决于两个二进制阶段：具有知识因子1的二进制初次共享阶段和二进制高级共享阶段。这两个阶段使NBGSK能够进行探索和利用搜索空间的高效和有效地解决了二进制空间中的问题。此外，为了提高NBGSK的性能并防止解决方案陷入局部最优状态，引入了人口规模减小的NBGSK（PR-NBGSK）。它通过线性函数逐渐减小人口规模。拟议中的NBGSK和PR-NBGSK应用于具有小尺寸和大尺寸的背包实例集合，这表明NBGSK和PR-NBGSK在收敛性，鲁棒性和准确性方面更加有效。