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

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