Mohamed A.W.Hadi A.A.Mohamed A.K.Operations Research DepartmentFaculty of Graduate Studies for Statistical�ResearchCairo UniversityGiza12613Egypt; Wireless Intelligent Networks Center (WINC)School of Engineering and Applied SciencesNile UniversityGizaEgypt; College of Computing and Information TechnologyKing Abdulaziz UniversityP. O. Box 80200Jeddah21589Saudi Arabia; Department of Computer ScienceFaculty of Computer ScienceOctober University for Modern Sciences and Arts (MSA)6th October CityGiza12451Egypt2020-01-092020-01-09201918688071https://doi.org/10.1007/s13042-019-01053-xPubMed ID :https://t.ly/j6rpJScopusThis paper proposes a novel nature-inspired algorithm called Gaining Sharing Knowledge based Algorithm (GSK) for solving optimization problems over continuous space. The GSK algorithm mimics the process of gaining and sharing knowledge during the human life span. It is based on two vital stages, junior gaining and sharing phase and senior gaining and sharing phase. The present work mathematically models these two phases to achieve the process of optimization. In order to verify and analyze the performance of GSK, numerical experiments on a set of 30 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions. Besides, the GSK algorithm has been applied to solve the set of real world optimization problems proposed for the IEEE-CEC2011 evolutionary algorithm competition. A comparison with 10 state-of-the-art and recent metaheuristic algorithms are executed. Experimental results indicate that in terms of robustness, convergence and quality of the solution obtained, GSK is significantly better than, or at least comparable to state-of-the-art approaches with outstanding performance in solving optimization problems especially with high dimensions. � 2019, Springer-Verlag GmbH Germany, part of Springer Nature.EnglishEvolutionary computationGlobal optimizationMeta-heuristicsNature-inspired algorithmsPopulation-based algorithmBenchmarkingBiomimeticsGlobal optimizationKnowledge based systemsMeta heuristic algorithmMeta heuristicsNature inspired algorithmsNumerical experimentsOptimization problemsPopulation-based algorithmReal-world optimizationState-of-the-art approachEvolutionary algorithmsArticlehttps://doi.org/10.1007/s13042-019-01053-xPubMed ID :