Mohamed, A.KHadi, A.AMohamed, A.W2020-12-272020-12-2710/26/202010.1109/NILES50944.2020.9257924http://repository.msa.edu.eg/xmlui/handle/123456789/4270ScopusThe effort devoted in introducing new numerical optimization benchmarks has attracted the attention to develop new optimization algorithms to solve them. Very recently, a new suite on bound constrained optimization problems is proposed as a new addition to CEC benchmark series. Differential Evolution (DE) is a simple Evolutionary Algorithm (EA) which shows superior performance to solve many CEC benchmark during the past years. This paper presents a new extension to DE algorithm through extending the line of research for AGDE algorithm. The new algorithm, which we name GADE, enhanced the DE algorithm by introducing a generalized adaptive framework for enhancing the performance of DE. Numerical experiments on a set of 10 test problems from the CEC2020 benchmarks for 5, 10, 15 and 20 dimensions, including a comparison with state-of-the-art algorithm are executed. Comparative analysis indicates that GADE is superior to other state-of-the-art algorithms in terms of stability, robustness, and quality of solution. © 2020 IEEE.en-USuniversitydifferential evolutionevolutionary algorithmsnumerical optimizationoptimizationArticle