الفهرس 
A novel differential evolution algorithm for solving optimization problems
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Abstract
IIn this study, the unconstrained, constrained deterministic and mixed integer global optimization problems have been studied. On the other hand, finding more accurate solution(s) in a shorter period of time for these complex problems is the main goal of all evolutionary algorithms and is considered as a challenging task. Therefore, in order to achieve the above goal, the new concept of learning, balancing and adapting of the best as well as the worst has been proposed. To the best of our knowledge, although this concept is already existing and well known in our daily life and in all sciences, that is the first time that this concept is adopted and utilized to enhance and to accelerate population-based algorithms. Based on the new concept, four new alternative and modified versions of metaheuristics are proposed as promising solvers for the considered problems. Finally, numerical experiments and comparisons on a set of well-known benchmark functions indicate that the new and improved algorithms outperform and are superior to other existing algorithms in terms of final solution quality, success rate, convergence rate, and robustness