A new parameter setting-based modified differential evolution for function optimization

The population-based efficient iterative evolutionary algorithm (EA) is differential evolution (DE). It has fewer control parameters but is useful when dealing with complex problems of optimization in the real world. A great deal of progress has already been made and implemented in various fields of engineering and science. Nevertheless, DE is prone to the setting of control parameters in its performance evaluation. Therefore, the appropriate adjustment of the time-consuming control parameters is necessary to achieve optimal DE efficiency. This research proposes a new version of the DE algorithm control parameters and mutation operator. For the justifiability of the suggested method, several benchmark functions are taken from the literature. The test results are contrasted with other literary algorithms.