Differential evolution (DE) is a powerful global optimization algorithm which has been studied intensively by many researchers in recent years. A number of mutation variants have been established for this… Click to show full abstract
Differential evolution (DE) is a powerful global optimization algorithm which has been studied intensively by many researchers in recent years. A number of mutation variants have been established for this algorithm. These mutation variants make the DE algorithm more applicable, but random development of these variants has created inconsistencies such as naming and formulation. Hence this study aims to identify inconsistencies and to propose solutions to make them consistent. Most of the inconsistencies exist because of the uncommon nomenclature used for these variants. In this study, a comprehensive study is carried out to identify inconsistencies in the nomenclature of mutation variants that do not match each other. Appropriate and consistent names are proposed for them. The proposed names assigned to conflicting variants are based on the name of the variant, the total number of vectors used to generate the trial vector, and the order of the vectors to form the equation of these mutation variants. To ensure the performance diversity of the consistent set of DE mutation strategies, experimental results are generated using a test suit of benchmark functions.
               
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