A sinusoidal differential evolution algorithm for numerical optimisation

Graphical abstractDisplay Omitted HighlightsThe proposed SinDE uses sine-based formulas to define the DE parameter values.The proposed SinDE outperformed state-of the-art metaheuristics.28 benchmark functions have been used for validating the proposed approach.The proposed SinDE achieved very good results. It reduces the number of parameters. This paper presents a new variant of the Differential Evolution (DE) algorithm called Sinusoidal Differential Evolution (SinDE). The key idea of the proposed SinDE is the use of new sinusoidal formulas to automatically adjust the values of the DE main parameters: the scaling factor and the crossover rate. The objective of using the proposed sinusoidal formulas is the search for a good balance between the exploration of non visited regions of the search space and the exploitation of the already found good solutions. By applying it on the recently proposed CEC-2013 set of benchmark functions, the proposed approach is statistically compared with the classical DE, the linearly parameter adjusting DE and 10 other state-of-the-art metaheuristics. The obtained results have proven the superiority of the proposed SinDE, it outperformed other approaches especially for multimodal and composition functions.

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