Differential evolution (DE) is an evolutionary algorithm that is straightforward but effective and has been satisfactorily implemented in a variety of fields. However, the DE relies heavily on the mutation strategy, and utilizing different mutation strategies can boost DE’s performance at different phases during the evolution. This paper proposes a DE variant with the strategy transformation (qSTDE) based on a quartile. The proposed qSTDE uses feedback information from individuals and the entire population distribution to determine the position ofmutation strategy transformation, balancing theDE’s exploration and exploitation capabilities. Individuals are first ranked in accordance with their fitness values, and then the ranked population is separated into four sub-populations using quartile points. Next, the Euclidean distances between the best individual and four individuals that are randomly selected from the four sub-populations are calculated, as well as their Euclidean distances to the searching space ratios. Further, the evolution process is completely separated into two phases, and the transformation position is determined through the comparison of ratios for a given threshold. Finally, different mutation strategies are used in different stages depending on the distinction between the two stages and the transformation position. The proposed qSTDE is evaluated on CEC2005 and CEC2014 benchmark test functions. The experimental results show that the qSTDE is efficient and competitive contrasted to the state-of-the-art DE variants.
Differential evolution with the mutation strategy transformation based on a quartile for numerical optimization
Qiao H.
2024-01-01
Abstract
Differential evolution (DE) is an evolutionary algorithm that is straightforward but effective and has been satisfactorily implemented in a variety of fields. However, the DE relies heavily on the mutation strategy, and utilizing different mutation strategies can boost DE’s performance at different phases during the evolution. This paper proposes a DE variant with the strategy transformation (qSTDE) based on a quartile. The proposed qSTDE uses feedback information from individuals and the entire population distribution to determine the position ofmutation strategy transformation, balancing theDE’s exploration and exploitation capabilities. Individuals are first ranked in accordance with their fitness values, and then the ranked population is separated into four sub-populations using quartile points. Next, the Euclidean distances between the best individual and four individuals that are randomly selected from the four sub-populations are calculated, as well as their Euclidean distances to the searching space ratios. Further, the evolution process is completely separated into two phases, and the transformation position is determined through the comparison of ratios for a given threshold. Finally, different mutation strategies are used in different stages depending on the distinction between the two stages and the transformation position. The proposed qSTDE is evaluated on CEC2005 and CEC2014 benchmark test functions. The experimental results show that the qSTDE is efficient and competitive contrasted to the state-of-the-art DE variants.
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