Abstract
In order to balance the exploration and exploitation ability of differential evolution (DE), different mutation strategy for different evolutionary stages may be effective. An adaptive differential evolution with information entropy-based mutation strategy (DEIE) is proposed to divide the evolutionary process reasonably. In DEIE, the number of Markov states deduced from the crowding strategy is determined first and then the transition matrix between states is inferred from the historical evolutionary information. Based on the above-mentioned knowledge, the Markov state model is constructed. The evolutionary process is divided into exploration and exploitation stages dynamically using the information entropy derived from the Markov state model. Consequently, stage-specific mutation operation is employed adaptively. Experiments are conducted on CEC 2013, 2014, and 2017 benchmark sets and classical benchmark functions to assess the performance of DEIE. Moreover, the proposed approach is also used to solve the protein structure prediction problem efficiently.
Highlights
D IFFERENTIAL evolution (DE), proposed by Storn and Price [1], is a competitive and popular population-based stochastic search algorithm
The information entropy metric is proposed to estimate the extent that the population explores the solution space, which is mainly used for the dynamic division of the evolutionary stages
In the proposed differential evolution with information entropy-based mutation strategy (DEIE), an information entropy metric is designed by using the historical evolutionary information across generations, which reveal the trend of the movement of individuals in the search space
Summary
D IFFERENTIAL evolution (DE), proposed by Storn and Price [1], is a competitive and popular population-based stochastic search algorithm. The difference vectors of DE have adaptability for perturbation to the natural scales of the objective landscape in a random process [3] This self-referential mutation provides DE with a tremendous speed advantage at the early stage. The motivation behind this research is to proposes an adaptive differential evolution with information entropy-based mutation strategy (DEIE), which realize a dynamic division of the evolutionary stages based on entropy and stage-specific mutation strategies adaption to obtain the trade-off between exploration and exploitation. The information entropy metric is proposed to estimate the extent that the population explores the solution space, which is mainly used for the dynamic division of the evolutionary stages. Compared to other DE variants, the contributions of this paper are : (1) Dynamic division of evolutionary stages of DE based on information entropy metric is realized in the hope of getting a trade-off between exploration and exploitation. The proposed DEIE is tested on CEC 2013, 2014, and 2017 test sets, classical benchmark functions and a real-world case
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