Enhanced Differential Evolution with Self-organizing Map for Numerical Optimization
In Differential evolution (DE), the valuable information from the data generated during the evolutionary process has not yet fully exploited to guide the search. As a clustering algorithm based on neural network structure, Self-organizing map (SOM) method can effectively preserve the topological structure of the data in the high dimensional input space. By taking the advantage of SOM, this paper presents a SOM-based DE variant (DE-SOM) to utilize the neighborhood information extracted by the SOM method. In DE-SOM, the neighborhood relationships among the individuals are firstly extracted by the SOM method. Then, with the obtained neighborhood relationships, a self-adaptive neighborhood mechanism (SNM) is introduced to dynamically adjust the neighborhood size for selecting parents involved in the mutation process. The performance of DE-SOM has been evaluated on the benchmark functions from CEC2013, and the results show its effectiveness when compared with the original DE algorithms.
KeywordsDifferential evolution Self-organizing map Self-adaptive neighborhood mechanism Numerical optimization
This work was supported in part by the Natural Science Foundation of Fujian Province of China (2018J01091, 2015J01258), the Postgraduate Scientific Research Innovation Ability Training Plan Funding Projects of Huaqiao University (17013083021) and the Opening Project of Guangdong Province Key Laboratory of Computational Science at the Sun Yat-sen University.
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