Xiaolin Hu
ABSTRACT
In recent years, evolutionary algorithms (EAs) have gained attention among scholars and have been applied to optimization engineering with various degrees of success. Concurrently, machine learning methods have rapidly developed in the field of artificial intelligence and have been increasingly integrated with other domains. This paper introduces a novel multi-population differential evolution algorithm called DE-FR, based on the proposed DBSCAN-FR clustering algorithm. This paper contributes to the improvement of the differential evolution algorithm in the following aspects. Firstly, it presents an enhanced clustering algorithm, DBSCAN-FR, which incorporates a forward distance filtering mechanism to divide the population into several groups successfully in high dimensional space. Secondly, it introduces a novel differential evolution algorithm named DE-FR, which builds upon the DBSCAN-FR clustering algorithm aims to solve complex single-objective optimization problems. Lastly, the proposed algorithm is compared with other classical differential evolution variants on CEC2014 benchmarks, and experimental results demonstrate its competitive performance.
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Index Terms
- DE-FR: Differential Evolution Algorithm Based on DBSCAN-FR Clustering Method
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