An Efficient Hybrid Optimization Approach Using Adaptive Elitist Differential Evolution and Spherical Quadratic Steepest Descent and Its Application for Clustering
| Autor: | T. Truong-Khac, A. T. Pham-Chau, HungLinh Ao, Thao Nguyen-Trang, Trung Nguyen-Thoi |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
education.field_of_study
Mathematical optimization Article Subject Computer science Population 02 engineering and technology 01 natural sciences Measure (mathematics) Computer Science Applications 010104 statistics & probability QA76.75-76.765 Quadratic equation Robustness (computer science) Differential evolution 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Computer software 0101 mathematics Gradient descent education Cluster analysis Software |
| Zdroj: | Scientific Programming, Vol 2019 (2019) |
| ISSN: | 1058-9244 |
| DOI: | 10.1155/2019/7151574 |
| Popis: | In this paper, a hybrid approach that combines a population-based method, adaptive elitist differential evolution (aeDE), with a powerful gradient-based method, spherical quadratic steepest descent (SQSD), is proposed and then applied for clustering analysis. This combination not only helps inherit the advantages of both the aeDE and SQSD but also helps reduce computational cost significantly. First, based on the aeDE’s global explorative manner in the initial steps, the proposed approach can quickly reach to a region that contains the global optimal value. Next, based on the SQSD’s locally effective exploitative manner in the later steps, the proposed approach can find the global optimal solution rapidly and accurately and hence helps reduce the computational cost. The proposed method is first tested over 32 benchmark functions to verify its robustness and effectiveness. Then, it is applied for clustering analysis which is one of the problems of interest in statistics, machine learning, and data mining. In this application, the proposed method is utilized to find the positions of the cluster centers, in which the internal validity measure is optimized. For both the benchmark functions and clustering problem, the numerical results show that the hybrid approach for aeDE (HaeDE) outperforms others in both accuracy and computational cost. |
| Databáze: | OpenAIRE |
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