Improved Alopex-based evolutionary algorithm by Gaussian copula estimation of distribution algorithm and its application to the Butterworth filter design. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Yang, Yihang; Cheng, Xiang; Cheng, Junrui; Jiang, Da; Li, Shaojun
• Is Part Of: International journal of systems science. Volume 49:Issue 1(2018)
• Page Start: 160
• Page End: 178
• Publication Date: 2018-01-02
• Subjects: Evolutionary algorithm -- Alopex -- Gaussian copula -- estimation of distribution algorithm; System analysis -- Periodicals; 003.3
• DOI: 10.1080/00207721.2017.1390702
• Languages: English
• ISSNs: 0020-7721

Abstract

ABSTRACT: The application of evolutionary algorithms (EAs) is becoming widespread in engineering optimisation problems because of their simplicity and effectiveness. The Alopex-based evolutionary algorithm (AEA) possesses the basic characteristics of heuristic search algorithms but is lacking in adequate information about the fitness landscape of the input domain, reducing the convergence speed. To improve the performance of AEA, the Gaussian copula estimation of distribution algorithm (EDA) is embedded into the original AEA in this paper. With the help of Gaussian copula EDA, precise probability models are built utilising the best solutions, which can increase the convergence speed, and at the same time, keep the population diversity as much as possible. The simulation results on the benchmark functions and the application to the Butterworth filter design demonstrate the efficiency and effectiveness of the proposed algorithm, compared with several other EAs.