Title: A clustering-based differential evolution with parapatric and cross-generation selection

Authors: Xujie Tan; Seong-Yoon Shin

Addresses: School of Information Science and Technology, Jiujiang University, Jiujiang, China ' School of Computer Information and Communication Engineering, Kunsan Nation University, Kunsan, South Korea

Abstract: Differential evolution (DE) is one of the efficient evolutionary algorithms (EA) for continuous optimisation problems. It is commonly known that the mutation is one of the cores of the DE algorithm. However, the mutation strategies randomly selected from the current population can't be fully exploited to search the optimal solution, especially in the big data era. To provide some suitable parent individuals for the mutation strategies, it is essential to exploit the data-driven method for analysing the population data. Tensor decomposition, proven to be an efficient data processing method, can be used to provide data-driven services. We propose a novel data-driven mutation strategy for parent individuals selection, namely tensor-based DE with parapatric and cross-generation (TPCDE). To evaluate the effectiveness of the proposed TPCDE, a series of data-driven experiments are carried out on 13 benchmark functions. The experimental results indicate that TPCDE is an effective and efficient framework.

Keywords: differential evolution; tensor; clustering; cross-generation selection scheme; parapatric selection scheme; PSS.

DOI: 10.1504/IJCVR.2021.10040488

International Journal of Computational Vision and Robotics, 2021 Vol.11 No.5, pp.497 - 511

Received: 01 Dec 2019
Accepted: 29 Apr 2020

Published online: 19 Aug 2021 *

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