Title: A united framework with multi-operator evolutionary algorithms and interior point method for efficient single objective optimisation problem solving

Authors: Junying Chen; Jinhui Chen; Huaqing Min

Addresses: Guangzhou Key Laboratory of Robotics and Intelligent Software, School of Software Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China ' Guangzhou Key Laboratory of Robotics and Intelligent Software, School of Software Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China ' Guangzhou Key Laboratory of Robotics and Intelligent Software, School of Software Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China

Abstract: Single objective optimisation problem solving is a big challenge in science and engineering areas. This is because the optimisation problems usually have the properties of high dimensions, many local optima, and limited iterations. Therefore, an efficient single objective optimisation problem solving method is investigated in this study. A united algorithm framework using multi-operator evolutionary algorithms and interior point method is proposed in this work. Within this framework, three multi-operator evolutionary algorithms are combined to search for the global optimum, and interior point method is used to optimise the evolutionary process with efficient searches. The proposed algorithm framework was tested on CEC-2014 benchmark suite, and the experimental results demonstrated that such algorithm framework presented good optimisation performance for most single objective optimisation problems through efficient iterations.

Keywords: united framework; multi-operator; evolutionary algorithms; interior point method; single objective optimisation; efficient problem solving; high performance computing; genetic algorithm; differential evolution; evolution strategy.

DOI: 10.1504/IJHPCN.2019.098586

International Journal of High Performance Computing and Networking, 2019 Vol.13 No.3, pp.340 - 353

Received: 23 Jun 2017
Accepted: 02 Dec 2017
Published online: 28 Mar 2019 *

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