Comparison of synchronous and asynchronous parallelization of extreme surrogate-assisted multi-objective evolutionary al
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Comparison of synchronous and asynchronous parallelization of extreme surrogate-assisted multi-objective evolutionary algorithm Tomohiro Harada1
•
Misaki Kaidan2 • Ruck Thawonmas3
Accepted: 1 September 2020 The Author(s) 2020
Abstract This paper investigates the integration of a surrogate-assisted multi-objective evolutionary algorithm (MOEA) and a parallel computation scheme to reduce the computing time until obtaining the optimal solutions in evolutionary algorithms (EAs). A surrogate-assisted MOEA solves multi-objective optimization problems while estimating the evaluation of solutions with a surrogate function. A surrogate function is produced by a machine learning model. This paper uses an extreme learning surrogate-assisted MOEA/D (ELMOEA/D), which utilizes one of the well-known MOEA algorithms, MOEA/D, and a machine learning technique, extreme learning machine (ELM). A parallelization of MOEA, on the other hand, evaluates solutions in parallel on multiple computing nodes to accelerate the optimization process. We consider a synchronous and an asynchronous parallel MOEA as a master-slave parallelization scheme for ELMOEA/D. We carry out an experiment with multi-objective optimization problems to compare the synchronous parallel ELMOEA/D with the asynchronous parallel ELMOEA/D. In the experiment, we simulate two settings of the evaluation time of solutions. One determines the evaluation time of solutions by the normal distribution with different variances. On the other hand, another evaluation time correlates to the objective function value. We compare the quality of solutions obtained by the parallel ELMOEA/D variants within a particular computing time. The experimental results show that the parallelization of ELMOEA/D significantly reduces the computational time. In addition, the integration of ELMOEA/D with the asynchronous parallelization scheme obtains higher quality of solutions quicker than the synchronous parallel ELMOEA/D. Keywords Evolutionary computation Extreme learning machine Multi-objective optimization Parallelization Surrogate model
1 Introduction Evolutionary algorithms (EAs) have been applied to many real-world applications like engineering (Obayashi et al. 2010; Oyama et al. 2017), data mining (Devos et al. 2014;
Soufan et al. 2015), electronics (Roberge et al. 2014), and nanoscience (Shayeghi et al. 2015; Davis et al. 2015) because of their high search capability without any preliminary knowledge of a target problem. Since many realworld applications include two or more objectives with conflict, i.e., a trade-off, multi-objective evolutionary algorithms (MOEAs) have attracted much attention for
This work was supported by Japan Society for the Promotion of Science Grant-in-Aid for Young Scientists Grant Number JP19K20362.
2
& Tomohiro Harada [email protected]
Graduate School of Information Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi Kusatsu, Shiga, Japan
3
College of Information Science and Engineerin
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