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Semi-supervised learning assisted particle swarm optimization of computationally expensive problems

Sun, Chaoli, Jin, Yaochu and Tan, Ying (2018) Semi-supervised learning assisted particle swarm optimization of computationally expensive problems In: GECCO '18 Genetic and Evolutionary Computation Conference, 15-19 Jul 2018, Kyoto, Japan.

Semi-supervised learning assisted particle swarm optimization of computationally expensive problems.pdf - Accepted version Manuscript

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In many real-world optimization problems, it is very time-consuming to evaluate the performance of candidate solutions because the evaluations involve computationally intensive numerical simulations or costly physical experiments. Therefore, standard population based meta-heuristic search algorithms are not best suited for solving such expensive problems because they typically require a large number of performance evaluations. To address this issue, many surrogate-assisted meta-heuristic algorithms have been proposed and shown to be promising in achieving acceptable solutions with a small computation budget. While most research focuses on reducing the required number of expensive fitness evaluations, not much attention has been paid to take advantage of the large amount of unlabelled data, i.e., the solutions that have not been evaluated using the expensive fitness functions, generated during the optimization. This paper aims to make use of semi-supervised learning techniques that are able to enhance the training of surrogate models using the unlabelled data together with the labelled data in a surrogate-assisted particle swarm optimization algorithm. Empirical studies on five 30-dimensional benchmark problems show that the proposed algorithm is able to find high-quality solutions for computationally expensive problems on a limited computational budget.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Sun, Chaoli
Tan, Ying
Date : 19 July 2018
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1145/3205455.3205596
Copyright Disclaimer : © 2018 Association for Computing Machinery. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full cita- tion on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re- publish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from
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Depositing User : Clive Harris
Date Deposited : 13 Aug 2018 10:33
Last Modified : 11 Dec 2018 11:24

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