University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Generalized Multi-tasking for Evolutionary Optimization of Expensive Problems

Ding, Jinliang, Yang, Cuie, Jin, Yaochu and Chai, Tianyou (2017) Generalized Multi-tasking for Evolutionary Optimization of Expensive Problems IEEE Transactions on Evolutionary Computation.

[img] Text
Generalized Multi-tasking for Evolutionary Optimization of Expensive Problems.docx - Accepted version Manuscript

Download (1MB)


Conventional evolutionary algorithms are not well suited for solving expensive optimization problems due to the fact that they often require a large number of fitness evaluations to obtain acceptable solutions. To alleviate the difficulty, this paper presents a multi-tasking evolutionary optimization framework for solving computationally expensive problems. In the framework, knowledge is transferred from a number of computationally cheap optimization problems to help the solution of the expensive problem on the basis of the recently proposed multifactorial evolutionary algorithm, leading to a faster convergence of the expensive problem. However, existing multifactorial evolutionary algorithms do not work well in solving multi-tasking problems whose optimums do not lie in the same location or when the dimensions of the decision space are not the same. To address the above issues, the existing multifactorial evolutionary algorithm is generalized by proposing two strategies, one for decision variable translation and the other for decision variable shuffling, to facilitate knowledge transfer between optimization problems having different locations of the optimums and different numbers of decision variables. To assess the effectiveness of the generalized multifactorial evolutionary algorithm, empirical studies have been conducted on eight multi-tasking instances and eight test problems for expensive optimization. The experimental results demonstrate that the proposed generalized multifactorial evolutionary algorithm works more efficiently for multi-tasking optimization and successfully accelerates the convergence of expensive optimization problems compared to single-task optimization.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Ding, Jinliang
Yang, Cuie
Chai, Tianyou
Date : 20 December 2017
DOI : 10.1109/TEVC.2017.2785351
Copyright Disclaimer : © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Evolutionary algorithms; Expensive optimization; Evolutionary multi-tasking optimization; Knowledge transfer
Depositing User : Clive Harris
Date Deposited : 20 Dec 2017 09:34
Last Modified : 16 Jan 2019 19:06

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800