Generalized Multitasking for Evolutionary Optimization of Expensive Problems

Conventional evolutionary algorithms (EAs) 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 multitasking evolutionary optimi...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 23; no. 1; pp. 44 - 58
Main Authors: Ding, Jinliang, Yang, Cuie, Jin, Yaochu, Chai, Tianyou
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Convergence
Evolutionary algorithms
Evolutionary algorithms (EAs)
Evolutionary computation
evolutionary multitasking optimization
expensive optimization
Fitness
Knowledge management
Knowledge transfer
Multitasking
Optimization
Sociology
Statistics
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Conventional evolutionary algorithms (EAs) 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 multitasking 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 EA (MFEA), leading to a faster convergence of the expensive problem. However, existing MFEAs do not work well in solving multitasking 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 MFEA 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 MFEA (G-MFEA), empirical studies have been conducted on eight multitasking instances and eight test problems for expensive optimization. The experimental results demonstrate that the proposed G-MFEA works more efficiently for multitasking optimization and successfully accelerates the convergence of expensive optimization problems compared to single-task optimization.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2017.2785351