A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, wh...

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Vydáno v:	IEEE transactions on evolutionary computation Ročník 23; číslo 1; s. 74 - 88
Hlavní autoři:	Pan, Linqiang, He, Cheng, Tian, Ye, Wang, Handing, Zhang, Xingyi, Jin, Yaochu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.02.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Artificial neural networks Classification Computer simulation Evolutionary algorithms Evolutionary computation expensive many-objective optimization Fitness Genetic algorithms Linear programming Multiple objective analysis Neural networks Neurons Optimization Pareto dominance Prediction algorithms Signal processing algorithms State of the art surrogate-assisted evolutionary optimization Uncertainty
ISSN:	1089-778X, 1941-0026
On-line přístup:	Získat plný text
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Abstract	Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogate-assisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of many-objective optimization test problems.
AbstractList	Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogate-assisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of many-objective optimization test problems.
Author	Pan, Linqiang Wang, Handing Zhang, Xingyi Tian, Ye Jin, Yaochu He, Cheng
Author_xml	– sequence: 1 givenname: Linqiang orcidid: 0000-0002-4554-455X surname: Pan fullname: Pan, Linqiang email: lqpanhust@gmail.com organization: Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Automation, University of Science and Technology, Wuhan, China – sequence: 2 givenname: Cheng orcidid: 0000-0003-4218-8454 surname: He fullname: He, Cheng email: chenghehust@gmail.com organization: Department of Computer Science, University of Surrey, Surrey, U.K – sequence: 3 givenname: Ye surname: Tian fullname: Tian, Ye email: field910921@gmail.com organization: Department of Computer Science, University of Surrey, Surrey, U.K – sequence: 4 givenname: Handing orcidid: 0000-0002-4805-3780 surname: Wang fullname: Wang, Handing email: wanghanding.patch@gmail.com organization: Department of Computer Science, University of Surrey, Surrey, U.K – sequence: 5 givenname: Xingyi orcidid: 0000-0002-5052-000X surname: Zhang fullname: Zhang, Xingyi email: xyzhanghust@gmail.com organization: Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China – sequence: 6 givenname: Yaochu orcidid: 0000-0003-1100-0631 surname: Jin fullname: Jin, Yaochu email: yaochu.jin@surrey.ac.uk organization: Department of Computer Science, University of Surrey, Surrey, U.K
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Snippet	Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real...
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SubjectTerms	Artificial neural networks Classification Computer simulation Evolutionary algorithms Evolutionary computation expensive many-objective optimization Fitness Genetic algorithms Linear programming Multiple objective analysis Neural networks Neurons Optimization Pareto dominance Prediction algorithms Signal processing algorithms State of the art surrogate-assisted evolutionary optimization Uncertainty
Title	A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization
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