Adaptive modelling strategy for continuous multi-objective optimization

Aimin Zhou; Qingfu Zhang; Yaochu Jin; Bernhard Sendhoff

doi:10.1109/CEC.2007.4424503

Adaptive modelling strategy for continuous multi-objective optimization

Aimin Zhou, Qingfu Zhang, Yaochu Jin, Bernhard Sendhoff

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

10 Citations (Scopus)

Abstract

The Pareto optimal set of a continuous multi-objective optimization problem is a piecewise continuous manifold under some mild conditions. We have recently developed several multi-objective evolutionary algorithms based on this property. However, the modelling methods used in these algorithms are rather costly. In this paper, a cheap and effective modelling strategy is proposed for building the probabilistic models of promising solutions. A new criterion is proposed for measuring the convergence of the algorithm. The locality degree of each local model is adjusted according to the proposed convergence criterion. Experimental results show that the algorithm with the proposed strategy is very promising. © 2007 IEEE.

Original language	English
Title of host publication	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages	431-437
DOIs	https://doi.org/10.1109/CEC.2007.4424503
Publication status	Published - 2007
Externally published	Yes
Event	2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore Duration: 25 Sept 2007 → 28 Sept 2007

Conference

Conference	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Country/Territory	Singapore
Period	25/09/07 → 28/09/07

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10.1109/CEC.2007.4424503

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title = "Adaptive modelling strategy for continuous multi-objective optimization",

abstract = "The Pareto optimal set of a continuous multi-objective optimization problem is a piecewise continuous manifold under some mild conditions. We have recently developed several multi-objective evolutionary algorithms based on this property. However, the modelling methods used in these algorithms are rather costly. In this paper, a cheap and effective modelling strategy is proposed for building the probabilistic models of promising solutions. A new criterion is proposed for measuring the convergence of the algorithm. The locality degree of each local model is adjusted according to the proposed convergence criterion. Experimental results show that the algorithm with the proposed strategy is very promising. {\textcopyright} 2007 IEEE.",

author = "Aimin Zhou and Qingfu Zhang and Yaochu Jin and Bernhard Sendhoff",

year = "2007",

doi = "10.1109/CEC.2007.4424503",

language = "English",

isbn = "1424413400",

pages = "431--437",

booktitle = "2007 IEEE Congress on Evolutionary Computation, CEC 2007",

note = "2007 IEEE Congress on Evolutionary Computation, CEC 2007 ; Conference date: 25-09-2007 Through 28-09-2007",

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TY - GEN

T1 - Adaptive modelling strategy for continuous multi-objective optimization

AU - Zhou, Aimin

AU - Zhang, Qingfu

AU - Jin, Yaochu

AU - Sendhoff, Bernhard

PY - 2007

Y1 - 2007

N2 - The Pareto optimal set of a continuous multi-objective optimization problem is a piecewise continuous manifold under some mild conditions. We have recently developed several multi-objective evolutionary algorithms based on this property. However, the modelling methods used in these algorithms are rather costly. In this paper, a cheap and effective modelling strategy is proposed for building the probabilistic models of promising solutions. A new criterion is proposed for measuring the convergence of the algorithm. The locality degree of each local model is adjusted according to the proposed convergence criterion. Experimental results show that the algorithm with the proposed strategy is very promising. © 2007 IEEE.

AB - The Pareto optimal set of a continuous multi-objective optimization problem is a piecewise continuous manifold under some mild conditions. We have recently developed several multi-objective evolutionary algorithms based on this property. However, the modelling methods used in these algorithms are rather costly. In this paper, a cheap and effective modelling strategy is proposed for building the probabilistic models of promising solutions. A new criterion is proposed for measuring the convergence of the algorithm. The locality degree of each local model is adjusted according to the proposed convergence criterion. Experimental results show that the algorithm with the proposed strategy is very promising. © 2007 IEEE.

UR - http://www.scopus.com/inward/record.url?scp=79955227918&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-79955227918&origin=recordpage

U2 - 10.1109/CEC.2007.4424503

DO - 10.1109/CEC.2007.4424503

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 1424413400

SN - 9781424413409

SP - 431

EP - 437

BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

T2 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

Y2 - 25 September 2007 through 28 September 2007

ER -

Adaptive modelling strategy for continuous multi-objective optimization

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