Multi-objective optimization using self-adaptive differential evolution algorithm

V. L. Huang; S. Z. Zhao; R. Mallipeddi; P. N. Suganthan

doi:10.1109/CEC.2009.4982947

Multi-objective optimization using self-adaptive differential evolution algorithm

V. L. Huang
, S. Z. Zhao
, R. Mallipeddi
, P. N. Suganthan

School of Electronics Engineering

Nanyang Technological University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

112 Scopus citations

Abstract

In this paper, we propose a Multiobjective Self-adaptive Differential Evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and mutation strategies for each objective separately in a multi-objective optimization problem. The performance of the proposed OW-MOSaDE algorithm is evaluated on a suit of 13 benchmark problems provided for the CEC2009 MOEA Special Session and Competition (http://www3.ntu.edu.sg/home/epnsugan/) on Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms.

Original language	English
Title of host publication	2009 IEEE Congress on Evolutionary Computation, CEC 2009
Pages	190-194
Number of pages	5
DOIs	https://doi.org/10.1109/CEC.2009.4982947
State	Published - 2009
Event	2009 IEEE Congress on Evolutionary Computation, CEC 2009 - Trondheim, Norway Duration: 18 May 2009 → 21 May 2009

Publication series

Name	2009 IEEE Congress on Evolutionary Computation, CEC 2009

Conference

Conference	2009 IEEE Congress on Evolutionary Computation, CEC 2009
Country/Territory	Norway
City	Trondheim
Period	18/05/09 → 21/05/09

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10.1109/CEC.2009.4982947

Cite this

@inproceedings{ca490e3df7434bf88f91c2bf6c3148a8,

title = "Multi-objective optimization using self-adaptive differential evolution algorithm",

abstract = "In this paper, we propose a Multiobjective Self-adaptive Differential Evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and mutation strategies for each objective separately in a multi-objective optimization problem. The performance of the proposed OW-MOSaDE algorithm is evaluated on a suit of 13 benchmark problems provided for the CEC2009 MOEA Special Session and Competition (http://www3.ntu.edu.sg/home/epnsugan/) on Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms.",

author = "Huang, \{V. L.\} and Zhao, \{S. Z.\} and R. Mallipeddi and Suganthan, \{P. N.\}",

year = "2009",

doi = "10.1109/CEC.2009.4982947",

language = "English",

isbn = "9781424429592",

series = "2009 IEEE Congress on Evolutionary Computation, CEC 2009",

pages = "190--194",

booktitle = "2009 IEEE Congress on Evolutionary Computation, CEC 2009",

note = "2009 IEEE Congress on Evolutionary Computation, CEC 2009 ; Conference date: 18-05-2009 Through 21-05-2009",

}

Huang, VL, Zhao, SZ, Mallipeddi, R & Suganthan, PN 2009, Multi-objective optimization using self-adaptive differential evolution algorithm. in 2009 IEEE Congress on Evolutionary Computation, CEC 2009., 4982947, 2009 IEEE Congress on Evolutionary Computation, CEC 2009, pp. 190-194, 2009 IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, 18/05/09. https://doi.org/10.1109/CEC.2009.4982947

Multi-objective optimization using self-adaptive differential evolution algorithm. / Huang, V. L.; Zhao, S. Z.; Mallipeddi, R. et al.
2009 IEEE Congress on Evolutionary Computation, CEC 2009. 2009. p. 190-194 4982947 (2009 IEEE Congress on Evolutionary Computation, CEC 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multi-objective optimization using self-adaptive differential evolution algorithm

AU - Huang, V. L.

AU - Zhao, S. Z.

AU - Mallipeddi, R.

AU - Suganthan, P. N.

PY - 2009

Y1 - 2009

N2 - In this paper, we propose a Multiobjective Self-adaptive Differential Evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and mutation strategies for each objective separately in a multi-objective optimization problem. The performance of the proposed OW-MOSaDE algorithm is evaluated on a suit of 13 benchmark problems provided for the CEC2009 MOEA Special Session and Competition (http://www3.ntu.edu.sg/home/epnsugan/) on Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms.

AB - In this paper, we propose a Multiobjective Self-adaptive Differential Evolution algorithm with objective-wise learning strategies (OW-MOSaDE) to solve numerical optimization problems with multiple conflicting objectives. The proposed approach learns suitable crossover parameter values and mutation strategies for each objective separately in a multi-objective optimization problem. The performance of the proposed OW-MOSaDE algorithm is evaluated on a suit of 13 benchmark problems provided for the CEC2009 MOEA Special Session and Competition (http://www3.ntu.edu.sg/home/epnsugan/) on Performance Assessment of Constrained / Bound Constrained Multi-Objective Optimization Algorithms.

UR - https://www.scopus.com/pages/publications/70449839425

U2 - 10.1109/CEC.2009.4982947

DO - 10.1109/CEC.2009.4982947

M3 - Conference contribution

AN - SCOPUS:70449839425

SN - 9781424429592

T3 - 2009 IEEE Congress on Evolutionary Computation, CEC 2009

SP - 190

EP - 194

BT - 2009 IEEE Congress on Evolutionary Computation, CEC 2009

T2 - 2009 IEEE Congress on Evolutionary Computation, CEC 2009

Y2 - 18 May 2009 through 21 May 2009

ER -

Multi-objective optimization using self-adaptive differential evolution algorithm

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