Differential evolution for solving multiobjective optimization problems

被引：23

作者：

Sarker, R ^{[1
]}

Abbass, HA ^{[1
]}

机构：

[1] Univ New S Wales, Sch Informat Technol & Elect Engn, Canberra, ACT 2600, Australia

来源：

ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH | 2004年 / 21卷 / 02期

关键词：

multi-objective optimization; vector optimization; evolutionary strategies; differential evolution; Pareto frontier; population-based approach;

D O I：

10.1142/S0217595904000217

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

The use of evolutionary strategies (ESs) to solve problems with multiple objectives [known as vector optimization problems (VOPs)] has attracted much attention recently. Being population-based approaches, ESs offer a means to find a set of Pareto-optimal solutions in a single run. Differential evolution (DE) is an ES that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto-frontier differential evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for two standard test problems, outperform the "strength Pareto evolutionary algorithm", one of the state-of-the-art evolutionary algorithm for solving VOPs.

引用

页码：225 / 240

页数：16

共 17 条

[1]

[Anonymous], 1989, GENETIC ALGORITHM SE

[2]

[Anonymous], 2001, SPEA2 IMPROVING STRE, DOI DOI 10.3929/ETHZ-A-004284029

[3]

[Anonymous], 1995, DIFFERENTIAL EVOLUTI

[4]

Coello C. A. C., 1999, Knowledge and Information Systems, V1, P269

[5]

Corne D. W., 2000, Parallel Problem Solving from Nature PPSN VI. 6th International Conference. Proceedings (Lecture Notes in Computer Science Vol.1917), P839

[6]

DEB K, 2002, 2000004 IIT

[7]

Deb K., 2001, Multi-Objective Optimization using Evolutionary Algorithms

[8]

FONSECA CM, 1993, PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON GENETIC ALGORITHMS, P416

[9]

HAJELA P, 1998, STRUCTURAL OPTIMIZAT, V4, P99

[10]

HORN J, 1994, 1ST P IEEE C EV COMP, V1, P82

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