Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 8462-8470 |
| Number of pages | 9 |
| Journal | Expert Systems with Applications |
| Volume | 37 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 1 Dec 2010 |
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Keywords
- Evolutionary computation
- convergence
- diversified archiving
- multiobjective evolutionary algorithms
- ϵ-Pareto set
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Adaptive, convergent, and diversified archiving strategy for multiobjective evolutionary algorithms. / JIN, Huidong; WONG, Man Leung.
In: Expert Systems with Applications, Vol. 37, No. 12, 01.12.2010, p. 8462-8470.Research output: Journal Publications › Journal Article (refereed) › Research › peer-review
TY - JOUR
T1 - Adaptive, convergent, and diversified archiving strategy for multiobjective evolutionary algorithms
AU - JIN, Huidong
AU - WONG, Man Leung
PY - 2010/12/1
Y1 - 2010/12/1
N2 - It is crucial to obtain automatically and efficiently a well-distributed set of Pareto optimal solutions in multiobjective evolutionary algorithms (MOEAs). Many studies have proposed different evolutionary algorithms that can progress toward the Pareto front with a widely spread distribution of solutions. However, most theoretically, convergent MOEAs necessitate certain prior knowledge about the Pareto front in order to efficiently maintain widespread solutions. In this paper, we propose, based on the new E-dominance concept, an Adaptive Rectangle Archiving (ARA) strategy that overcomes this important problem. The MOEA with this archiving technique provably converges to well-distributed Pareto optimal solutions without prior knowledge about the Pareto front. ARA complements the existing archiving techniques and is useful to both researchers and practitioners.
AB - It is crucial to obtain automatically and efficiently a well-distributed set of Pareto optimal solutions in multiobjective evolutionary algorithms (MOEAs). Many studies have proposed different evolutionary algorithms that can progress toward the Pareto front with a widely spread distribution of solutions. However, most theoretically, convergent MOEAs necessitate certain prior knowledge about the Pareto front in order to efficiently maintain widespread solutions. In this paper, we propose, based on the new E-dominance concept, an Adaptive Rectangle Archiving (ARA) strategy that overcomes this important problem. The MOEA with this archiving technique provably converges to well-distributed Pareto optimal solutions without prior knowledge about the Pareto front. ARA complements the existing archiving techniques and is useful to both researchers and practitioners.
KW - Evolutionary computation
KW - convergence
KW - diversified archiving
KW - multiobjective evolutionary algorithms
KW - ϵ-Pareto set
UR - http://commons.ln.edu.hk/sw_master/173
U2 - 10.1016/j.eswa.2010.05.032
DO - 10.1016/j.eswa.2010.05.032
M3 - Journal Article (refereed)
VL - 37
SP - 8462
EP - 8470
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 12
ER -