Interval Multiobjective Optimization With Memetic Algorithms

One of the most important and widely faced optimization problems in real applications is the interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary algorithms (EAs) for IMOPs (IMOEAs) need a great deal of objective function evaluations to find a final Pareto front w...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 50; no. 8; pp. 3444 - 3457
Main Authors: Sun, Jing, Miao, Zhuang, Gong, Dunwei, Zeng, Xiao-Jun, Li, Junqing, Wang, Gaige
Format: Journal Article
Language:English
Published: United States IEEE 01.08.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Desalination
Evolutionary algorithm (EA)
Evolutionary algorithms
interval
IP networks
Linear programming
memetic algorithm (MA)
Memetics
multiobjective optimization
Multiple objective analysis
Optimization
Pareto optimization
Search problems
Searching
Sun
Uncertainty
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:One of the most important and widely faced optimization problems in real applications is the interval multiobjective optimization problems (IMOPs). The state-of-the-art evolutionary algorithms (EAs) for IMOPs (IMOEAs) need a great deal of objective function evaluations to find a final Pareto front with good convergence and even distribution. Further, the final Pareto front is of great uncertainty. In this paper, we incorporate several local searches into an existing IMOEA, and propose a memetic algorithm (MA) to tackle IMOPs. At the start, the existing IMOEA is utilized to explore the entire decision space; then, the increment of the hypervolume is employed to develop an activation strategy for every local search procedure; finally, the local search procedure is conducted by constituting its initial population, whose center is an individual with a small uncertainty and a big contribution to the hypervolume, taking the contribution of an individual to the hypervolume as its fitness function, and performing the conventional genetic operators. The proposed MA is empirically evaluated on ten benchmark IMOPs as well as an uncertain solar desalination optimization problem and compared with three state-of-the-art algorithms with no local search procedure. The experimental results demonstrate the applicability and effectiveness of the proposed MA.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2019.2908485