An R2 indicator and weight vector-based evolutionary algorithm for multi-objective optimization

A two-stage R2 indicator-based evolution algorithm (TS-R2EA) was proposed in the recent years. A good balance between convergence and diversity can be achieved, due to the R2 indicator and reference vector-guided selection strategy. However, TSR2-EA is sensitive to problem geometries. In order to ad...

Full description

Saved in:
Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 24; no. 7; pp. 5079 - 5100
Main Authors: Liu, Yuanchao, Liu, Jianchang, Li, Tianjun, Li, Qian
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2020
Springer Nature B.V
Subjects:
Approximation
Artificial Intelligence
Business metrics
Computational Intelligence
Control
Decomposition
Engineering
Evolutionary algorithms
Genetic algorithms
Mathematical Logic and Foundations
Mechatronics
Methodologies and Application
Monte Carlo simulation
Multiple objective analysis
Optimization
Robotics
Strategy
Utility functions
R2 indicator
Weight vector adaption
Multi-objective optimization
Evolutionary algorithm
ISSN:1432-7643, 1433-7479
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A two-stage R2 indicator-based evolution algorithm (TS-R2EA) was proposed in the recent years. A good balance between convergence and diversity can be achieved, due to the R2 indicator and reference vector-guided selection strategy. However, TSR2-EA is sensitive to problem geometries. In order to address this issue, a weight vector-based selection strategy is introduced, and a weight vector adaptive strategy based on population partition is proposed. In the selection strategy, each candidate solution is ranked according to the scalarizing function values in the corresponding neighbor, and the candidate solutions with good performance can be selected. In the adaptive strategy, the population is partitioned by associating each individual with its closest weight vector, and the weight vectors with a worse performance are adjusted. Similar to TS-R2EA, these strategies are combined with the R2 indicator to solve multi-objective optimization problems. The performance of proposed algorithm has been validated and compared with four related algorithms on a variety of benchmark test problems. The experimental results have demonstrated that the proposed algorithm has high competition and is less sensitive to problem geometries.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-019-04258-y