A Self-Adaptive Evolutionary Multi-Task Based Constrained Multi-Objective Evolutionary Algorithm

Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the satisfaction of various constraints. Most constrained multi-objective evolutionary algorithms (CMOEAs) are prone to fall into the local optima due to th...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on emerging topics in computational intelligence Vol. 7; no. 4; pp. 1 - 15
Main Authors: Qiao, Kangjia, Liang, Jing, Yu, Kunjie, Wang, Minghui, Qu, Boyang, Yue, Caitong, Guo, Yinan
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Constrained multi-objective optimization
Constraints
Evolutionary algorithms
evolutionary multi-task optimization
Genetic algorithms
inter-task
intra-task
Knowledge transfer
Multiple objective analysis
Multitasking
Optimization
Populations
self-adaptive
Sociology
Statistics
Task analysis
ISSN:2471-285X, 2471-285X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the satisfaction of various constraints. Most constrained multi-objective evolutionary algorithms (CMOEAs) are prone to fall into the local optima due to the imbalance between objectives and constraints as well as the poor search ability of the population. To better solve CMOPs, this paper proposes a double-balanced evolutionary multi-task optimization (DBEMTO) algorithm, which evolves two populations to respectively solve the main task (CMOP) and the auxiliary task (MOP extracted from the CMOP). In DBEMTO, three evolutionary strategies are assigned to each population for offspring generation. The three evolutionary strategies include an individual transfer-based inter-task strategy and two intra-task strategies, not only utilizing the information of inter-task but also providing diverse search abilities of intra-task. Moreover, a self-adaptive scheme is developed to self-adaptively employ three strategies, so that the population can balance the information utilization of both intra-task and inter-task. Then, in the environmental selection, the performance of the three strategies is adopted to guide the sharing of the two offspring populations. Compared with several other state-of-the-art CMOEAs, DBEMTO has performed more competitively according to the final results.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2471-285X
2471-285X
DOI:10.1109/TETCI.2023.3236633