Dual-Population Evolution Based Dynamic Constrained Multiobjective Optimization With Discontinuous and Irregular Feasible Regions

Dynamic constrained multiobjective optimization problems include irregular and discontinuous feasible regions, segmented true Pareto front, and dynamic environments. To address these problems, we design a dynamic constrained multiobjective optimization algorithm based on dual-population evolution. T...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on emerging topics in computational intelligence Jg. 9; H. 2; S. 1352 - 1366
Hauptverfasser: Jiang, Xiaoxu, Chen, Qingda, Ding, Jinliang, Zhang, Xingyi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Constraint handling
Constraints
Convergence
dual-population
Dynamic constrained multiobjective optimization
Dynamic response
Evolutionary algorithms
Feasibility
Heuristic algorithms
Multiple objective analysis
Optical fibers
Optimization
Pareto optimization
Populations
Power supplies
Prediction methods
Production
Resampling
test suit
Vectors
ISSN:2471-285X, 2471-285X
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Dynamic constrained multiobjective optimization problems include irregular and discontinuous feasible regions, segmented true Pareto front, and dynamic environments. To address these problems, we design a dynamic constrained multiobjective optimization algorithm based on dual-population evolution. This algorithm includes two populations, P 1 and P 2 , based on the feasibility of solutions. It utilizes valuable information from infeasible solutions to drive the populations toward the feasible regions and the true Pareto front. At the same time, we propose a mating selection operator to facilitate information exchange between populations and generate promising offspring solutions. To respond to environmental changes, we design a strategy that combines new solutions obtained by the sampling-selection-resampling method and updated old ones, rapidly generating a promising population in a new environment. Additionally, we also design a test suit that can effectively present the discontinuous feasible regions and the irregular changes of true Pareto front in practical appcation problems. The results from experiments demonstrate the efficacy of the test suit, and the proposed algorithm exhibits competitiveness compared to other algorithms.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2471-285X
2471-285X
DOI:10.1109/TETCI.2025.3529882