Direct method for uncertain multi-objective optimization based on interval non-dominated sorting

The interval optimization methods which describe the uncertainty by intervals are widely used in uncertain multi-objective optimization. Commonly, the interval values of the objective functions are substituted by approximate values, which will cause the loss of accuracy. To deal with the problem, a...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Structural and multidisciplinary optimization Ročník 62; číslo 2; s. 729 - 745
Hlavní autori: Liu, Guiping, Liu, Sheng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2020
Springer Nature B.V
Predmet:
Computational Mathematics and Numerical Analysis
Computer simulation
Constraints
Crashworthiness
Engineering
Engineering Design
Genetic algorithms
Impact strength
Multiple objective analysis
Optimization
Population
Research Paper
Theoretical and Applied Mechanics
Multi-objective genetic algorithm
Interval non-dominated sorting
Uncertain multi-objective optimization
Crashworthiness
General crowding distance
ISSN:1615-147X, 1615-1488
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:The interval optimization methods which describe the uncertainty by intervals are widely used in uncertain multi-objective optimization. Commonly, the interval values of the objective functions are substituted by approximate values, which will cause the loss of accuracy. To deal with the problem, a direct method is presented in this paper. In the framework of the multi-objective genetic algorithm, the upper and lower boundaries of the objective and constraint functions are found using the Monte Carlo stochastic simulation method and the constraints are handled by the degree of interval constraint violation; and then every two individuals from the combination population of the evolutionary population and the external elite population are compared using the interval comparison method. Afterwards, an interval non-dominated sorting based on the interval dominance relations is performed to sort the combination population into different non-dominated level. Furthermore, a general crowding distance is calculated for each individual to make individuals from the same non-dominated level comparable and also to maintain the diversity. The efficiency and accuracy of the proposed method is demonstrated by three test functions, the multi-objective structural optimization of the ten-bar truss, and the multi-objective structural optimization of a vehicle body for crashworthiness and lightweight.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-020-02530-6