Structural optimization of a centrifugal impeller using differential evolution in CATIA™ environment

2007 ◽  
Vol 7 (2) ◽  
pp. 185-211 ◽  
Author(s):  
I. M. Valakos ◽  
M. S. Ntipteni ◽  
I. K. Nikolos
Keyword(s):  
Structural Optimization ◽  
Differential Evolution ◽  
Centrifugal Impeller

scholarly journals Performance of the Modified Dolphin Monitoring Operator for Weight Optimization of Skeletal Structures

2018 ◽  
Author(s):  
Ali Kaveh ◽  
S.R. Hoseini Vaez ◽  
Pedram Hosseini
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Structural Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Harmony Search ◽  
Average Weight ◽  
Weight Optimization ◽  
Swarm Optimization ◽  
Population Dispersion

In this study, the Modified Dolphin Monitoring (MDM) operator is used to enhance the performance of some metaheuristic algorithms. The MDM is a recently presented operator that controls the population dispersion in each iteration. Algorithms are selected from some well-established algorithms. Here, this operator is applied on Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Vibrating Particles System (VPS), Enhanced Vibrating Particles System (EVPS), Colliding Bodied Optimization (CBO) and Harmony Search (HS) and the performance of these algorithms are evaluated with and without this operator on three well-known structural optimization problems. The results show the performance of this operator on these algorithms for the best, the worst, average and average weight of the first quarter of answers.

Improved Differential Evolution Algorithm for Structural Optimization Design of Flumes with Hybrid Discrete Variables

2011 ◽  
Vol 383-390 ◽  
pp. 672-677
Author(s):  
Juan Zhou ◽  
Duo Xin Zhang ◽  
Xian Liang Liu
Keyword(s):  
Structural Optimization ◽  
Differential Evolution ◽  
Optimization Design ◽  
Optimization Problems ◽  
Logistic Map ◽  
Differential Evolution Algorithm ◽  
Local Optimum ◽  
Original Design ◽  
Design Scheme ◽  
De Algorithm

The traditional method applying to solve continuous variable optimization problems is not suit for flume structural optimization design with hybrid discrete variable. According to the mathematical model of structural optimum design of the prestressed U-shell flumes, differential evolution (DE) algorithm was introduced to flume structural optimization design. In order to improve the population’s diversity and the ability of escaping from the local optimum, a self-adapting crossover probability factor was presented. Furthermore, a chaotic sequence based on logistic map was employed to self-adaptively adjust mutation factor based on linear crossover, which can improve the convergence of DE algorithm. Dynamic penalty function, to transform the constrained problem to unconstrained one, was employed. The result shows that, compared with the original design scheme, the optimization design scheme can greatly reduce the amount of prestressed reinforcement. The construction cost of both the flume and the whole project can be reduced accordingly.

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