An improved particle swarm optimizer for mechanical design optimization problems

He, Suining; Prempain, Emmanuel; Wu, Q. H.

doi:10.1080/03052150410001704854

Cited by 360 publications

(184 citation statements)

References 17 publications

Supporting

Mentioning

176

Contrasting

Unclassified

Order By: Relevance

“…Although GSO was based on continuous variables, this algorithm adapts the new position to the closest discrete position thanks to the discrete nature of the structural variables. This proposal was already assumed for the PSO algorithm (Parsopoulos and Vrahatis, 2002;He et al, 2004).…”

Section: Resultsmentioning

confidence: 99%

Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm

García-Segura

Piqueras

Martí

et al. 2014

Lat. Am. j. solids struct.

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm

García-Segura

Piqueras

Martí

et al. 2014

Lat. Am. j. solids struct.

View full text Add to dashboard Cite

“…Though several formulations have been reported, the optimum design is formulated according to Ref. [30], as follows: Table 3. In addition, the histories of the objective and the standard deviation of the objective function are shown in Fig.…”

Section: Optimum Design Of Pressure Vesselmentioning

confidence: 99%

Discrete Differential Evolution for Mixed Discrete Non-Linear Problems

Kitayama¹,

Arakawa²,

Yamazaki³

2012

JCEA

View full text Add to dashboard Cite

Differential evolution (DE) is a global optimizer for continuous design variables. To enhance DE, it is necessary to handle discrete design variables. In this paper, a discrete differential evolution (DDE) algorithm is proposed to handle discrete design variables. The proposed DDE is based on the DE/1/rand/bin method. In the proposed DDE, the mutation ratio is regarded as the exchange probability, and thus, no modifications of DE/1/rand/bin are required. In addition, in order to maintain diversity through the search process, we initialize all search points. By introducing the initialization of all search points, global or quasi-optimum solution can be found. We validate the proposed DDE by applying it to several benchmark problems.

show abstract

“…The problem is a constrained mixed discrete-continuous nonlinear optimization problem. In recent years, many bioinspired algorithms have been proposed to solve the problem [8][9][10][11]. The widely used benchmark problems also include a nonlinear optimization problem proposed by Himmelblau [12].…”

Section: Introductionmentioning

confidence: 99%

Porcellio scaber algorithm (PSA) for solving constrained optimization problems

Zhang

Guo

2017

MATEC Web Conf.

View full text Add to dashboard Cite

Abstract. In this paper, we extend a bio-inspired algorithm called the porcellio scaber algorithm (PSA) to solve constrained optimization problems, including a constrained mixed discrete-continuous nonlinear optimization problem. Our extensive experiment results based on benchmark optimization problems show that the PSA has a better performance than many existing methods or algorithms. The results indicate that the PSA is a promising algorithm for constrained optimization.

show abstract

An improved particle swarm optimizer for mechanical design optimization problems

Cited by 360 publications

References 17 publications

Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm

Optimization of concrete I-beams using a new hybrid glowworm swarm algorithm

Discrete Differential Evolution for Mixed Discrete Non-Linear Problems

Porcellio scaber algorithm (PSA) for solving constrained optimization problems

Contact Info

Product

Resources

About