A binary particle swarm optimization for continuum structural topology optimization

Luh, Guan-Chun; Lin, Chun-Yi; Lin, Yung-Kai

doi:10.1016/j.asoc.2010.11.013

Cited by 111 publications

(53 citation statements)

References 30 publications

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“…The control parameters adjusted to the EBPSO are illustrated in Table 2 Four S-shape families of transfer functions are attempted [30]. The S-shaped is defined in Eq.…”

Section: Numerical Results Discussion and Comparisonsmentioning

confidence: 99%

“…This version of BPSO suffers from getting stuck to the local minima. Some modifications to the BPSO have been proposed by using new updating techniques for position and velocity vectors in order to enhance the performance of the conventional BPSO [30,31]. An enhanced BPSO (EBPSO) based on new transfer functions with two categories, S-and V-shaped, is presented and applied [32].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Reconfiguration Comprising Voltage Stability Aspect Using Enhanced Binary Particle Swarm Optimization Algorithm

Othman

El‐Fergany

Abdelaziz

2015

Electric Power Components and Systems

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“…The control parameters adjusted to the EBPSO are illustrated in Table 2 Four S-shape families of transfer functions are attempted [30]. The S-shaped is defined in Eq.…”

Section: Numerical Results Discussion and Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Reconfiguration Comprising Voltage Stability Aspect Using Enhanced Binary Particle Swarm Optimization Algorithm

Othman

El‐Fergany

Abdelaziz

2015

Electric Power Components and Systems

View full text Add to dashboard Cite

“…198.064 (6) 252.258 (7) 285.161 (8) 363.225 (9) 388.386 (10) 494.193 (11) 506.451 (12) 641.289 (13) 645.160 (14) 729.031 (15) 792.256 (16) 816.773 (17) 939.998 (18) 1008.385 (19) 1045.159 (20) 1161.288 (21) 1283.868 (22) 1374.191 (23) 1535.481 (24) 1690.319 (25) 1696.771 (26) 1858.061 (27) 1890.319 (28) 1993.544 (29) 2180.641 (30) 2238.705 (31) 2290.318 (32) 2341.931 (33) 2477.414 (34) 2496.769 (35) 2503 …”

Section: The 72-bar Spatial Truss Exampleunclassified

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

Hu³

et al. 2017

Mathematical Problems in Engineering

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Firefly Algorithm (FA, for short) is inspired by the social behavior of fireflies and their phenomenon of bioluminescent communication. Based on the fundamentals of FA, two improved strategies are proposed to conduct size and topology optimization for trusses with discrete design variables. Firstly, development of structural topology optimization method and the basic principle of standard FA are introduced in detail. Then, in order to apply the algorithm to optimization problems with discrete variables, the initial positions of fireflies and the position updating formula are discretized. By embedding the random-weight and enhancing the attractiveness, the performance of this algorithm is improved, and thus an Improved Firefly Algorithm (IFA, for short) is proposed. Furthermore, using size variables which are capable of including topology variables and size and topology optimization for trusses with discrete variables is formulated based on the Ground Structure Approach. The essential techniques of variable elastic modulus technology and geometric construction analysis are applied in the structural analysis process. Subsequently, an optimization method for the size and topological design of trusses based on the IFA is introduced. Finally, two numerical examples are shown to verify the feasibility and efficiency of the proposed method by comparing with different deterministic methods.

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“…In Standard Binary PSO proposed by [2],binary patterns are generated by employing a sigmoid function having a range within [0,1] with an input from basic velocity update equations. Researchers have proposed several variants of BPSO ( [4], [5], [6] etc). Interestingly, in most of the versions basic sigmoid function is used for generating the binary numbers.…”

Section: Introductionmentioning

confidence: 99%

Novel Binary PSO for Continuous Global Optimization Problems

Chauhan

Pant

Deep

2012

Advances in Intelligent and Soft Computing

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Abstract. The present study proposes a novel variant of binary PSO employing gompertz function for generating binary numbers with an additional benefit of controlling its parametrs so as to induce an combined effect of sigmoid as well as linear function. The corresponding variant is termend as Gompertz Binary Particle Swarm Optimization (GBPSO). The proposed GBPSO is tested on a a well known suite of 5 continuous benchmark fuctions. Numerical results indicate the competence of the proposed mechanism.

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A binary particle swarm optimization for continuum structural topology optimization

Cited by 111 publications

References 30 publications

Optimal Reconfiguration Comprising Voltage Stability Aspect Using Enhanced Binary Particle Swarm Optimization Algorithm

Optimal Reconfiguration Comprising Voltage Stability Aspect Using Enhanced Binary Particle Swarm Optimization Algorithm

Size and Topology Optimization for Trusses with Discrete Design Variables by Improved Firefly Algorithm

Novel Binary PSO for Continuous Global Optimization Problems

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