Enhancing particle swarm optimization algorithm using two new strategies for optimizing design of truss structures

Lu, Yung Chi; Jan, J. C.; Hung, Shan Ling; Hung, Giun Yi

doi:10.1080/0305215x.2012.729054

Cited by 35 publications

(13 citation statements)

References 20 publications

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“…The computing performances of employed MOAs are summarized in Table 7. Considering Figures (6)(7)(8) and Tables (3-7), the preliminary results are listed as: • There does not exist any relation among the employed MOAs taking into account of the lower probability ratio (Prob>Chi-sq) as 2.42e-77 (see also the note as "3 groups have mean ranks significantly different from PAGA" in Fig. 9).…”

Section: Preliminary Results Obtained From Optimization Of Benchmark mentioning

confidence: 95%

“…Thus, the entire weight of tubular structure is minimized to obtain a further economic design. The design constraints are assigned to check the strengths of the lattice girder members according to an allowable member stress and/or joint displacement value (Cagnina and et al [6]; Chen & Huang [7]; Lu and et al [8]; Torii and et al [9]). However, it has to be taken into account of being governed the strength of members by a number of coupled strength-related criteria (Beer and et al [10]).…”

Section: Introductionmentioning

confidence: 99%

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Design Optimization of Tubular Lattice Girders

Talaslıoğlu¹

2019

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The lattice girder, members of which are constructed by use of ready profiles with tubular cross-sections, has a simple but an effective structural framing form. In this regard, this study proposes to optimize the design of tubular lattice girders i n a way of minimizing its entire weight and joint displacement and maximizin g its load-carrying capacity considering the design codes of API RP2A-LRFD. As an optimization tool, a multi-objective optimization methodology named pareto archived genetic algorithm (PAGA) was utilized. The search capability of PAGA was improved by invol ving a designer module for automatically creation of a lattice girder form. The improved PAGA has a big responsibility of increasing the convergence degree of optimal designs against the stability problem. Furthermore, the content of this study is enriched by evaluating the computing efficiency of PAGA with respect to several multi-objective optimization algorithms. Consequently, the improved PAGA achieves to explore the optimal lattice girder designs with the higher convergence, diversity and capacity degr ees. Therefore, the proposed optimum lattice girder design tool is recommended for the designers due to its capability of obtaining a wide range of promising designs.

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Section: Preliminary Results Obtained From Optimization Of Benchmark mentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Design Optimization of Tubular Lattice Girders

Talaslıoğlu¹

2019

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“…The weights obtained with the PLOR and DIRECT-l algorithms are better than with other two DIRECT-type algorithms and are close to the best known value obtained with the AugPSO algorithm [26]. The convergence curves (see Fig.…”

Section: -Bar Space Trussmentioning

confidence: 82%

“…The symmetrical truss is considered, therefore all truss elements are divided into 16 groups. Table 11 presents a comparison of the optimal solutions obtained with the four DIRECT-type algorithms, also with the Schmit method [36], Perez and Behdinan [35], and three (PSO, PSOPC and AugPSO) from the recent paper [26]. As in the 25-bar case, all DIRECT-type algorithms have been terminated after 150, 000 function evaluations.…”

Section: -Bar Space Trussmentioning

confidence: 99%

Application of Reduced-set Pareto-Lipschitzian Optimization to truss optimization

et al. 2015

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In this paper, a recently proposed global Lipschitz optimization algorithm PLOR (Pareto-Lipschitzian Optimization with Reduced-set) is further developed, investigated and applied to truss optimization problems. Partition patterns of the PLOR algorithm are similar to those of DIRECT (DIviding RECTangles), which was widely applied to different real-life problems. However here a set of all Lipschitz constants is reduced to just two: the maximal and the minimal ones. In such a way the PLOR approach is independent of any user-defined parameters and balances equally local and global search during the optimization process. An expanded list of other well-known DIRECT-type algorithms is used in investigation and experimental comparison using the standard test problems and truss optimization problems. The experimental investigation shows that the PLOR algorithm gives very competitive results to other DIRECT-type algorithms using standard test problems and performs pretty well on real truss optimization problems.

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“…As a gradient-free algorithm, it has been proved to be a feasible and effective method for solving difficult optimization problems in which the gradient is too laborious or even impossible to derive. In the past decade, more and more PSO versions have been developed Chang 2009, 2010;Fan and Chiu 2007;Hashemi, Rezapour and Moradi 2010;Lu et al 2013;Mahmoodabadi et al 2012;Sun et al 2011;Xu et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Self-regulating and self-evolving particle swarm optimizer

Wang

Qiao

Xia

et al. 2014

Engineering Optimization

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In this article, a novel self-regulating and self-evolving particle swarm optimizer (SSPSO) is proposed. Learning from the idea of direction reversal, self-regulating behaviour is a modified position update rule for particles, according to which the algorithm improves the best position to accelerate convergence in situations where the traditional update rule does not work. Borrowing the idea of mutation from evolutionary computation, self-evolving behaviour acts on the current best particle in the swarm to prevent the algorithm from prematurely converging. The performance of SSPSO and four other improved particle swarm optimizers is numerically evaluated by unimodal, multimodal and rotated multimodal benchmark functions. The effectiveness of SSPSO in solving real-world problems is shown by the magnetic optimization of a Halbach-based permanent magnet machine. The results show that SSPSO has good convergence performance and high reliability, and is well matched to actual problems.

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Enhancing particle swarm optimization algorithm using two new strategies for optimizing design of truss structures

Cited by 35 publications

References 20 publications

Design Optimization of Tubular Lattice Girders

Design Optimization of Tubular Lattice Girders

Application of Reduced-set Pareto-Lipschitzian Optimization to truss optimization

Self-regulating and self-evolving particle swarm optimizer

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