Embedding parts in shape grammars using a parallel particle swarm optimization method on graphics processing units

Keleş, Hacer Yalım

doi:10.1017/s089006041700052x

Cited by 4 publications

(3 citation statements)

References 28 publications

(36 reference statements)

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“…One way to profit from such powerful hardware is to run multiple swarms simultaneously. This kind of implementation can be seen in [24,47,[47][48][49][50][51][52][53][54][55][56]. SI algorithms have a non-deterministic behavior, therefore each run typically produces different results.…”

Section: Parallel and Cooperative Swarmsmentioning

confidence: 99%

Parallelization of Swarm Intelligence Algorithms: Literature Review

Menezes

Kuchen²,

Neto³

2022

Int J Parallel Prog

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Swarm Intelligence (SI) algorithms are frequently applied to tackle complex optimization problems. SI is especially used when good solutions are requested for NP hard problems within a reasonable response time. And when such problems possess a very high dimensionality, a dynamic nature, or present intrinsic complex intertwined independent variables, computational costs for SI algorithms may still be too high. Therefore, new approaches and hardware support are needed to speed up processing. Nowadays, with the popularization of GPU and multi-core processing, parallel versions of SI algorithms can provide the required performance on those though problems. This paper aims to describe the state of the art of such approaches, to summarize the key points addressed, and also to identify the research gaps that could be addressed better. The scope of this review considers recent papers mainly focusing on parallel implementations of the most frequently used SI algorithms. The use of nested parallelism is of particular interest, since one level of parallelism is often not sufficient to exploit the computational power of contemporary parallel hardware. The sources were main scientific databases and filtered accordingly to the set requirements of this literature review.

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Section: Parallel and Cooperative Swarmsmentioning

confidence: 99%

Parallelization of Swarm Intelligence Algorithms: Literature Review

Menezes

Kuchen²,

Neto³

2022

Int J Parallel Prog

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“…The algorithm starts with the generation of random particles within the specified domain and searching for the global optimum solution by updating generation. This algorithm has the capability to serve fine optimum results compared with traditional techniques (Keles, 2018).…”

Section: Intelligent Optimization Proceduresmentioning

confidence: 99%

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Pourmostaghimi

Zadshakoyan

Badamchizadeh

2020

AIEDAM

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This paper proposes an intelligent model-based optimization methodology for optimizing the production cost and material removal rate subjected to surface quality constraint in turning operation of hardened AISI D2. Unlike traditional approaches, this paper deals with finding optimum cutting parameters considering the real condition of the cutting tool. Tool flank wear is predicted by the model obtained using genetic programming. On the basis of the predicted flank wear value, the surface roughness of work piece is estimated by neural networks. Applying the particle swarm optimization algorithm, the optimum machining parameters are determined. The simulation and experimental results show that machining with proposed intelligent optimization methodology has higher efficiency than conventional techniques with constant optimized cutting parameters.

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“…In addition, CPT is a commonly used soil exploration survey in engineering applications because it is quick, economic, and could continuously obtain physical soil parameters (Eslami, 1997; Abu-Farsakh and Titi, 2004). In recent years, some researchers developed concepts of advanced artificial intelligence (AI) in different engineering applications, such as engineering mechanics, design of energy-saving buildings, automation, and construction (Coello and Aguirre, 2002; Nariman-Zadeh et al ., 2003; Hanna, 2007; Padmini et al ., 2008; Rossi and Lanzetta, 2013; Hu et al ., 2014; Zeng et al ., 2014; Kontovourkis et al ., 2015; Salido et al ., 2015; Adeniran and El-Ferik, 2017; Persson and Ölvander, 2017; Thimmisetty et al ., 2017; Al-Refaie et al ., 2018; Keles, 2018; Lin and Huang, 2018; Mirabi and Seddighi, 2018; Zimmermann et al ., 2018). In recent years, applications of AI models have become widespread through civil engineering projects (e.g., Armaghani et al ., 2017, 2018; Koopialipoor et al ., 2018).…”

Section: Introductionmentioning

confidence: 99%

Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

Harandizadeh

2020

AIEDAM

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This research intends to investigate a new hybrid artificial intelligence (AI) technique compared to some common CPT methods in estimating axial ultimate pile bearing capacity (UPBC) using cone penetration test (CPT) data in geotechnical engineering applications. A data series of 108 samples was collected in order to develop a new hybrid structure of an adaptive neuro-fuzzy inference system (ANFIS) network, and the group method of the data handling (GMDH) type neural network was optimized by applying the particle swarm optimization (PSO) algorithm over the hybrid ANFIS-GMDH topology, which leads to a new hybrid AI model called as ANFIS-GMDH-PSO. The derived database provides information related to pile load tests, in situ field CPT data, and soil–pile information for introducing the proposed hybrid neural system. The cross-section of the pile toe, average cone tip resistance along embedded pile length, and sleeve frictional resistance along the shaft had been considered as input parameters for the proposed network. The results of this research indicated that the proposed ANFIS-GMDH-PSO model predicted the UPBC with an acceptable precision compared to various CPT methods, including Schmertmann, De Kuiter & Bringen, and LPC/LPCT methods. Moreover, ANFIS-GMDH-PSO network model performance was compared to CPT-based models in terms of statistical criteria in order to achieve a best fitted model. From the statistical results, it was found that the developed ANFIS-GMDH-PSO model has achieved a higher accuracy level in terms of statistical indices compared to CPT-based empirical methods, such as Schmertmann model, De Kuiter & Beringen model, and Bustamante & Gianeselli for predicting driven pile ultimate bearing capacity.

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Embedding parts in shape grammars using a parallel particle swarm optimization method on graphics processing units

Cited by 4 publications

References 28 publications

Parallelization of Swarm Intelligence Algorithms: Literature Review

Parallelization of Swarm Intelligence Algorithms: Literature Review

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Developing a new hybrid soft computing technique in predicting ultimate pile bearing capacity using cone penetration test data

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