A robust parallel algorithm of the particle swarm optimization method for large dimensional engineering problems

Moraes, Antonio O.S.; Mitre, João Felipe; Lage, Paulo L.C.; Secchi, Argimiro Resende

doi:10.1016/j.apm.2014.12.034

Cited by 26 publications

(13 citation statements)

References 29 publications

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“…Our implementation of PSO is based on ref. [25]. Our implementation of SS follows the outline presented in the introduction of ref.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…Such algorithms often include some type of iterative randomized selection of candidate parameter sets, followed by evaluation of the selected parameter sets, which is used to direct the selection of parameters in future iterations to more favorable regions of parameter space. Many modern descriptions of metaheuristics allow for parallelized evaluation of parameter sets (Moraes et al., 2015, Penas et al., 2015, Penas et al., 2017), which is valuable when each model simulation is computationally expensive. Although these algorithms are well-established, software designed for biological applications is limited.…”

Section: Introductionmentioning

confidence: 99%

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PyBioNetFit and the Biological Property Specification Language

et al. 2019

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SummaryIn systems biology modeling, important steps include model parameterization, uncertainty quantification, and evaluation of agreement with experimental observations. To help modelers perform these steps, we developed the software PyBioNetFit, which in addition supports checking models against known system properties and solving design problems. PyBioNetFit introduces Biological Property Specification Language (BPSL) for the formal declaration of system properties. BPSL allows qualitative data to be used alone or in combination with quantitative data. PyBioNetFit performs parameterization with parallelized metaheuristic optimization algorithms that work directly with existing model definition standards: BioNetGen Language (BNGL) and Systems Biology Markup Language (SBML). We demonstrate PyBioNetFit's capabilities by solving various example problems, including the challenging problem of parameterizing a 153-parameter model of cell cycle control in yeast based on both quantitative and qualitative data. We demonstrate the model checking and design applications of PyBioNetFit and BPSL by analyzing a model of targeted drug interventions in autophagy signaling.

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“…Our implementation of PSO is based on ref. [25]. Our implementation of SS follows the outline presented in the introduction of ref.…”

Section: Implementation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PyBioNetFit and the Biological Property Specification Language

et al. 2019

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show abstract

“…A feature of many but not all of these algorithms is the maintenance of a population of good parameter sets, which are used to generate new trial parameter sets. Many modern descriptions of population-based metaheuristic algorithms (e.g [39,40,41]) allow for parallelized function evaluations within a single run of the algorithm, which enables these algorithms to take advantage of high-performance computing resources.…”

Section: Metaheuristic Optimizationmentioning

confidence: 99%

“…Note that the parallelization of these algorithms is not simply from performing multiple fitting replicates (which can be trivially done for any algorithm); evaluations are parallelized within each iteration of the algorithm. Some metaheuristic algorithms (e.g., [40]) are asynchronous. Such algorithms improve load balancing by running simulations on all available cores at all times (cores are never left idle).…”

Section: Metaheuristic Optimizationmentioning

confidence: 99%

Parameter estimation and uncertainty quantification for systems biology models

Mitra

Hlavacek

2019

Current Opinion in Systems Biology

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Mathematical models can provide quantitative insight into immunoreceptor signaling, but require parameterization and uncertainty quantification before making reliable predictions. We review currently available methods and software tools to address these problems. We consider gradient-based and gradient-free methods for point estimation of parameter values, and methods of profile likelihood, bootstrapping, and Bayesian inference for uncertainty quantification. We consider recent and potential future applications of these methods to systems-level modeling of immune-related phenomena. Highlights• Models of immunoreceptor signaling often contain parameters that must be fit to data.• New tools including PyBioNetFit and AMICI support automated parameter estimation.• Optimization algorithms can be used to obtain point estimates of parameter values.• Parameter estimation can incorporate both quantitative and qualitative data.• Uncertainty quantification assesses confidence in parameter values and model predictions.

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“…The same is also a valid case for algorithms that are inherently feasible for parallelism such as a genetic algorithm (GA). However, the real implementation of parallelism for such algorithms may be challenged by the fact that many current computing platforms inherent serial processing mechanism of von-Neumann architecture [1,2]. This makes the implementation of parallel information processing complicated and expensive [3,4].…”

Section: Introductionmentioning

confidence: 99%

Fine-grained or coarse-grained? Strategies for implementing parallel genetic algorithms in a programmable neuromorphic platform

Sugiarto¹,

Furber²

2021

TELKOMNIKA

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Genetic algorithm (GA) is one of popular heuristic-based optimization methods that attracts engineers and scientists for many years. With the advancement of multiand many-core technologies, GAs are transformed into more powerful tools by parallelising their core processes. This paper describes a feasibility study of implementing parallel GAs (pGAs) on a SpiNNaker. As a many-core neuromorphic platform, SpiNNaker offers a possibility to scale-up a parallelised algorithm, such as a pGA, whilst offering low power consumption on its processing and communication overhead. However, due to its small packets distribution mechanism and constrained processing resources, parallelising processes of a GA in SpiNNaker is challenging. In this paper we show how a pGA can be implemented on SpiNNaker and analyse its performance. Due to inherently numerous parameter and classification of pGAs, we evaluate only the most common aspects of a pGA and use some artificial benchmarking test functions. The experiments produced some promising results that may lead to further developments of massively parallel GAs on SpiNNaker. This is an open access article under the CC BY-SA license.

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A robust parallel algorithm of the particle swarm optimization method for large dimensional engineering problems

Cited by 26 publications

References 29 publications

PyBioNetFit and the Biological Property Specification Language

PyBioNetFit and the Biological Property Specification Language

Parameter estimation and uncertainty quantification for systems biology models

Fine-grained or coarse-grained? Strategies for implementing parallel genetic algorithms in a programmable neuromorphic platform

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