Using a novel parallel genetic hybrid algorithm to generate and determine new zeolite frameworks

Abdelkafi, Omar; Idoumghar, Lhassane; Lepagnot, Julien; Paillaud, Jean-Louis; Deroche, Irena; Baumes, Laurent A.; Collet, Pierre

doi:10.1016/j.compchemeng.2016.11.036

Cited by 11 publications

(4 citation statements)

References 42 publications

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“…Especially after CUDA (compute unified device architecture) platform were distributed, developing highly parallel GPU applications becomes much easier. GPUs are very well to address general problems that are suitable for data-parallel computations, including GA [23][24][25]. The IMGA, a parallel genetic algorithm model, can fully explore the computing power by either coarse or fine-grained parallelisms.…”

Section: Introductionmentioning

confidence: 99%

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

et al. 2018

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Facility layout problem (FLP) is one of the hottest research areas in industrial engineering. A good facility layout can achieve efficient production management, improve production efficiency, and create high economic values. Because FLP is an NP-hard problem, meaning it is impossible to find the optimal solution when problem becomes sufficiently large, various evolutionary algorithms (EAs) have been proposed to find a sub-optimal solution within a reasonable time interval. Recently, a genetic algorithm (GA) was proposed for unequal area FLP (UA-FLP), where the areas of facilities are not identical. More precisely, the GA is an island model based, which is called IMGA. Since EAs are still very time consuming, many efforts have been devoted to how to parallelize various EAs including IMGA. In recent work, Steffen and Dietmar proposed how to parallelize island models of EAs. However, their parallelization approaches are preliminary because they focused mainly on comparing the performances between different parallel architectures. In addition, they used one mathematical function to model the problem. To further investigate on how to parallelize the IMGA by GPU, in this paper we propose multiple parallel algorithms, for each individual step in the IMGA when solving the industrial engineering problem, UA-FLP, and conduct experiments to compare their performances. After integrating better algorithms for all steps into the IMGA, our GPU implementation outperforms the CPU counterpart and the best speedup can be as high as 84.

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Section: Introductionmentioning

confidence: 99%

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

et al. 2018

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“…For example, Matsuoka et al applied a combination of descriptor-based techniques, inexpensive force-fields, and DFT to identify strong Brønsted acid sites in a database of over 500,000 zeolite structures . Evolutionary algorithms have also been applied to accelerate the determination of zeolite structures, , providing a possible route to determine active site structures without relying on databases. These examples suggest that the numerous advances in machine-learning for adsorption energy prediction (see section ) combined with search and optimization algorithms will enable comprehensive determination of active-site structures for bottom-up models in the future.…”

Section: From Data To Knowledgementioning

confidence: 99%

Extracting Knowledge from Data through Catalysis Informatics

et al. 2018

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Catalysis informatics is a distinct subfield that lies at the intersection of cheminformatics and materials informatics but with distinctive challenges arising from the dynamic, surface-sensitive, and multiscale nature of heterogeneous catalysis. The ideas behind catalysis informatics can be traced back decades, but the field is only recently emerging due to advances in data infrastructure, statistics, machine learning, and computational methods. In this work, we review the field from early works on expert systems and knowledge engines to more recent approaches utilizing machine-learning and uncertainty quantification. The data–information–knowledge hierarchy is introduced and used to classify various developments. The chemical master equation and microkinetic models are proposed as a quantitative representation of catalysis knowledge, which can be used to generate explanative and predictive hypotheses for the understanding and discovery of catalytic materials. We discuss future prospects for the field, including improved quantitative coupling of experiment/theory, advanced microkinetic models, and the development of open-source software tools. Ultimately, integration of existing chemical and physical models with emerging statistical and computational tools presents a promising route toward the automated design, discovery, and optimization of heterogeneous catalytic processes.

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“…These can simultaneously deal with a set of possible solutions without requiring series of separate runs, thus enabling the direct investigation of the multi-objective problem (Coello et al, 2007). As a result, population-based algorithms have been a popular choice among many researchers for the MOO of various systems, including truss design (Ray et al, 2001), thermal system design (Toffolo and Lazzaretto, 2002), environmental economic power dispatch (Gong et al, 2010; Wang and Singh, 2007), beam design (Sanchis et al, 2008), water distribution network design (di Pierro et al, 2009) and more recently the MOO of zeolite framework determination (Abdelkafi et al, 2017). In addition to these, the books by Rangaiah and Bonilla-Petriciolet (2013), and Coello et al (2007) demonstrate a plethora of applications of evolutionary algorithms to numerous MOO problems.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of energy systems using constrained grey-box multi-objective optimization

Beykal

Boukouvala

Floudas

et al. 2018

Computers & Chemical Engineering

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The (global) optimization of energy systems, commonly characterized by high-fidelity and large-scale complex models, poses a formidable challenge partially due to the high noise and/or computational expense associated with the calculation of derivatives. This complexity is further amplified in the presence of multiple conflicting objectives, for which the goal is to generate trade-off compromise solutions, commonly known as Pareto-optimal solutions. We have previously introduced the p-ARGONAUT system, parallel AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems, which is designed to optimize general constrained single objective grey-box problems by postulating accurate and tractable surrogate formulations for all unknown equations in a computationally efficient manner. In this work, we extend p-ARGONAUT towards multi-objective optimization problems and test the performance of the framework, both in terms of accuracy and consistency, under many equality constraints. Computational results are reported for a number of benchmark multi-objective problems and a case study of an energy market design problem for a commercial building, while the performance of the framework is compared with other derivative-free optimization solvers.

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Using a novel parallel genetic hybrid algorithm to generate and determine new zeolite frameworks

Cited by 11 publications

References 42 publications

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

Extracting Knowledge from Data through Catalysis Informatics

Optimal design of energy systems using constrained grey-box multi-objective optimization

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