Device Simulation–Based Multiobjective Evolutionary Algorithm for Process Optimization of Semiconductor Solar Cells

Li, Yiming; Chen, Yuyu; Chen, Chieh-Yang; Shen, Cheng-Han; Cheng, Hui-Wen; Lo, I-Hsiu; Chen, Chun‐Nan

doi:10.1080/10426914.2013.792433

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…20 Several subsequent publications have dealt with diverse materials including iron and steel, 21,22 residual stress minimisation during machining, 34 semiconductor solar cell design, 35 iron ore sintering, 36 etc. have come under its purview, in addition to materials studies of more fundamental nature, 37 providing vital insights to materials design, fabrication and processing.…”

Section: Some Recent Evolutionary Studiesmentioning

confidence: 99%

“…20 Several subsequent publications have dealt with diverse materials including iron and steel, 21,22 titanium alloys, 23,24 bulk metallic glasses, 25,26 composites, 27,28 industrial chemicals, 29 polymers, 30 and so on. Diverse processes such as friction stir welding, 31,32 complex sheet metal forming, 33 residual stress minimisation during machining, 34 semi-conductor solar cell design, 35 iron ore sintering, 36 etc. have come under its purview, in addition to materials studies of more fundamental nature, 37 providing vital insights to materials design, fabrication and processing.…”

Section: Some Recent Evolutionary Studiesmentioning

confidence: 99%

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Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research

Chakraborti

2014

Materials Science and Technology

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The current state of the art of materials research using multi-objective genetic and evolutionary algorithms is briefly presented with critical analyses. The basic concepts of multi-objective optimisation and Pareto optimality are explained in simple terms and the advantages of an evolutionary approach are emphasised. Current materials related research in this area is summarised, focusing on the achievements to date and the specific needs for further improvement.

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Section: Some Recent Evolutionary Studiesmentioning

confidence: 99%

Section: Some Recent Evolutionary Studiesmentioning

confidence: 99%

Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research

Chakraborti

2014

Materials Science and Technology

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“…In [18], the authors have performed process optimization of semiconductor solar cells using multiobjective optimization model and have applied evolutionary algorithms for solving it. Multiobjective nonlinear system identification of controlled series capacitors is considered in [19].…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Electrical Performance of Polymeric Films

Tyagi

Pant

Negi

et al. 2014

Materials and Manufacturing Processes

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In the present study, an attempt is made to optimize the electrical performance of the thin polymeric films through optimization techniques. The study is conducted in two phases: (1) laboratory experiments and (2) through numerical optimization. For laboratory analysis, thin and transparent films are prepared using polyethersulfone (PES) as host material and meta-nitroaniline (MNA) as guest materials. A set of nine film samples are prepared by the solution casting method in the laboratory using different concentrations of MNA. The electrical properties capacitance, conductance, and dissipation factor of films are measured by Aligent Impedance Analyzer. These characteristics are then optimized mathematically. For this purpose, initially single-objectives are considered for optimizing the electrical properties individually, and later a multiobjective model is considered for analyzing the properties simultaneously. The algorithms employed are metaheuristics: genetic algorithms, particle swarm optimization, differential evolution, and its variant modified differential evolution along with fmincon (a MATLAB toolbox) for single-objective optimization and multiobjective differential evolution algorithm and nondominated sorting genetic algorithm-II for multiobjective optimization.

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“…9-21 Given the fact that many complex engineering problems require decision making in terms of more than one conflicting criteria, the modern evolutionary algorithms are increasingly attempting to tackle such multiobjective problems, and those are now fully geared to compute the so called Pareto optimality: the best possible tradeoff between two or more objectives that are in mutual conflict. [22][23][24][25][26][27][28][29][30][31][32][33][34][35] The notion of Pareto optimality, named after legendary European mathematician and social scientist Vilfredo Pareto , is established through rigorous and complex mathematics. Figure 1 is a light hearted effort to capture the essence of it.…”

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confidence: 99%

Promise of multiobjective genetic algorithms in coating performance formulation

Chakraborti¹

2014

Surface Engineering

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Tamilnadu Surface Engineering provides a forum for the publication of refereed material on both the theory and practice of this important enabling technology, embracing science, technology, and engineering. Contributions are invited on any aspect of the use of surface engineering to produce substrate-surface systems having mechanical, tribological, chemical, and/ or functional properties that cannot be achieved from the individual components alone. Coverage includes design, surface modification technologies and process control, and the characterisation and properties of the final system or component, including quality control and non-destructive examination. Advance Articles are published online ahead of print, with a DOi for citation purposes, once proof corrections and a copyright transfer form are received. Submissions New submissions should be uploaded online at http://sur.edmgr.com ; see inside back cover or www.maneyonline.com/sur for details. Submissions are vetted for quality of English and basic structure before being sent for review.

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Device Simulation–Based Multiobjective Evolutionary Algorithm for Process Optimization of Semiconductor Solar Cells

Cited by 6 publications

References 30 publications

Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research

Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research

Optimization of the Electrical Performance of Polymeric Films

Promise of multiobjective genetic algorithms in coating performance formulation

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