N
Nirupam Chakraborti
Researcher at Indian Institute of Technology Kharagpur
Publications - 153
Citations - 3122
Nirupam Chakraborti is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Multi-objective optimization & Genetic algorithm. The author has an hindex of 28, co-authored 146 publications receiving 2813 citations. Previous affiliations of Nirupam Chakraborti include Pohang University of Science and Technology & Indian Institutes of Technology.
Papers
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Journal ArticleDOI
Optimization of Cellular Automata Model for the Heating of Dual-Phase Steel by Genetic Algorithm and Genetic Programming
TL;DR: In this paper, the authors considered a common metallurgical problem associated with the phase transformation of steel during heating where austenite grain tends to grow in size with time and results in poor mechanical properties in the final stages.
Journal ArticleDOI
Multiobjective Optimization of Manganese Recovery from Sea Nodules Using Genetic Algorithms
TL;DR: In this paper, the treatment of polymetallic sea nodules in an acid-based hydrometallurgical route has been proposed using two different schemes, batch and parallel in nature.
Journal ArticleDOI
Genetic algorithms based multi-objective optimization of an iron making rotary kiln
TL;DR: The optimization of the operation is carried out using multi-objective genetic algorithms and the resulting Pareto fronts conform to the existing trends and also suggest some possible improvements.
Journal ArticleDOI
Genetic Algorithms Applied to Li+ Ions Contained in Carbon Nanotubes: An Investigation Using Particle Swarm Optimization and Differential Evolution Along with Molecular Dynamics
TL;DR: In this article, the potential for using carbon nanotubes in the negative electrode of lithium ion batteries was also critically examined, where structural optimizations for various assemblages were conducted using evolutionary and genetic algorithms, where differential evolution and particle swarm optimization techniques worked satisfactorily.
Book ChapterDOI
Evolutionary Data-Driven Modeling
TL;DR: This chapter discusses two emerging algorithms, Evolutionary Neural Net (EvoNN) and Bi-objective Genetic Programming (BioGP), which utilize the concept of Pareto tradeoff and apply a bi-objectives genetic algorithm (GA) in the basic framework of both ANNs and GP.