Socio-Inspired Optimization Methods for Advanced Manufacturing Processes (eBook)

This book discusses comprehensively the advanced manufacturing processes, including illustrative examples of the processes, mathematical modeling, and the need to optimize associated parameter problems. In addition, it describes in detail the cohort intelligence methodology and its variants along with illustrations, to help readers gain a better understanding of the framework. The theoretical and statistical rigor is validated by comparing the solutions with evolutionary algorithms, simulation annealing, response surface methodology, the firefly algorithm, and experimental work. Lastly, the book critically reviews several socio-inspired optimization methods. 

?Apoorva S Shastri holds a Master of Technology (M.Tech) in VLSI Design and Bachelor of Engineering in Electronics & Product Design Technology from R.T.M.N.U, Nagpur. She has also done Diploma from the Govt. Polytechnic, Nagpur. She worked as a guest faculty at Centre for Development of Advanced Computing (C-DAC), Pune. Currently, she is Assistant Professor at the Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune. She is also pursuing PhD in Optimization Algorithms and Applications from Symbiosis International (Deemed University). Her research interests include development of optimization algorithms, VLSI design, multi-objective optimization, continuous, discrete and combinatorial optimization, complex systems, probability collectives and self-organizing systems. Apoorva developed socio-inspired optimization methodologies such as Multi-Cohort Intelligence Algorithm and Expectation Algorithm. Apoorva has published several research papers in peer-reviewed journals, chapters and conferences.

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This book discusses comprehensively the advanced manufacturing processes, including illustrative examples of the processes, mathematical modeling, and the need to optimize associated parameter problems. In addition, it describes in detail the cohort intelligence methodology and its variants along with illustrations, to help readers gain a better understanding of the framework. The theoretical and statistical rigor is validated by comparing the solutions with evolutionary algorithms, simulation annealing, response surface methodology, the firefly algorithm, and experimental work. Lastly, the book critically reviews several socio-inspired optimization methods.