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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Bio-inspired Algorithms for Evolving the Architecture of Convolutional Neural Networks

Bhandare, Ashray Sadashiv January 2017 (has links)
No description available.
2

Inverse Modeling: Theory and Engineering Examples

Yarlagadda, Rahul Rama Swamy January 2015 (has links)
No description available.
3

Multi-objective day-ahead scheduling of microgrids using modified grey wolf optimizer algorithm

Javidsharifi, M., Niknam, T., Aghaei, J., Mokryani, Geev, Papadopoulos, P. 10 August 2018 (has links)
Yes / Investigation of the environmental/economic optimal operation management of a microgrid (MG) as a case study for applying a novel modified multi-objective grey wolf optimizer (MMOGWO) algorithm is presented in this paper. MGs can be considered as a fundamental solution in order for distributed generators’ (DGs) management in future smart grids. In the multi-objective problems, since the objective functions are conflict, the best compromised solution should be extracted through an efficient approach. Accordingly, a proper method is applied for exploring the best compromised solution. Additionally, a novel distance-based method is proposed to control the size of the repository within an aimed limit which leads to a fast and precise convergence along with a well-distributed Pareto optimal front. The proposed method is implemented in a typical grid-connected MG with non-dispatchable units including renewable energy sources (RESs), along with a hybrid power source (micro-turbine, fuel-cell and battery) as dispatchable units, to accumulate excess energy or to equalize power mismatch, by optimal scheduling of DGs and the power exchange between the utility grid and storage system. The efficiency of the suggested algorithm in satisfying the load and optimizing the objective functions is validated through comparison with different methods, including PSO and the original GWO. / Supported in part by Royal Academy of Engineering Distinguished Visiting Fellowship under Grant DVF1617\6\45
4

Nature Inspired Grey Wolf Optimizer Algorithm for Minimizing Operating Cost in Green Smart Home

Lakshminarayanan, Srivathsan January 2015 (has links)
No description available.

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