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Amir H. Gandomi

Researcher at University of Technology, Sydney

Publications -  530
Citations -  37572

Amir H. Gandomi is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Computer science & Optimization problem. The author has an hindex of 67, co-authored 375 publications receiving 22192 citations. Previous affiliations of Amir H. Gandomi include Michigan State University & University of Akron.

Papers
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Salp Swarm Algorithm

TL;DR: The qualitative and quantitative results prove the efficiency of SSA and MSSA and demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.
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Beyond the hype

TL;DR: The need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats is highlighted and the need to devise new tools for predictive analytics for structured big data is reinforced.
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Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems

TL;DR: The performance of the CS algorithm is further compared with various algorithms representative of the state of the art in the area and the optimal solutions obtained are mostly far better than the best solutions obtained by the existing methods.
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Krill herd: A new bio-inspired optimization algorithm

TL;DR: The proposed KH algorithm, based on the simulation of the herding behavior of krill individuals, is capable of efficiently solving a wide range of benchmark optimization problems and outperforms the exciting algorithms.
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Bat algorithm: a novel approach for global engineering optimization

TL;DR: A new nature‐inspired metaheuristic optimization algorithm, called bat algorithm (BA), based on the echolocation behavior of bats is introduced, and the optimal solutions obtained are better than the best solutions obtained by the existing methods.