A
Ali Kaveh
Researcher at Iran University of Science and Technology
Publications - 792
Citations - 21600
Ali Kaveh is an academic researcher from Iran University of Science and Technology. The author has contributed to research in topics: Metaheuristic & Optimization problem. The author has an hindex of 58, co-authored 753 publications receiving 16647 citations. Previous affiliations of Ali Kaveh include Vienna University of Technology & University of Vienna.
Papers
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A novel heuristic optimization method: charged system search
Ali Kaveh,Siamak Talatahari +1 more
TL;DR: A comparison of the results with those of other evolutionary algorithms shows that the proposed algorithm outperforms its rivals.
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A new meta-heuristic method: Ray Optimization
Ali Kaveh,Mojtaba Khayatazad +1 more
TL;DR: A new meta-heuristic method, so-called Ray Optimization, is developed, which has a number of particles consisting of the variables of the problem considered as rays of light based on the Snell's light refraction law.
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Colliding bodies optimization: A novel meta-heuristic method
Ali Kaveh,V.R. Mahdavi +1 more
TL;DR: In this paper, the authors presented a novel efficient meta-heuristic optimization algorithm called Colliding Bodies Optimization (CBO), which is based on one-dimensional collisions between bodies, with each agent solution being considered as an object or body with mass.
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Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures
Ali Kaveh,Siamak Talatahari +1 more
TL;DR: In this paper, a heuristic particle swarm ant colony optimization (HPSACO) is presented for optimum design of trusses, which is based on the particle swarm optimizer with passive congregation (PSOPC), ant colony optimizer and harmony search scheme.
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A new optimization method: Dolphin echolocation
Ali Kaveh,N. Farhoudi +1 more
TL;DR: Dolphin echolocation takes advantages of these rules and outperforms many existing optimization methods, while it has few parameters to be set, and this approach leads to excellent results with low computational efforts.