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Institution

Iran University of Science and Technology

EducationTehran, Iran
About: Iran University of Science and Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Control theory & Nonlinear system. The organization has 12917 authors who have published 24965 publications receiving 372013 citations. The organization is also known as: Governmental Technical Institute & Advanced Art College.


Papers
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Journal ArticleDOI
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.

452 citations

Journal ArticleDOI
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.

427 citations

Journal ArticleDOI
01 Jan 2010
TL;DR: A new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem is presented, called FAPSO-ACO-K, which can find better cluster partition and is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO-SA).
Abstract: Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO-K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO-SA), combination of ACO and SA (ACO-SA), combination of PSO and ACO (PSO-ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.

413 citations

Journal ArticleDOI
TL;DR: In this article, a residential energy hub model is proposed which receives electricity, natural gas and solar radiation at its input port to supply required electrical, heating and cooling demands at the output port.

411 citations

Journal ArticleDOI
TL;DR: The results show the effectiveness of the proposed control structure in compensating the voltage unbalance in an islanded microgrid.
Abstract: The concept of microgrid hierarchical control is presented recently. In this paper, a hierarchical scheme is proposed which includes primary and secondary control levels. The primary level comprises distributed generators (DGs) local controllers. The local controllers mainly consist of power, voltage and current controllers, and virtual impedance control loop. The central secondary controller is designed to manage the compensation of voltage unbalance at the point of common coupling (PCC) in an islanded microgrid. Unbalance compensation is achieved by sending proper control signals to the DGs local controllers. The design procedure of the control system is discussed in detail and the simulation results are presented. The results show the effectiveness of the proposed control structure in compensating the voltage unbalance.

407 citations


Authors

Showing all 13049 results

NameH-indexPapersCitations
Peter Hall132164085019
Josep M. Guerrero110119760890
Rahman Saidur9757634409
Victor C. M. Leung91158540397
Mehdi Dehghan8387529225
Amir H. Gandomi6737522192
Toraj Mohammadi6439414043
Emil Björnson6245817954
Amir A. Zadpoor6129411653
Majid R. Ayatollahi6037310771
Ali Kaveh5875316647
David Andrew Barry5746213363
Miguel A. Mariño532918304
Ali Saberi5144810959
Ali Maleki513768853
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202371
2022233
20212,309
20202,289
20191,915
20181,746