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Letteratura scientifica selezionata sul tema "HYBRIDIZED SEARCH"

Autore: Grafiati

Pubblicato: 11 settembre 2023

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Articoli di riviste sul tema "HYBRIDIZED SEARCH"

1

Lenin, Kanagasabai. "True power loss reduction by augmented mine blast algorithm." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 2 (2020): 83. http://dx.doi.org/10.11591/ijict.v9i2.pp83-91.

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In this paper, Mine Blast Algorithm (MBA) has been intermingled with Harmony Search (HS) algorithm for solving optimal reactive power dispatch problem. MBA is based on explosion of landmines and HS is based on Creativeness progression of musicians – both are hybridized to solve the problem. In MBA Initial distance of shrapnel pieces are reduced gradually to allow the mine bombs search the probable global minimum location in order to amplify the global explore capability. Harmony search (HS) imitates the music creativity process where the musicians supervise their instruments’ pitch by searchin

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2

Shankar, Rajendran, Narayanan Ganesh, Robert Čep, Rama Chandran Narayanan, Subham Pal, and Kanak Kalita. "Hybridized Particle Swarm—Gravitational Search Algorithm for Process Optimization." Processes 10, no. 3 (2022): 616. http://dx.doi.org/10.3390/pr10030616.

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The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection of optimum process parameter levels in any industrial process, numerous metaheuristic algorithms have been proposed so far. However, many algorithms are either computationally too expensive or become trapped in the pit of local optima. To counter these challenges, in this paper, a hybrid metaheuristic called PSO-GSA is employed that works by combining the iterative improvement capability of particle swarm optimization (PSO) and gravitational search algorithm (GSA

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3

Gouthamkumar, N., Veena Sharma, and R. Naresh. "Hybridized Gravitational Search Algorithm for Short-Term Hydrothermal Scheduling." IETE Journal of Research 62, no. 4 (2015): 468–78. http://dx.doi.org/10.1080/03772063.2015.1083904.

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4

Lenin, K. "A NOVEL HYBRIDIZED ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research -GRANTHAALAYAH 5, no. 11 (2017): 316–24. http://dx.doi.org/10.29121/granthaalayah.v5.i11.2017.2358.

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This paper proposes Hybridization of Gravitational Search algorithm with Simulated Annealing algorithm (HGS) for solving optimal reactive power problem. Individual position modernize strategy in Gravitational Search Algorithm (GSA) may cause damage to the individual position and also the local search capability of GSA is very weak. The new HGS algorithm introduced the idea of Simulated Annealing (SA) into Gravitational Search Algorithm (GSA), which took the Metropolis-principle-based individual position modernize strategy to perk up the particle moves, & after the operation of gravitation,

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5

Tang, Z., X. Hu, and J. Périaux. "Multi-level Hybridized Optimization Methods Coupling Local Search Deterministic and Global Search Evolutionary Algorithms." Archives of Computational Methods in Engineering 27, no. 3 (2019): 939–75. http://dx.doi.org/10.1007/s11831-019-09336-w.

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6

Beauregard, J., B. Ritz, E. W. Jenkins, K. R. Kavanagh, and M. W. Farthing. "Optimization of a Basin Network Using Hybridized Global Search Algorithms." Journal of Irrigation and Drainage Engineering 144, no. 8 (2018): 04018017. http://dx.doi.org/10.1061/(asce)ir.1943-4774.0001310.

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7

Khanum, Rashida Adeeb, Muhammad Asif Jan, Nasser Mansoor Tairan, and Wali Khan Mashwani. "Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method." Journal of Optimization 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/3260940.

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Abstract (sommario):

Differential evolution (DE) is an effective and efficient heuristic for global optimization problems. However, it faces difficulty in exploiting the local region around the approximate solution. To handle this issue, local search (LS) techniques could be hybridized with DE to improve its local search capability. In this work, we hybridize an updated version of DE, adaptive differential evolution with optional external archive (JADE) with an expensive LS method, Broydon-Fletcher-Goldfarb-Shano (BFGS) for solving continuous unconstrained global optimization problems. The new hybrid algorithm is

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8

Zaman, Fawad, Ijaz Mansoor Qureshi, Ata Ur Rehman, and Shujaat Ali Khan Tanoli. "Multiple Target Localization with Bistatic Radar Using Heuristic Computational Intelligence Techniques." International Journal of Antennas and Propagation 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/982967.

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We assume Bistatic Phase Multiple Input Multiple Output radar having passive Centrosymmetric Cross Shape Sensor Array (CSCA) on its receiver. Let the transmitter of this Bistatic radar send coherent signals using a subarray that gives a fairly wide beam with a large solid angle so as to cover up any potential relevant target in the near field. We developed Heuristic Computational Intelligence (HCI) based techniques to jointly estimate the range, amplitude, and elevation and azimuth angles of these multiple targets impinging on the CSCA. In this connection, first the global search optimizers, t

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9

Rao, Yunqing, Peng Wang, and Qiang Luo. "Hybridizing Beam Search with Tabu Search for the Irregular Packing Problem." Mathematical Problems in Engineering 2021 (January 21, 2021): 1–14. http://dx.doi.org/10.1155/2021/5054916.

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Abstract (sommario):

The irregular packing problem involves arranging all the irregular pieces on a plate with the objective of maximizing the use of material. In this article, the layout is formed by the ordered sequence of the irregular pieces which is obtained by a hybrid search algorithm and where the order is decoded by a proposed placement principle. First, a novel no-fit-polygon (NFP) generator is introduced. Then, a placement principle is presented with the new NFP generator. Finally, a search algorithm hybridized with beam search (BS) and tabu search (TS) is proposed to search over the sequence. The numer

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10

Li, Yan Ling, and Gang Li. "Mean Shift Segmentation Algorithm Based on Hybridized Bacterial Chemotaxis." Advanced Materials Research 468-471 (February 2012): 2019–23. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.2019.

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Abstract (sommario):

Mean shift, like other gradient ascent optimization methods, is susceptible to local maximum/minimum, and hence often fails to find the desired global maximum/minimum. For this reason, mean shift segmentation algorithm based on hybridized bacterial chemotaxis (HBC) is proposed in this paper. In HBC, particle swarm operation algorithm(PSO) is introduced before bacterial chemotaxis(BC) works. And PSO is firstly introduced to execute the global search, and then stochastic local search works by BC. Meanwhile, elitism preservation is used in the paper in order to improve the efficiency of the new a

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