Relevant bibliographies by topics / HYBRIDIZED SEARCH

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  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'HYBRIDIZED SEARCH'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "HYBRIDIZED SEARCH"

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Lenin, Kanagasabai. "True power loss reduction by augmented mine blast algorithm." International Journal of Informatics and Communication Technology (IJ-ICT) 9, no. 2 (August 1, 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 searching for a best state of harmony. Hybridization of Mine Blast Algorithm with Harmony Search algorithm (MH) improves the search effectively in the solution space. Mine blast algorithm improves the exploration and harmony search algorithm augments the exploitation. At first the proposed algorithm starts with exploration & gradually it moves to the phase of exploitation. Proposed Hybridized Mine Blast Algorithm with Harmony Search algorithm (MH) has been tested on standard IEEE 14, 300 bus test systems. Real power loss has been reduced considerably by the proposed algorithm. Then Hybridized Mine Blast Algorithm with Harmony Search algorithm (MH) tested in IEEE 30, bus system (with considering voltage stability index)- real power loss minimization, voltage deviation minimization, and voltage stability index enhancement has been attained.
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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 (March 21, 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). A binary PSO is also fused with GSA to develop a BPSO-GSA algorithm. Both the hybrid algorithms i.e., PSO-GSA and BPSO-GSA, are compared against traditional algorithms, such as tabu search (TS), genetic algorithm (GA), differential evolution (DE), GSA and PSO algorithms. Moreover, another popular hybrid algorithm DE-GA is also used for comparison. Since earlier works have already studied the performance of these algorithms on mathematical benchmark functions, in this paper, two real-world-applicable independent case studies on biodiesel production are considered. Based on the extensive comparisons, significantly better solutions are observed in the PSO-GSA algorithm as compared to the traditional algorithms. The outcomes of this work will be beneficial to similar studies that rely on polynomial models.
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Gouthamkumar, N., Veena Sharma, and R. Naresh. "Hybridized Gravitational Search Algorithm for Short-Term Hydrothermal Scheduling." IETE Journal of Research 62, no. 4 (September 25, 2015): 468–78. http://dx.doi.org/10.1080/03772063.2015.1083904.

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Lenin, K. "A NOVEL HYBRIDIZED ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research -GRANTHAALAYAH 5, no. 11 (November 30, 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, Simulated Annealing operation has been applied to the optimal individual. In order to evaluate the efficiency of the proposed Hybridization of Gravitational Search algorithm with Simulated Annealing algorithm (HGS), it has been tested on standard IEEE 118 & practical 191 bus test systems and compared to the standard reported algorithms. Simulation results show that HGS is superior to other algorithms in reducing the real power loss and voltage profiles also within the limits.
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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 (March 27, 2019): 939–75. http://dx.doi.org/10.1007/s11831-019-09336-w.

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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 (August 2018): 04018017. http://dx.doi.org/10.1061/(asce)ir.1943-4774.0001310.

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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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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 denoted by DEELS. To validate the performance of DEELS, we carried out extensive experiments on well known test problems suits, CEC2005 and CEC2010. The experimental results, in terms of function error values, success rate, and some other statistics, are compared with some of the state-of-the-art algorithms, self-adaptive control parameters in differential evolution (jDE), sequential DE enhanced by neighborhood search for large-scale global optimization (SDENS), and differential ant-stigmergy algorithm (DASA). These comparisons reveal that DEELS outperforms jDE and SDENS except DASA on the majority of test instances.
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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, that is,are developed separately Particle Swarm Optimization (PSO) and Differential Evolution (DE) are developed separately, and, to enhance the performances further, both of them are hybridized with a local search optimizer called Active Set Algorithm (ASA). Initially, the performance of PSO, DE, PSO hybridized with ASA, and DE hybridized with ASA are compared with each other and then with some traditional techniques available in literature using root mean square error (RMSE) as figure of merit.
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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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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 numerical experiments with many benchmark problems show that the hybrid algorithm is an applicative and effective approach for solving the irregular packing problem. The hybrid algorithm can produce competitive solutions in less time than many other typical algorithms.
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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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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 algorithm. After mean shift vector is optimized using HBC algorithm, the optimal mean shift vector is updated using mean shift procedure. Experimental results show that new algorithm not only has higher convergence speed, but also can achieve more robust segmentation results.
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Dissertations / Theses on the topic "HYBRIDIZED SEARCH"

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PRASAD, MOHIT. "SOFTWARE EFFORT ESTIMATION USING HYBRIDIZED SEARCH BASED TECHNIQUES." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15248.

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Prediction of resource requirements of a software project is crucial for the timely delivery of quality-assured software within a reasonable timeframe. Software effort estimation is the process of prognosticating the amount of effort required to build a software project. Most cost estimation models attempts to generate an effort estimation, which can then be mapped into project duration and cost. Many conventional (model-based) and Artificial Intelligence (AI) oriented (model-free) resource estimators have been proposed in the recent past. In this thesis two search based Effort Estimation techniques are discussed .Firstly we evaluates a genetically trained neural network (NN) predictor trained on historical data. Secondly, Particle Swarm Optimization (PSO) technique which operates on data sets clustered using the K-means clustering algorithm. Hence PSO and Genetic Algorithm (GA) based search techniques are employed to perform optimized search in solution space. The comparison of this new predictor is established using n-fold cross validation and Student’s t-test. The data is obtained on various partitions of merged COCOMO data set and Kemerer data sets incorporating data from 78 real-life software projects. PSO is employed to generate parameters of the COCOMO (Constructive Cost Model) model for each cluster of data values. The clusters and effort parameters are then trained to a Neural Network by using Back propagation technique, for classification of data. Here we have tested the model on the COCOMO dataset and also compared the obtained values with standard COCOMO model. By making use of the experience from Neural Networks and the efficient tuning of parameters by PSO operating on clusters, the proposed model is able to generate comparable results and it can be applied efficiently to larger data sets. It goes without saying that a predictor trained on historical data can only be as accurate as the data set itself. Hence, there is a need to continue collection of data on diverse projects with wide range of attributes to construct a sizable historical database for training neural predictors. Using search based techniques to train NN; we are looking to overcome this limitation to possible extent.
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Book chapters on the topic "HYBRIDIZED SEARCH"

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Eslami, Mahdiyeh, Hussain Shareef, and Mohammad Khajehzadeh. "Firefly Algorithm and Pattern Search Hybridized for Global Optimization." In Intelligent Computing Theories and Technology, 172–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39482-9_20.

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Kumar, R., and P. K. Singh. "Pareto Evolutionary Algorithm Hybridized with Local Search for Biobjective TSP." In Hybrid Evolutionary Algorithms, 361–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73297-6_14.

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Santana-Quintero, Luis V., Noel Ramírez, and Carlos Coello Coello. "A Multi-objective Particle Swarm Optimizer Hybridized with Scatter Search." In Lecture Notes in Computer Science, 294–304. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11925231_28.

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Amarjeet Singh, Kusum Deep, and Aakash Deep. "Curve Fitting Using Gravitational Search Algorithm and Its Hybridized Variants." In Advances in Intelligent Systems and Computing, 823–37. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0451-3_74.

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Choi, Zhi Chuan, Koon Meng Ang, Wei Hong Lim, Sew Sun Tiang, Chun Kit Ang, Mahmud Iwan Solihin, Mohd Rizon Mohamed Juhari, and Cher En Chow. "Hybridized Metaheuristic Search Algorithm with Modified Initialization Scheme for Global Optimization." In Advances in Robotics, Automation and Data Analytics, 172–82. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70917-4_17.

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Alrajhi, Khaled, Jonathan Thompson, and Wasin Padungwech. "Tabu Search Hybridized with Multiple Neighborhood Structures for the Frequency Assignment Problem." In Hybrid Metaheuristics, 157–70. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39636-1_12.

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Rey, Emmanuel, Martine Laprise, and Sophie Lufkin. "An Operational Monitoring Tool." In Neighbourhoods in Transition, 143–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82208-8_9.

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AbstractThe transition from an urban brownfield to a sustainable neighbourhood is a complex operation. To help decision-makers reach sustainability objectives through measurement, follow-up, and communication about performance indicators, we introduce in this chapter a tailor-made operational monitoring tool. Such a tool should satisfy three general requirements: a search for overall quality, adequacy with the specificities of urban brownfield regeneration projects, and integration into the project dynamics. Accordingly, the multi-criteria evaluation system SIPRIUS and the quality management monitoring software OKpilot are hybridized to create SIPRIUS+. In the first section, we explain the functioning of the two existing methodologies and the adaptions we made to help meet the general requirements and to create the hybrid tool. Then, we present the resulting monitoring tool, SIPRIUS+, and its functionalities.
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Singh, Amarjeet, and Kusum Deep. "Hybridized Gravitational Search Algorithms with Real Coded Genetic Algorithms for Integer and Mixed Integer Optimization Problems." In Advances in Intelligent Systems and Computing, 84–112. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3322-3_9.

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Bagyamathi, M., and H. Hannah Inbarani. "A Novel Hybridized Rough Set and Improved Harmony Search Based Feature Selection for Protein Sequence Classification." In Studies in Big Data, 173–204. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11056-1_6.

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González, Miguel A., María Sierra, Camino R. Vela, and Ramiro Varela. "Genetic Algorithms Hybridized with Greedy Algorithms and Local Search over the Spaces of Active and Semi-active Schedules." In Current Topics in Artificial Intelligence, 231–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881216_25.

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Conference papers on the topic "HYBRIDIZED SEARCH"

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Strumberger, Ivana, Eva Tuba, Nebojsa Bacanin, Marko Beko, and Milan Tuba. "Hybridized moth search algorithm for constrained optimization problems." In 2018 International Young Engineers Forum (YEF-ECE). IEEE, 2018. http://dx.doi.org/10.1109/yef-ece.2018.8368930.

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Strumberger, Ivana, Eva Tuba, Nebojsa Bacanin, Marko Beko, and Milan Tuba. "Wireless Sensor Network Localization Problem by Hybridized Moth Search Algorithm." In 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC). IEEE, 2018. http://dx.doi.org/10.1109/iwcmc.2018.8450491.

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Mingping Xia. "An ant colony algorithm Hybridized with Iterated Local Search for the QAP." In 2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA 2009). IEEE, 2009. http://dx.doi.org/10.1109/paciia.2009.5406542.

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George, Golda, and Latha Parthiban. "Multi objective hybridized firefly algorithm with group search optimization for data clustering." In 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE, 2015. http://dx.doi.org/10.1109/icrcicn.2015.7434222.

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Baudiš, Petr, and Petr Pošík. "Global Line Search Algorithm Hybridized with Quadratic Interpolation and Its Extension to Separable Functions." In GECCO '15: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2739480.2754717.

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Galvão, Viviane J., Helio J. C. Barbosa, and Heder S. Bernardino. "Particle Swarm Optimization and Differential Evolution Methods Hybridized with Pattern Search for Solving Optimization Problems." In Congresso Brasileiro de Inteligência Computacional. ABRICOM, 2018. http://dx.doi.org/10.21528/cbic2017-121.

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Zainuddin, Zarita, Kee Huong Lai, and Pauline Ong. "Wavelet neural networks initialization using hybridized clustering and harmony search algorithm: Application in epileptic seizure detection." In PROCEEDINGS OF THE 20TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES: Research in Mathematical Sciences: A Catalyst for Creativity and Innovation. AIP, 2013. http://dx.doi.org/10.1063/1.4801131.

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Kanagaraj, G., S. G. Ponnambalam, and W. C. E. Lim. "Application of a hybridized cuckoo search-genetic algorithm to path optimization for PCB holes drilling process." In 2014 IEEE International Conference on Automation Science and Engineering (CASE). IEEE, 2014. http://dx.doi.org/10.1109/coase.2014.6899353.

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Hanafy, Mohmmad M. A., and Sayed M. Metwalli. "Hybrid General Heuristic Gradient Projection for Frame Optimization of Micro and Macro Applications." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87012.

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In this paper, a generalization is suggested for the Heuristic Gradient Projection method. The previous Heuristic Gradient Projection method (HGP) has been developed for 3D-frame design and optimization. It mainly employed bending stress relations in order to simplify the process of iterations for stress constrained optimization. The General Heuristic Gradient Projection (GHGP) is used in a more general form to satisfy the stress constraints. Another direct search method is hybridized to satisfy other constraints on deflection. Two examples are solved using the new method. The proposed method is compared with the Hybrid Fuzzy Heuristic technique (FHGP) when solving a MEMS resonator. Results showed that the proposed hybrid technique with (GHGP) converges to the optimum solutions faster by an 8%. The MEMS weight is also decreased by 23.7%. For a macro level, the GHGP improved the solution time by 33.3%. The hybrid technique with (GHGP) improved the stresses in the members of the optimum ten-member cantilever.
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Alpak, Faruk, Yixuan Wang, Guohua Gao, and Vivek Jain. "Benchmarking and Field-Testing of the Distributed Quasi-Newton Derivative-Free Optimization Method for Field Development Optimization." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206267-ms.

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Abstract Recently, a novel distributed quasi-Newton (DQN) derivative-free optimization (DFO) method was developed for generic reservoir performance optimization problems including well-location optimization (WLO) and well-control optimization (WCO). DQN is designed to effectively locate multiple local optima of highly nonlinear optimization problems. However, its performance has neither been validated by realistic applications nor compared to other DFO methods. We have integrated DQN into a versatile field-development optimization platform designed specifically for iterative workflows enabled through distributed-parallel flow simulations. DQN is benchmarked against alternative DFO techniques, namely, the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method hybridized with Direct Pattern Search (BFGS-DPS), Mesh Adaptive Direct Search (MADS), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). DQN is a multi-thread optimization method that distributes an ensemble of optimization tasks among multiple high-performance-computing nodes. Thus, it can locate multiple optima of the objective function in parallel within a single run. Simulation results computed from one DQN optimization thread are shared with others by updating a unified set of training data points composed of responses (implicit variables) of all successful simulation jobs. The sensitivity matrix at the current best solution of each optimization thread is approximated by a linear-interpolation technique using all or a subset of training-data points. The gradient of the objective function is analytically computed using the estimated sensitivities of implicit variables with respect to explicit variables. The Hessian matrix is then updated using the quasi-Newton method. A new search point for each thread is solved from a trust-region subproblem for the next iteration. In contrast, other DFO methods rely on a single-thread optimization paradigm that can only locate a single optimum. To locate multiple optima, one must repeat the same optimization process multiple times starting from different initial guesses for such methods. Moreover, simulation results generated from a single-thread optimization task cannot be shared with other tasks. Benchmarking results are presented for synthetic yet challenging WLO and WCO problems. Finally, DQN method is field-tested on two realistic applications. DQN identifies the global optimum with the least number of simulations and the shortest run time on a synthetic problem with known solution. On other benchmarking problems without a known solution, DQN identified compatible local optima with reasonably smaller numbers of simulations compared to alternative techniques. Field-testing results reinforce the auspicious computational attributes of DQN. Overall, the results indicate that DQN is a novel and effective parallel algorithm for field-scale development optimization problems.
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