Artykuły w czasopismach: „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, nr 2 (1.08.2020): 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 i Kanak Kalita. "Hybridized Particle Swarm—Gravitational Search Algorithm for Process Optimization". Processes 10, nr 3 (21.03.2022): 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 i R. Naresh. "Hybridized Gravitational Search Algorithm for Short-Term Hydrothermal Scheduling". IETE Journal of Research 62, nr 4 (25.09.2015): 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, nr 11 (30.11.2017): 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, 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 i J. Périaux. "Multi-level Hybridized Optimization Methods Coupling Local Search Deterministic and Global Search Evolutionary Algorithms". Archives of Computational Methods in Engineering 27, nr 3 (27.03.2019): 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 i M. W. Farthing. "Optimization of a Basin Network Using Hybridized Global Search Algorithms". Journal of Irrigation and Drainage Engineering 144, nr 8 (sierpień 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 i 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 i 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 i Qiang Luo. "Hybridizing Beam Search with Tabu Search for the Irregular Packing Problem". Mathematical Problems in Engineering 2021 (21.01.2021): 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, i Gang Li. "Mean Shift Segmentation Algorithm Based on Hybridized Bacterial Chemotaxis". Advanced Materials Research 468-471 (luty 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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Kang, Fei, Junjie Li i Haojin Li. "Artificial bee colony algorithm and pattern search hybridized for global optimization". Applied Soft Computing 13, nr 4 (kwiecień 2013): 1781–91. http://dx.doi.org/10.1016/j.asoc.2012.12.025.

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Lenin, K. "REDUCTION OF ACTIVE POWER LOSS BY IMPROVED TABU SEARCH ALGORITHM". International Journal of Research -GRANTHAALAYAH 6, nr 7 (31.07.2018): 1–9. http://dx.doi.org/10.29121/granthaalayah.v6.i7.2018.1277.

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In this paper, an Improved Tabu Search (ITS) algorithm has been proposed to solve the optimal reactive power problem. In this work Tabu Search- has been hybridized with Simulated Annealing algorithm to solve the optimal reactive power problem. Hybridization of these two algorithms improves the exploration & exploitation capabilities during the search. Proposed Improved Tabu Search (ITS) algorithm has been tested in Standard IEEE 57,118 bus systems & real power loss has been comparatively reduced with voltage profiles are within the limits.

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Chopra, Namarta, Y. S. Brar i J. S. Dhillon. "Hybridized Particle Swarm Optimization on Constrained Economic Dispatch Problem". Journal of Computational and Theoretical Nanoscience 17, nr 1 (1.01.2020): 322–28. http://dx.doi.org/10.1166/jctn.2020.8669.

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The hybridization of particle swarm optimization (PSO) with simplex search method (SSM) is presented on the problem of economic dispatch in the thermal plants so as to minimizes the overall operating fuel cost while subjected to various constraints. This hybridization of stochastic with deterministic optimization method helps the global optimum solution to further refine by the local search. It also overcome some of the drawbacks of conventional PSO like premature convergence and stagnation in the solution if the number of iterations are increased. This proposed optimization method is used to get the overall minimum cost of fuel by including transmission line losses and valve point loading effect (VPLE) in the classical problem of economic dispatch, so as to have the more practical impact in the case considered. The validness of the suggested algorithm is tested using small scale and large scale system and the analogy of results obtained are done with existing algorithms cited in the literature, showing improvement of 29.3% in small scale system and 6.4% in large scale system, which proves the robustness of the suggested approach.

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Zade, Anita, Narendra Patel i Nitin Padhiyar. "Effective Constrained Handling by Hybridized Cuckoo Search Algorithm with Box Complex Method". IFAC-PapersOnLine 50, nr 2 (grudzień 2017): 209–14. http://dx.doi.org/10.1016/j.ifacol.2017.12.038.

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Voß, Stefan, i Andreas Fink. "A hybridized tabu search approach for the minimum weight vertex cover problem". Journal of Heuristics 18, nr 6 (19.09.2012): 869–76. http://dx.doi.org/10.1007/s10732-012-9211-9.

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SATO, Hayaho, i Hajime IGARASHI. "Unified Design Using Monte Carlo Tree Search and Hybridized Topology-Parameter Optimization". Journal of the Japan Society of Applied Electromagnetics and Mechanics 30, nr 4 (2022): 384–89. http://dx.doi.org/10.14243/jsaem.30.384.

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Raja, G., i U. Srinivasulu Reddy. "DNA Fragment Assembly Using Hybridized Catfish PSO". International Journal of Software Innovation 9, nr 2 (kwiecień 2021): 69–80. http://dx.doi.org/10.4018/ijsi.2021040104.

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DNA fragment assembly aids in uncovering several aspects of the human DNA, and hence in-turn enables scientists in understanding and curing several hereditary problems. Several computational methods have been proposed to solve this problem. However, the huge size and the NP-hard nature of the problem poses several challenges in proposing a time effective system for fragment assembly. This paper proposes a hybridized catfish PSO model for the process of fragment assembly. PSO algorithm is enhanced by incorporating the catfish particles to enable the model to get out of the local optimal solutions. Further, the local search process has been hybridized to incorporate simulated annealing, such that the model performs faster selection of solutions. This has enabled the proposed model to provide effective results with low computational requirements. Experiments were performed with 10 benchmark instances from GenFrag. The results were compared with state-of-the-art models in literature, and it was identified that the proposed model exhibits high performance in comparatively shorter time.

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Elkhechafi, Mariam, Hanaa Hachimi i Youssfi Elkettani. "A new hybrid cuckoo search and firefly optimization". Monte Carlo Methods and Applications 24, nr 1 (1.03.2018): 71–77. http://dx.doi.org/10.1515/mcma-2018-0003.

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Abstract In this paper, we present a new hybrid algorithm which is a combination of a hybrid Cuckoo search algorithm and Firefly optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, Cuckoo Search (CS) and Firefly Algorithm (FA) for the global optimization. Denoted as CS-FA. The hybrid CS-FA technique incorporates concepts from CS and FA and creates individuals in a new generation not only by random walk as found in CS but also by mechanisms of FA. To analyze the benefits of hybridization, we have comparatively evaluated the classical Cuckoo Search and Firefly Algorithms versus the proposed hybridized algorithms (CS-FA).

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Navnath Dattatraya, Kale, K. Raghava Rao i D. Satish Kumar. "Architectural analysis for lifetime maximization and energy efficiency in hybridized WSN model". International Journal of Engineering & Technology 7, nr 2.7 (18.03.2018): 494. http://dx.doi.org/10.14419/ijet.v7i2.7.10870.

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It is well known that WSN is one of the leading techniques in granting pervasive computing for various applications regarding health sector and communication sector. However, the raising of issues in WSN is still a burden cause because of certain renowned terms like energy consumption and network lifetime extension. Clustering is a major contribution in any network and moreover Cluster Head selection is also a vital role since it is additively responsible in sending data to the base station, which means that Cluster Head directly makes its communication with base station. Day by day, the researches in cluster head selection get increased, but the requirements are not yet fulfilled. This paper proposes a energy efficient cluster head selection algorithm for maximizing the WSN lifetime. This paper develops a hybrid optimization process termed Group Search Ant Lion with Levy Flight (GAL-LF) for selecting the Cluster head in WSN. The proposed model is compared to the conventional models such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Group Search Optimization (GSO), Ant Lion Optimization (ALO) and Cuckoo Search (CS). The outcome of the simulation result shows the superiority of the proposed model by prolonging the lifetime of the network.

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Callens, Johan. "Anne-Marie Boisvert, Manon Oligny, and Thomas Israël: Three Artists in Search of Cindy Sherman". TDR/The Drama Review 54, nr 1 (marzec 2010): 39–58. http://dx.doi.org/10.1162/dram.2010.54.1.39.

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One dancer, one choreographer, one filmmaker. Together they recursively confront Sherman's by now famous, gender-inflected, photographic self-enactments, probing the conventions and lingering specificity, if any, of the media and art forms in our hybridized digital culture. In their intermedial performances, these media and art forms become the means and the objects of analysis.

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Deepak, Gerard, i Ishdutt Trivedi. "A Hybridized Deep Learning Strategy for Course Recommendation". International Journal of Adult Education and Technology 14, nr 1 (21.04.2023): 1–16. http://dx.doi.org/10.4018/ijaet.321752.

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Recommender systems have been actively used in many areas like e-commerce, movie and video suggestions, and have proven to be highly useful for its users. But the use of recommender systems in online learning platforms is often underrated and less likely used. But many of the times it lacks personalisation especially in collaborative approach while content-based doesn't work well for new users. Therefore, the authors propose a hybrid course recommender system for this problem which takes content as well as collaborative approaches and tackles their individual limitations. The authors see recommendation as a sequential problem and thus have used RNNs for solving the problem. Each recommendation can be seen as the new course in the sequence. The results suggest it outperforms other recommender systems when using LSTMs instead of RNNs. The authors develop and test the recommendation system using the Kaggle dataset of users for grouping similar users as historical data and search history of different users' data.

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Sulaiman, Muhammad, Ashfaq Ahmad, Asfandyar Khan i Shakoor Muhammad. "Hybridized Symbiotic Organism Search Algorithm for the Optimal Operation of Directional Overcurrent Relays". Complexity 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/4605769.

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This paper presents the solution of directional overcurrent relay (DOCR) problems using Simulated Annealing based Symbiotic Organism Search (SASOS). The objective function of the problem is to minimize the sum of the operating times of all primary relays. The DOCR problem is nonlinear and highly constrained with two types of decision variables, namely, the time dial settings (TDS) and plug setting (PS). In this paper, three models of the problem are considered, the IEEE 3-bus, 4-bus, and 6-bus, respectively. We have applied SASOS to solve the problem and the obtained results are compared with other algorithms available in the literature.

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Singh, Santosh Kumar, Deepika Kumari, Nilotpal Sinha, Arup Kumar Goswami i Nidul Sinha. "Gravity Search Algorithm hybridized Recursive Least Square method for power system harmonic estimation". Engineering Science and Technology, an International Journal 20, nr 3 (czerwiec 2017): 874–84. http://dx.doi.org/10.1016/j.jestch.2017.01.006.

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Labadie, Nacima, Jan Melechovský i Roberto Wolfler Calvo. "Hybridized evolutionary local search algorithm for the team orienteering problem with time windows". Journal of Heuristics 17, nr 6 (22.12.2010): 729–53. http://dx.doi.org/10.1007/s10732-010-9153-z.

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Sood, Monica, Dr Sahil Verma, Vinod Kumar Panchal i Dr Kavita. "Optimal Path Planning using Hybrid Bat Algorithm and Cuckoo Search". International Journal of Engineering & Technology 7, nr 4.12 (4.10.2018): 30. http://dx.doi.org/10.14419/ijet.v7i4.12.20987.

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Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.

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Bojji, Chakib, Othmane EL YAAKOUBI i MERYAM BENABDOUALLAH. "Genetic algorithm hybridized by a guided local search to solve the emergency coverage problem". International Journal of Mathematical Modelling and Numerical Optimisation 8, nr 1 (2017): 1. http://dx.doi.org/10.1504/ijmmno.2017.10003752.

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Li, X.-Y., P. Tian i S. C. H. Leung. "An ant colony optimization metaheuristic hybridized with tabu search for open vehicle routing problems". Journal of the Operational Research Society 60, nr 7 (lipiec 2009): 1012–25. http://dx.doi.org/10.1057/palgrave.jors.2602644.

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Fan, Shu-Kai S., Yun-Chia Liang i Erwie Zahara. "A genetic algorithm and a particle swarm optimizer hybridized with Nelder–Mead simplex search". Computers & Industrial Engineering 50, nr 4 (sierpień 2006): 401–25. http://dx.doi.org/10.1016/j.cie.2005.01.022.

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Lim, Wee Loon, Antoni Wibowo, Mohammad Ishak Desa i Habibollah Haron. "A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem". Computational Intelligence and Neuroscience 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/5803893.

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The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them.

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Lenin, Kanagasabai. "Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm for Solving Optimal Reactive Power Dispatch Problem". Journal of Applied Science, Engineering, Technology, and Education 3, nr 1 (20.06.2020): 12–21. http://dx.doi.org/10.35877/454ri.asci31106.

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In this work Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm (HGPSOS) has been done for solving the power dispatch problem. Genetic particle swarm optimization problem has been hybridized with Symbiotic organisms search (SOS) algorithm to solve the problem. Genetic particle swarm optimization algorithm is formed by combining the Particle swarm optimization algorithm (PSO) with genetic algorithm (GA). Symbiotic organisms search algorithm is based on the actions between two different organisms in the ecosystem- mutualism, commensalism and parasitism. Exploration process has been instigated capriciously and every organism specifies a solution with fitness value. Projected HGPSOS algorithm improves the quality of the search. Proposed HGPSOS algorithm is tested in IEEE 30, bus test system- power loss minimization, voltage deviation minimization and voltage stability enhancement has been attained.

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Zaman, Fawad. "Joint Angle-Amplitude Estimation for Multiple Signals with L-Structured Arrays Using Bioinspired Computing". Wireless Communications and Mobile Computing 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/9428196.

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The aim of this work is to estimate jointly the elevation and azimuth angles along with the amplitudes of multiple signals impinging on 1-L- and 2-L-shape arrays. An efficient mechanism based on hybrid Bioinspired techniques is proposed for this purpose. The global search optimizers such as Differential Evolution (DE) and Particle Swarm optimization (PSO) are hybridized with a local search optimizer called pattern search (PS). Approximation theory in Mean Square Error sense is exploited to develop a fitness function of the problem. The unknown parameters of multiple signals transmitted by far-field sources are estimated with the strength of hybrid DE-PS and PSO-PS. The effectiveness of the proposed techniques is tested in terms of estimation accuracy, proximity effect, convergence, and computational complexity.

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Sutradhar, Suman, Nalin B. Dev Choudhury i Nidul Sinha. "Modelling of Hydrothermal Unit Commitment Coordination Using Efficient Metaheuristic Algorithm: A Hybridized Approach". Journal of Optimization 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/4529836.

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In this paper, a novel approach of hybridization of two efficient metaheuristic algorithms is proposed for energy system analysis and modelling based on a hydro and thermal based power system in both single and multiobjective environment. The scheduling of hydro and thermal power is modelled descriptively including the handling method of various practical nonlinear constraints. The main goal for the proposed modelling is to minimize the total production cost (which is highly nonlinear and nonconvex problem) and emission while satisfying involved hydro and thermal unit commitment limitations. The cascaded hydro reservoirs of hydro subsystem and intertemporal constraints regarding thermal units along with nonlinear nonconvex, mixed-integer mixed-binary objective function make the search space highly complex. To solve such a complicated system, a hybridization of Gray Wolf Optimization and Artificial Bee Colony algorithm, that is, h-ABC/GWO, is used for better exploration and exploitation in the multidimensional search space. Two different test systems are used for modelling and analysis. Experimental results demonstrate the superior performance of the proposed algorithm as compared to other recently reported ones in terms of convergence and better quality of solutions.

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Gumireddy, Ashwini, Kevin DeBoyace, Alexander Rupprecht, Mohit Gupta, Saloni Patel, Patrick T. Flaherty i Peter L. D. Wildfong. "Crystal structure of tert-butyl 4-[4-(4-fluorophenyl)-2-methylbut-3-yn-2-yl]piperazine-1-carboxylate". Acta Crystallographica Section E Crystallographic Communications 77, nr 4 (5.03.2021): 360–65. http://dx.doi.org/10.1107/s2056989021002346.

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The title sterically congested piperazine derivative, C20H27FN2O2, was prepared using a modified Bruylants approach. A search of the Cambridge Structural Database identified 51 compounds possessing an N-tert-butyl piperazine substructure. Of these only 14 were asymmetrically substituted on the piperazine ring and none with a synthetically useful second nitrogen. Given the novel chemistry generating a pharmacologically useful core, determination of the crystal structure for this compound was necessary. The piperazine ring is present in a chair conformation with di-equatorial substitution. Of the two N atoms, one is sp 3 hybridized while the other is sp 2 hybridized. Intermolecular interactions resulting from the crystal packing patterns were investigated using Hirshfeld surface analysis and fingerprint analysis. Directional weak hydrogen-bond-like interactions (C—H...O) and C—H...π interactions with the dispersion interactions as the major source of attraction are present in the crystal packing.

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Raja, Muhammad Asif Zahoor, Junaid Ali Khan, Siraj-ul-Islam Ahmad i Ijaz Mansoor Qureshi. "A New Stochastic Technique for Painlevé Equation-I Using Neural Network Optimized with Swarm Intelligence". Computational Intelligence and Neuroscience 2012 (2012): 1–10. http://dx.doi.org/10.1155/2012/721867.

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A methodology for solution of Painlevé equation-I is presented using computational intelligence technique based on neural networks and particle swarm optimization hybridized with active set algorithm. The mathematical model of the equation is developed with the help of linear combination of feed-forward artificial neural networks that define the unsupervised error of the model. This error is minimized subject to the availability of appropriate weights of the networks. The learning of the weights is carried out using particle swarm optimization algorithm used as a tool for viable global search method, hybridized with active set algorithm for rapid local convergence. The accuracy, convergence rate, and computational complexity of the scheme are analyzed based on large number of independents runs and their comprehensive statistical analysis. The comparative studies of the results obtained are made with MATHEMATICA solutions, as well as, with variational iteration method and homotopy perturbation method.

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Agrawal, Anubha, Nitish Ghune, Shiv Prakash i Manojkumar Ramteke. "Evolutionary algorithm hybridized with local search and intelligent seeding for solving multi-objective Euclidian TSP". Expert Systems with Applications 181 (listopad 2021): 115192. http://dx.doi.org/10.1016/j.eswa.2021.115192.

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Gambardella, Luca Maria, i Marco Dorigo. "An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem". INFORMS Journal on Computing 12, nr 3 (sierpień 2000): 237–55. http://dx.doi.org/10.1287/ijoc.12.3.237.12636.

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Kanso, Bilal, Ali Kansou i Adnan Yassine. "Open Capacitated ARC routing problem by Hybridized Ant Colony Algorithm". RAIRO - Operations Research 55, nr 2 (marzec 2021): 639–52. http://dx.doi.org/10.1051/ro/2021034.

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The Open Capacitated Arc Routing Problem OCARP is a well-known NP-hard real-world combinatorial optimization problem. It consists of determining optimal routes for vehicles in a given service area at a minimal cost distance. The main real application for OCARP is the Meter Reader Routing Problem (MRRP). In MRRP problem, each worker in the electric (or gas) company must visit and read the electric (or gas) meters to a set of customers by starting his route from the first customer on his visit list and finishing with the last one. The worker leaves where he wants once all the associated customers have been visited. In this paper, a metaheuristic called an Hybridized Ant Colony Algorithm (HACA) is developed and hybridized with a local search algorithm that involves the 2-opt, Swap, Relocate and Cross-exchange moves to solve OCARP problem. Computational results conducted on five different sets of OCARP-instances showed that our proposed algorithm HACA has reached good and competitive results on benchmark instances for the problem.

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Song, Junseong, Hyun Yong Song, Zhen Wang, Seokhee Lee, Jae-Yeol Hwang, Seung Youb Lee, Jouhahn Lee i in. "Creation of two-dimensional layered Zintl phase by dimensional manipulation of crystal structure". Science Advances 5, nr 6 (czerwiec 2019): eaax0390. http://dx.doi.org/10.1126/sciadv.aax0390.

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The discovery of new families, beyond graphene, of two-dimensional (2D) layered materials has always attracted great attention. However, it has been challenging to artificially develop layered materials with honeycomb atomic lattice structure composed of multicomponents such as hexagonal boron nitride. Here, through the dimensional manipulation of a crystal structure from sp3-hybridized 3D-ZnSb, we create an unprecedented layered structure of Zintl phase, which is constructed by the staking of sp2-hybridized honeycomb ZnSb layers. Using structural analysis combined with theoretical calculation, it is found that the 2D-ZnSb has a stable and robust layered structure. The bidimensional polymorphism is a previously unobserved phenomenon at ambient pressure in Zintl families and can be a common feature of transition metal pnictides. This dimensional manipulation of a crystal structure thus provides a rational design strategy to search for new 2D layered materials in various compounds, enabling unlimited expansion of 2D libraries and corresponding physical properties.

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Lim, Hui Sheng, Shuangshuang Fan, Christopher K. H. Chin, Shuhong Chai i Neil Bose. "Particle swarm optimization algorithms with selective differential evolution for AUV path planning". IAES International Journal of Robotics and Automation (IJRA) 9, nr 2 (1.06.2020): 94. http://dx.doi.org/10.11591/ijra.v9i2.pp94-112.

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Particle swarm optimization (PSO)-based algorithms are suitable for path planning of the Autonomous Underwater Vehicle (AUV) due to their high computational efficiency. However, such algorithms may produce sub-optimal paths or require higher computational load to produce an optimal path. This paper proposed a new approach that improves the ability of PSO-based algorithms to search for the optimal path while maintaining a low computational requirement. By hybridizing with differential evolution (DE), the proposed algorithms carry out the DE operator selectively to improve the search ability. The algorithms were applied in an offline AUV path planner to generate a near-optimal path that safely guides the AUV through an environment with a priori known obstacles and time-invariant non-uniform currents. The algorithm performances were benchmarked against other algorithms in an offline path planner because if the proposed algorithms can provide better computational efficiency to demonstrate the minimum capability of a path planner, then they will outperform the tested algorithms in a realistic scenario. Through Monte Carlo simulations and Kruskal-Wallis test, SDEAPSO (selective DE-hybridized PSO with adaptive factor) and SDEQPSO (selective DE-hybridized Quantum-behaved PSO) were found to be capable of generating feasible AUV path with higher efficiency than other algorithms tested, as indicated by their lower computational requirement and excellent path quality.

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Lenin, K. "BACK PROPAGATION NEURAL NETWORK TECHNIQUE FOR REDUCTION OF REAL POWER LOSS". International Journal of Research -GRANTHAALAYAH 6, nr 12 (31.12.2018): 140–46. http://dx.doi.org/10.29121/granthaalayah.v6.i12.2018.1100.

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In this work particle swarm optimization algorithm has been hybridized with Back propagation neural network (PSBP) to solve the reactive power problem. Proposed PSBP methodology improves search. PSO algorithm to optimize the original weight, threshold value and when the algorithm ends, optimal point can be found- on the base of PSO algorithm; Back propagation neural network algorithm to search overall situation and then achieve the network training goal. In the particle swarm, every particle’s position represents weights set among the network during the resent iteration. In order to evaluate the proposed algorithm, it has been tested on IEEE 118 bus system and compared to other algorithms and simulation results show that proposed algorithm reduces the real power loss effectively.

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Ahmed, Ijaz, Ali Rauf Rao, Alaf Shah, Engr Alamzeb i Junaid Ali Khan. "Performance of Various Metaheuristic Techniques for Economic Dispatch Problem with Valve Point Loading Effects and Multiple Fueling Options". Advances in Electrical Engineering 2014 (27.11.2014): 1–14. http://dx.doi.org/10.1155/2014/765053.

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The paper presents the metaheuristic approaches based on pattern search and simulated annealing hybridized with sequential quadratic programming, a powerful nonlinear adaptive technique for estimation of the finest combination of generated power in a given system at lowest operating cost while sustaining the operating condition of system efficiently. The fuel cost is minimized by satisfying the nonlinear operating conditions of thermal units mainly based on generation capacity constraints, generator ramp limit, power balance constraints, and valve point loading effect and by keeping in view the prohibited operating zones, respectively. About the optimization, a comparative study is made for pattern search (PS) and simulated annealing (SA), as a viable global search technique and sequential quadratic programming, an efficient local optimizer and their hybrid versions. The applicability, stability, and reliability of the designed approaches are validated through comprehensive statistical analysis based on Monte Carlo simulations.

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Torres, Giovana A., Sidney N. Parentoni, Maurício A. Lopes i Edilson Paiva. "A search for RFLP markers to identify genes for aluminum tolerance in maize". Brazilian Journal of Genetics 20, nr 3 (wrzesień 1997): 459–65. http://dx.doi.org/10.1590/s0100-84551997000300017.

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The objective of this study was to identify restriction fragment length polymorphism (RFLP) markers linked to QTLs that control aluminum (Al) tolerance in maize. The strategy used was bulked segregant analysis (BSA) and the genetic material utilized was an F2 population derived from a cross between the Al-susceptible inbred line L53 and Al-tolerant inbred line L1327. Both lines were developed at the National Maize and Sorghum Research Center - CNPMS/EMBRAPA. The F2 population of 1554 individuals was evaluated in a nutrient solution containing a toxic concentration of Al and relative seminal root length (RSRL) was used as a phenotypic measure of tolerance. The RSRL frequency distribution was continuous, but skewed towards Al-susceptible individuals. Seedlings of the F2 population which scored the highest and the lowest RSRL values were transplanted to the field and subsequently selfed to obtain F3 families. Thirty F3 families (15 Al-susceptible and 15 Al-tolerant) were evaluated in nutrient solution, using an incomplete block design, to identify those with the smallest variances for aluminum tolerance and susceptibility. Six Al-susceptible and five Al-tolerant F3 families were chosen to construct one pool of Al-susceptible individuals, and another of Al-tolerant, herein referred as "bulks", based on average values of RSRL and genetic variance. One hundred and thirteen probes were selected, with an average interval of 30 cM, covering the 10 maize chromosomes. These were tested for their ability to discriminate the parental lines. Fifty-four of these probes were polymorphic, with 46 showing codominance. These probes were hybridized with DNA from the two contrasting bulks. Three RFLPs on chromosome 8 distinguished the bulks on the basis of band intensity. DNA of individuals from the bulks was hybridized with these probes and showed the presence of heterozygous individuals in each bulk. These results suggest that in maize there is a region related to aluminum tolerance on chromosome 8

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Ficarella, Elisa, Luciano Lamberti i Sadik Ozgur Degertekin. "Mechanical Identification of Materials and Structures with Optical Methods and Metaheuristic Optimization". Materials 12, nr 13 (2.07.2019): 2133. http://dx.doi.org/10.3390/ma12132133.

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This study presents a hybrid framework for mechanical identification of materials and structures. The inverse problem is solved by combining experimental measurements performed by optical methods and non-linear optimization using metaheuristic algorithms. In particular, we develop three advanced formulations of Simulated Annealing (SA), Harmony Search (HS) and Big Bang-Big Crunch (BBBC) including enhanced approximate line search and computationally cheap gradient evaluation strategies. The rationale behind the new algorithms—denoted as Hybrid Fast Simulated Annealing (HFSA), Hybrid Fast Harmony Search (HFHS) and Hybrid Fast Big Bang-Big Crunch (HFBBBC)—is to generate high quality trial designs lying on a properly selected set of descent directions. Besides hybridizing SA/HS/BBBC metaheuristic search engines with gradient information and approximate line search, HS and BBBC are also hybridized with an enhanced 1-D probabilistic search derived from SA. The results obtained in three inverse problems regarding composite and transversely isotropic hyperelastic materials/structures with up to 17 unknown properties clearly demonstrate the validity of the proposed approach, which allows to significantly reduce the number of structural analyses with respect to previous SA/HS/BBBC formulations and improves robustness of metaheuristic search engines.

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Alzaqebah, Malek, Sana Jawarneh, Rami Mustafa A. Mohammad, Mutasem K. Alsmadi, Ibrahim Al-marashdeh, Eman A. E. Ahmed, Nashat Alrefai i Fahad A. Alghamdi. "Hybrid feature selection method based on particle swarm optimization and adaptive local search method". International Journal of Electrical and Computer Engineering (IJECE) 11, nr 3 (1.06.2021): 2414. http://dx.doi.org/10.11591/ijece.v11i3.pp2414-2422.

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Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance.

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Vallente-Samonte, Rhea U., Robert A. Conte i Ram S. Verma. "Syntenic homology of human unique DNA sequences within chromossome regions 5q31, 10q22, 13q32-33 and 19q13.1 in the great apes". Genetics and Molecular Biology 23, nr 3 (wrzesień 2000): 527–29. http://dx.doi.org/10.1590/s1415-47572000000300004.

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Homologies between chromosome banding patterns and DNA sequences in the great apes and humans suggest an apparent common origin for these two lineages. The availability of DNA probes for specific regions of human chromosomes (5q31, 10q22, 13q32-33 and 19q13.1) led us to cross-hybridize these to chimpanzee (Pan troglodytes, PTR), gorilla (Gorilla gorilla, GGO) and orangutan (Pongo pygmaeus, PPY) chromosomes in a search for equivalent regions in the great apes. Positive hybridization signals to the chromosome 5q31-specific DNA probe were observed at HSA 5q31, PTR 4q31, GGO 4q31 and PPY 4q31, while fluorescent signals using the chromosome 10q22-specific DNA probe were noted at HSA 10q22, PTR 8q22, GGO 8q22 and PPY 7q22. The chromosome arms showing hybridization signals to the Quint-EssentialTM 13-specific DNA probe were identified as HSA 13q32-33, PTR 14q32-33, GGO 14q32-33 and PPY 14q32-33, while those presenting hybridization signals to the chromosome 19q13.1-specific DNA probe were identified as HSA 19q13.1, PTR 20q13, GGO 20q13 and PPY 20q13. All four probes presumably hybridized to homologous chromosomal locations in the apes, which suggests a homology of certain unique DNA sequences among hominoid species.

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Baghlani, A., i M. H. Makiabadi. "Weight optimization of truss structures by a new feasible boundary search technique hybridized with firefly algorithm". KSCE Journal of Civil Engineering 18, nr 4 (25.04.2014): 1105–18. http://dx.doi.org/10.1007/s12205-014-0438-x.

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Kaveh, A., i S. Talatahari. "Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures". Computers & Structures 87, nr 5-6 (marzec 2009): 267–83. http://dx.doi.org/10.1016/j.compstruc.2009.01.003.

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Baniamerian, Ali, Mahdi Bashiri i Fahime Zabihi. "A modified variable neighborhood search hybridized with genetic algorithm for vehicle routing problems with cross-docking". Electronic Notes in Discrete Mathematics 66 (kwiecień 2018): 143–50. http://dx.doi.org/10.1016/j.endm.2018.03.019.

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Fister, Iztok, Simon Fong, Janez Brest i Iztok Fister. "A Novel Hybrid Self-Adaptive Bat Algorithm". Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/709738.

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Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction.

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Pieau, Claude, Marc Girondot i Jean Servan. "Rapid search for genetic differentiation of populations: application to the European pond turtle". Amphibia-Reptilia 32, nr 3 (2011): 297–304. http://dx.doi.org/10.1163/017353711x571865.

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AbstractA strategy has been developed to screen very rapidly many individuals and loci in one step to screen for qualitative genetic difference among different populations. Genomic DNAs of individuals are digested with a restriction enzyme and then pooled according to the phenotypic character to genetically characterize. After electrophoresis on the same gel of the different DNA pools, the DNA fragments are hybridized with synthetic oligonucleotides of 4 base repeats. The hybridization patterns obtained with the different pools of DNA are compared and are used as references to analyze the individual profiles. This strategy was used to screen the genetic structure of several breeding groups of the European pond turtle (Emys orbicularis) from department of Indre (Brenne region), and Var, both in France at 4 geographic scales. The results are concordant with those obtained using morphological and mtDNA data.

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