Journal articles: 'Hill climbing'

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Pendergrass, L. "Climbing the commercialization hill." IEEE Instrumentation and Measurement Magazine 9, no. 1 (February 2006): 12–21. http://dx.doi.org/10.1109/mim.2006.1634952.

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Anam, Hairul, Feby Sabilhul Hanafi, Ahmad Fauzal Adifia, Ahmad Firdaus Ababil, and Saiful Bukhori. "Penerapan Metode Steepest Ascent Hill Climb pada Permainan Puzzle." INFORMAL: Informatics Journal 3, no. 2 (August 31, 2018): 36. http://dx.doi.org/10.19184/isj.v3i2.9987.

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Puzzle is one example of the application of artificial intelligence, in the process of completion there are many search algorithms that can be applied. The 8 puzzle solution will be faster obtained if the array principle is used with a variation of the Steepest-Ascent Hill Climbing (Hill Climbing algorithm by choosing the sharpest / steepest slope) with the correct heuristic parameters and distance heuristics and combined with LogList as the storage state ever passed to overcome the problems in the hill climbing algorithm itself and avoid the looping state that has been passed. Steepest Ascent Hill Climbing is an algorithm method that is widely used for optimization problems. The application of the SAHC (Steepest Ascent Hill Climbing) Algorithm to the puzzle is needed so that the game is completed with optimal time.

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MURAMATSU, Daigo. "Hill-Climbing Attacks and Robust Online Signature Verification Algorithm against Hill-Climbing Attacks." IEICE Transactions on Information and Systems E93-D, no. 3 (2010): 448–57. http://dx.doi.org/10.1587/transinf.e93.d.448.

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Abualigah, Laith Mohammad, Essam Said Hanandeh, Ahamad Tajudin Khader, Mohammed Abdallh Otair, and Shishir Kumar Shandilya. "An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 4 (May 7, 2020): 296–306. http://dx.doi.org/10.2174/1573405614666180903112541.

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Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

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Ilwaru, Venn Y. I., Tesa Sumah, Yopi Andry Lesnussa, and Zeth A. Leleury. "PERBANDINGAN ALGORITMA HILL CLIMBING DAN ALGORITMA ANT COLONY DALAM PENENTUAN RUTE OPTIMUM." BAREKENG: Jurnal Ilmu Matematika dan Terapan 11, no. 2 (December 1, 2017): 139–50. http://dx.doi.org/10.30598/barekengvol11iss2pp139-150.

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Optimasi adalah pencarian nilai-nilai variabel yang dianggap optimal untuk mencapai hasil yang diinginkan. Untuk memecahkan masalah optimasi tersebut, tentunya diperlukan algoritma yang handal. Algoritma Hill Climbing dan Algotrima Ant Colony adalah metode dari sekian banyak metode kecerdasan buatan untuk menyelesaikan permasalahan optimasi. Karena algoritmanya yang cukup sederhana, metode Hill Climbing telah banyak diterapkan dalam berbagai aplikasi. Disamping itu metode Hill Climbing juga mengefisienkan penggunaan memori yang besar. Algoritma Ant Colony adalah algoritma yang diadopsi dari perilaku koloni semut. Secara alamiah koloni semut mampu menemukan rute terpendek dalam perjalanan dari sarang ke tempat-tempat sumber makanan, berdasarkan jejak kaki pada lintasan yang telah dilewati. Dari hasil penelitian yang dilakukan dengan menggunakan algoritma Hill Climbing dan Algoritma Ant Colony diperoleh rute optimum ferry di Pulau Ambon, Pulau Seram, dan Pulau-Pulau Lease yang berbeda. Pada Algoritma Hill Climbing diperoleh rute yang optimal yaitu Tulehu – Wainama – Umeputih – Wailey – Amahai - Nalahia dengan jarak tempuh 126 Km, sedangkan menggunakan Algoritma Ant Colony diperoleh rute yang optimal yaitu Tulehu – Wainama – Umeputih – Wailey – Amahai – Nalahia - Tulehu dengan jarak tempuh 197 Km

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Vaughan, Diane E., Sheldon H. Jacobson, and Derek E. Armstrong. "A New Neighborhood Function for Discrete Manufacturing Process Design Optimization Using Generalized Hill Climbing Algorithms." Journal of Mechanical Design 122, no. 2 (March 1, 2000): 164–71. http://dx.doi.org/10.1115/1.533566.

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Discrete manufacturing process design optimization can be difficult, due to the large number of manufacturing process design sequences and associated input parameter setting combinations that exist. Generalized hill climbing algorithms have been introduced to address such manufacturing design problems. Initial results with generalized hill climbing algorithms required the manufacturing process design sequence to be fixed, with the generalized hill climbing algorithm used to identify optimal input parameter settings. This paper introduces a new neighborhood function that allows generalized hill climbing algorithms to be used to also identify the optimal discrete manufacturing process design sequence among a set of valid design sequences. The neighborhood function uses a switch function for all the input parameters, hence allows the generalized hill climbing algorithm to simultaneously optimize over both the design sequences and the inputs parameters. Computational results are reported with an integrated blade rotor discrete manufacturing process design problem under study at the Materials Process Design Branch of the Air Force Research Laboratory, Wright Patterson Air Force Base (Dayton, Ohio, USA). [S1050-0472(00)01002-3]

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Basseur, Matthieu, Adrien Goëffon, Frédéric Lardeux, Frédéric Saubion, and Vincent Vigneron. "On the Attainability of NK Landscapes Global Optima." Proceedings of the International Symposium on Combinatorial Search 5, no. 1 (September 1, 2021): 28–34. http://dx.doi.org/10.1609/socs.v5i1.18312.

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In this paper, we aim at evaluating the impact of the starting point of a basic local search based on the first improvement strategy. We define the coverage rate of a configuration as the proportion of the search space from which a particular configuration can be reached by a strict hill-climbling with a non-zero probability. In particular, we compute the coverage rate of fitness landscapes global optima, in order to evaluate their attainability by hill-climbing algorithms. The experimental study is realized on NK landscapes, in which the size and ruggedness can be controlled. Results indicate that the coverage rate of global optima is usually high, which means that a basic strictly improving hill-climbing with first improvement strategy is able to reach global optima, independently to the starting point considered. This confirms that it is more important to focus on an effective search strategy rather than worrying about the choice of the initial configurations.

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Senese, Donald J. "Climbing the Electronic Capitol Hill." Serials Review 28, no. 4 (December 2002): 287–94. http://dx.doi.org/10.1080/00987913.2002.10764761.

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Ervine, Alice. "Instructions for Climbing a Hill." Appalachian Heritage 17, no. 3 (1989): 38. http://dx.doi.org/10.1353/aph.1989.0136.

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Heap, Danny, Peter Danziger, and Eric Mendelsohn. "Hill-climbing to Pasch valleys." Journal of Combinatorial Designs 15, no. 5 (2007): 405–19. http://dx.doi.org/10.1002/jcd.20142.

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P, Klaudius Nikotino, Magdalena Ineke Pakereng, and Ramos Somya. "Perancangan dan Implementasi Penjadwalan Mata Kuliah Menggunakan Algoritma Steepest Ascent Hill Climbing (Studi Kasus : Fakultas Psikologi UKSW)." d'CARTESIAN 2, no. 1 (March 30, 2013): 11. http://dx.doi.org/10.35799/dc.2.1.2013.2121.

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Abstract Schedule of courses has been one of the most important part in a university’s teaching and learning activity. The large number of courses and lecturers that are involved, make some clashes on course’s schedule or class room’s schedule could happens frequently, so it requires an application to simplify the process of developing the course schedule. Steepest ascent hill climbing algorithm is an algorithm that generates all possible solutions, and then checks every solution to obtain optimum solution. The result of this research is this course scheduling system can facilitate courses schedulling, in addition steepest ascent hill climbing algorithm is able to obtain optimum scheduling courses solutions. Keywords : Course Schedulling, Steepest Ascent Hill Climbing Abstrak Jadwal mata kuliah telah menjadi salah satu bagian terpenting dalam proses belajar mengajar sebuah universitas. Banyaknya jumlah mata kuliah dan dosen yang terlibat, mengakibatkan sering terjadinya bentrokan jadwal mata kuliah atau pun dalam pemakaian ruang perkuliahan, sehingga diperlukan sebuah aplikasi guna mempermudah proses penyusunan jadwal mata kuliah tersebut. Algoritma steepest ascent hill climbing adalah algoritma yang mengumpulkan seluruh solusi yang mungkin, dan men gecek setiap solusi untuk memperoleh solusi yang optimal. Hasil dari penelitian ini adalah aplikasi yang dibangun dapat digunakan untuk memudahkan penjadwalan mata kuliah selain itu algoritma steepest ascent hill climbing mampu memperoleh solusi penjadwalan mata kuliah yang optimal. Kata Kunci : Penjadwalan Mata Kuliah, Steepest Ascent Hill Climbing.

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Panggabean, Tongam Evi, and Yuchia Natalia. "Optimasi Penjadwalan Mata Pelajaran Menggunakan Algoritma Steepest Ascent Hill Climbing." JURNAL ARMADA INFORMATIKA 2, no. 2 (December 18, 2018): 55–65. http://dx.doi.org/10.36520/jai.v2i2.38.

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Proses pembelajaran di sekolah yang efektif sangat ditentukan oleh pembuatan jadwal mata pelajaran. Oleh karena itu diperlukan penjadwalan yang optimal dengan pengaturan alokasi waktu dan ruang belajar secara akurat terhadap peserta didik, guru, mata pelajaran, dan fasilitas pembelajaran. Untuk menyelesaikan masalah penjadwalan mata pelajaran yang optimal diperlukan algoritma yang dapat menyelesaikan masalah multi-kriteria dan multi-objektif karena ada beberapa kendala yang harus diselesaikan dalam masalah ini seperti kendala pada peserta didik, guru, mata pelajaran dan fasilitas belajar mengajar. Salah satu algoritma yang dapat digunakan dalam menyelesaikan masalah ini adalah algoritma steepest ascent hill climbing. Algoritma steepest ascent hill climbing adalah salah satu algoritma yang sangat tepat digunakan dalam menyelesaikan masalah optimasi kompleks yang sulit dilakukan oleh metode konvensional. Algoritma steepest ascent hill climbing merupakan pengembangan dari algoritma simple hill climbing, urutan penggunaan operator tidak berpengaruh terhadap solusi. Pengujian sistem ini dilakukan dengan melakukan pengujian terhadap prioritas- prioritas yang ada. Berdasarkan nilai heuristik yang diperoleh dari setiap successor- successornya, maka membuktikan bahwa sistem ini mampu memenuhi setiap prioritas yang ada. Dengan dibuatnya sistem ini, diharapkan dapat mengoptimasi penjadwalan mata pelajaran pada SMA Methodist Binjai. Keyword: penjadwalan, mata pelajaran, algoritma steepest ascent hill climbing

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Luo, Dong Dong, Shou An Chen, and Yuan Ping Lin. "Research on Power Industry with Application of Improved Hill-Climbing Algorithm in Wind Power Generation System." Advanced Materials Research 1014 (July 2014): 211–15. http://dx.doi.org/10.4028/www.scientific.net/amr.1014.211.

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This article puts forward improved hill-climbing algorithm based on the problems that exist in wind power system compared with traditional hill-climbing searching MPPT control algorithm.In order to obtain optimum power curve,the improved algorithm finds the maximum power point when wind speed is steady firstly,then estimating torque loss accurately of motor to calculate system power loss indirectly when system is in the state of maximum power point.At last,the disturbance step is adjusted in real time based on distance between actual work point and optimum power curve.The improved hill-climbing algorithm does not exist the phenomenon that there is concussion near the maximum power point.What is more, the tracking direction could not be misled and tracking speed is sooner for tracking maximum power point when wind speed changes rapidly.By using Matlab for algorithm simulation,the result shows that improved hill-climbing algorithm could make up for the deficiency of traditional algorithm effectively.

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Wilt, Christopher, Jordan Thayer, and Wheeler Ruml. "A Comparison of Greedy Search Algorithms." Proceedings of the International Symposium on Combinatorial Search 1, no. 1 (August 25, 2010): 129–36. http://dx.doi.org/10.1609/socs.v1i1.18182.

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We discuss the relationships between three approaches to greedy heuristic search: best-first, hill-climbing, and beam search. We consider the design decisions within each family and point out their oft-overlooked similarities. We consider the following best-first searches: weighted A*, greedy search, ASeps, window A* and multi-state commitment k-weighted A*. For hill climbing algorithms, we consider enforced hill climbing and LSS-LRTA*. We also consider a variety of beam searches, including BULB and beam-stack search. We show how to best configure beam search in order to maximize robustness. An empirical analysis on six standard benchmarks reveals that beam search and best-first search have remarkably similar performance, and outperform hill-climbing approaches in terms of both time to solution and solution quality. Of these, beam search is preferable for very large problems and best first search is better on problems where the goal cannot be reached from all states.

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Yang, Ping, and Ping Sheng Liang. "An Improved MPPT Algorithm for Off-Grid Wind Power Generator Based on Hill Climbing Method." Advanced Materials Research 621 (December 2012): 334–39. http://dx.doi.org/10.4028/www.scientific.net/amr.621.334.

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The control algorithm of maximum power point tracking (MPPT) plays a key role in improving the wind energy conversion efficiency. This paper proposes an improved MPPT control algorithm based on hill climbing method, which combines proportional control with hill climbing method, with the advantage of precise steady-state control from hill climbing method, the combination with proportional control enhances the speed of dynamic response. By using this algorithm in the wind power simulation system based on Matlab, we get the simulation results for both fixed and variable wind speed. The result shows that this improved algorithm can track the maximum power point of wind power generation system quickly and precisely.

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Wieczorek, Bartosz, Łukasz Warguła, and Dominik Rybarczyk. "Impact of a Hybrid Assisted Wheelchair Propulsion System on Motion Kinematics during Climbing up a Slope." Applied Sciences 10, no. 3 (February 4, 2020): 1025. http://dx.doi.org/10.3390/app10031025.

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Overcoming terrain obstacles presents a major problem for people with disabilities or with limited mobility who are dependent on wheelchairs. An engineering solution designed to facilitate the use of wheelchairs are assisted-propulsion systems. The objective of the research described in this article is to analyze the impact of the hybrid manual–electric wheelchair propulsion system on the kinematics of the anthropotechnical system when climbing hills. The tests were carried out on a wheelchair ramp with an incline of 4°, using a prototype wheelchair with a hybrid manual–electric propulsion system in accordance with the patent application P.427855. The test subjects were three people whose task was to propel the wheelchair in two assistance modes supporting manual propulsion. The first mode is hill-climbing assistance, while the second one is assistance with propulsion torque in the propulsive phase. During the tests, several kinematic parameters of the wheelchair were monitored. An in-depth analysis was performed for the amplitude of speed during a hill climb and the number of propulsive cycles performed on a hill. The tests performed showed that when propelling the wheelchair only using the hand rims, the subject needed an average of 13 ± 1 pushes on the uphill slope, and their speed amplitude was 1.8 km/h with an average speed of 1.73 km/h. The climbing assistance mode reduced the speed amplitude to 0.76 km/h. The torque-assisted mode in the propulsive phase reduced the number of cycles required to climb the hill from 13 to 6, while in the climbing assistance mode the number of cycles required to climb the hill was reduced from 12 to 10 cycles. The tests were carried out at various values of assistance and assistance amplification coefficient, and the most optimally selected parameters of this coefficient are presented in the results. The tests proved that electric propulsion assistance has a beneficial and significant impact on the kinematics of manual wheelchair propulsion when compared to a classic manual propulsion system when overcoming hills. In addition, assistance and assistance amplification coefficient were proved to be correlated with operating conditions and the user’s individual characteristics.

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Kumar, Vivek, and Nitin Kaistha. "Hill-Climbing for Economic Plantwide Control." IFAC Proceedings Volumes 47, no. 3 (2014): 7641–46. http://dx.doi.org/10.3182/20140824-6-za-1003.02787.

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Choi, Seungryul, and Donald Yeung. "Hill-climbing SMT processor resource distribution." ACM Transactions on Computer Systems 27, no. 1 (February 2009): 1–47. http://dx.doi.org/10.1145/1482619.1482620.

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Vaughan, Diane E., and Sheldon H. Jacobson. "Tabu Guided Generalized Hill Climbing Algorithms." Methodology and Computing in Applied Probability 6, no. 3 (September 2004): 343–54. http://dx.doi.org/10.1023/b:mcap.0000026564.87435.66.

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Yoshizawa, Hiroki, and Shuji Hashimoto. "Genetic Algorithms As Hill-climbing Methods." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 1998 (May 5, 1998): 33–38. http://dx.doi.org/10.5687/sss.1998.33.

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Burke, Edmund K., and Yuri Bykov. "The late acceptance Hill-Climbing heuristic." European Journal of Operational Research 258, no. 1 (April 2017): 70–78. http://dx.doi.org/10.1016/j.ejor.2016.07.012.

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Khari, Manju, and Prabhat Kumar. "Empirical Evaluation of Hill Climbing Algorithm." International Journal of Applied Metaheuristic Computing 8, no. 4 (October 2017): 27–40. http://dx.doi.org/10.4018/ijamc.2017100102.

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The software is growing in size and complexity every day due to which strong need is felt by the research community to search for the techniques which can optimize test cases effectively. The current study is inspired by the collective behavior of finding paths from the colony of food and uses different versions of Hill Climbing Algorithm (HCA) such as Stochastic, and Steepest Ascent HCA for the purpose of finding a good optimal solution. The performance of the proposed algorithm is verified on the basis of three parameters comprising of optimized test cases, time is taken during the optimization process, and the percentage of optimization achieved. The results suggest that proposed Stochastic HCA is significantly average percentage better than Steepest Ascent HCA in reducing the number of test cases in order to accomplish the optimization target.

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Tovey, Craig A. "Hill Climbing with Multiple Local Optima." SIAM Journal on Algebraic Discrete Methods 6, no. 3 (July 1985): 384–93. http://dx.doi.org/10.1137/0606040.

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Greiner, Russell. "PALO: a probabilistic hill-climbing algorithm." Artificial Intelligence 84, no. 1-2 (July 1996): 177–208. http://dx.doi.org/10.1016/0004-3702(95)00040-2.

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Sathiyaraj, Chinnasamy, M. Ramachandran, M. Amudha, and Ramu Kurinjimalar. "A Review on Hill Climbing Optimization Methodology." Recent trends in Management and Commerce 3, no. 1 (January 31, 2022): 1–7. http://dx.doi.org/10.46632/rmc/3/1/1.

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The activity of walking through hilly country for pleasure. He is an avid athlete and loves mountain walking. Mountaineering is a terrifying quest used for mathematical optimization problems in the field of artificial intelligence. Given a large input and a good horistic function, it tries to find a good enough solution to the problem. The mountaineering algorithm consists of three parts, where the global maximum or optimal solution cannot be reached: the local maximum, the ridge and the plateau. The trek is not complete or optimal, the time complex of O (∞) but the space complex of O (b). There is no special processing data system as mountaineering rejects old nodes. Trekking in the Alps or other high mountains. This is not an efficient method. This does not apply to problems where the value of the horticultural function suddenly decreases while the solution is in view. First-choice trekking enables balanced trekking by randomly creating heirs until something better than the current situation develops. Whenever this is a good strategy there are many (e.g., thousands) heirs in a state. So the first preferred mountain climbing is a special type Random mountain climbing. Description. This is a robust mountaineering algorithm. A person is initiated approximately. When the individual reaches a local optimal state a new solution is created approximately and mountaineering begins again. The best first search is a traversal technique, which checks which node is the most reliable and decides which node to visit next by checking it. To this end, it uses the appraisal function to determine travel. Climbing is used to describe traditional ‘siege’ techniques, where you will climb the mountain several times before being driven to the summit. Albinism, on the other hand, focuses on 'fast and light' climbs. Free climbing was created to describe any style of climbing that is not AIDS related. ... In free climbing, the climber moves the wall under their own force without the use of any special gear (except for the climbing shoes) to help them move upwards. Climbers can only survive for a short time in the 'death zone' at 8000 m and above, where there are numerous challenges. Deep cracks, avalanches, cliffs and snowflakes make the high form of trekking a very dangerous endeavor. Caldwell and George's son use headlamps to illuminate their way, climbing at night when the temperature is cold - meaning their hands sweat less and there is more friction between their rubber shoes and granite. According to the author, climbing mountains is a very difficult task for people and they enjoy crossing obstacles. Mountaineering is neither complete nor optimal, the time complex of O (∞) but the space complex of O (b). There is no special processing data system as mountaineering rejects old nodes

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Seck-Tuoh-Mora, Juan Carlos, Nayeli Jazmín Escamilla-Serna, Leonardo Javier Montiel-Arrieta, Irving Barragan-Vite, and Joselito Medina-Marin. "A Global Neighborhood with Hill-Climbing Algorithm for Fuzzy Flexible Job Shop Scheduling Problem." Mathematics 10, no. 22 (November 12, 2022): 4233. http://dx.doi.org/10.3390/math10224233.

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The Flexible Job Shop Scheduling Problem (FJSSP) continues to be studied extensively to test new metaheuristics and because of its closeness to current production systems. A variant of the FJSSP uses fuzzy processing times instead of fixed times. This paper proposes a new algorithm for FJSSP with fuzzy processing times called the global neighborhood with hill-climbing algorithm (GN-HC). This algorithm performs solution exploration using simple operators concurrently for global search neighborhood handling. For local search, random restart hill-climbing is applied at each solution to find the best machine for each operation. For the selection of operations in hill climbing, a record of the operations defining the fuzzy makespan is employed to use them as a critical path. Finally, an estimation of the crisp makespan with the longest processing times in hill climbing is made to improve the speed of the GN-HC. The GN-HC is compared with other recently proposed methods recognized for their excellent performance, using 6 FJSSP instances with fuzzy times. The obtained results show satisfactory competitiveness for GN-HC compared to state-of-the-art algorithms. The GN-HC implementation was performed in Matlab and can be found on GitHub (check Data Availability Statement at the end of the paper).

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Li, An Lin, Huan Ping Zhang, Hui Du, Yang Li, and Hai Xiang Song. "The Application of Improved Hill-Climbing in the Multiple Nonlinear Regression." Advanced Materials Research 396-398 (November 2011): 2353–56. http://dx.doi.org/10.4028/www.scientific.net/amr.396-398.2353.

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The hill-climbing has been improved by using the percentage random step and by changing variable values instantly, which is under the condition of an improvement in the objective function having been made by a step forward of a variable in a certain direction, and the improved hill-climbing has been used to solve nonlinear regression problems , and the specific algorithm steps is given, and it has been validated with satisfactory results by using practical examples.

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Yu, Yuan Chih, Shing Chern D. You, and Dwen Ren Tsai. "Hill Climbing Algorithm for License Plate Recognition." Advanced Materials Research 267 (June 2011): 995–1000. http://dx.doi.org/10.4028/www.scientific.net/amr.267.995.

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Histogram thresholding has been widely used for image processing—it is simple, fast, and computationally inexpensive. In this paper, we develop a creative approach based on histogram’s distributions to segment interest regions from background. Unlike the existing threshold detection methods which measure the statistics of histogram in the multi-modal images, our approach analyses the shape representation of multi-modal which has several hill-climbing curves. The behavior of algorithm works like human vision which focuses on the high contrast areas and scans the shape variation first. Moreover, such an algorithm presents a new type of histogram analysis that depends on the particular shape of certain distribution in histogram. Experimental results reveal that the proposed algorithm performs distinct effects especially on the recognition of artificial signs such as road sign, vehicle plate, and signboard.

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Siegel, Roberta S., Karen Foerstel, Herbert N. Foerstel, and Sandy McClure. "Climbing the Hill: Gender Conflict in Congress." Political Science Quarterly 112, no. 2 (1997): 322. http://dx.doi.org/10.2307/2657955.

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Lyakhovich, Leonid S., Pavel A. Akimov, and Boris A. Tukhfatullin. "ABOUT HILL-CLIMBING PROBLEMS IN STRUCTURAL MECHANICS." International Journal for Computational Civil and Structural Engineering 13, no. 2 (June 30, 2017): 103–24. http://dx.doi.org/10.22337/2587-9618-2017-13-2-103-124.

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The distinctive paper is devoted to some topical issues related to hill-climbing problems (minimum search, maximum search in structural mechanics. Theoretical foundations for computing of the minimum and maximum of functions and functionals (these problem normally turn up within variational methods) including several simplest samples and the corresponding mixed case of finding the minimum (maximum) of the integral, when both the variables and the functions vary

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Maiorana, Emanuele, Gabriel Emile Hine, and Patrizio Campisi. "Hill-Climbing Attacks on Multibiometrics Recognition Systems." IEEE Transactions on Information Forensics and Security 10, no. 5 (May 2015): 900–915. http://dx.doi.org/10.1109/tifs.2014.2384735.

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Prügel-Bennett, Adam. "When a genetic algorithm outperforms hill-climbing." Theoretical Computer Science 320, no. 1 (June 2004): 135–53. http://dx.doi.org/10.1016/j.tcs.2004.03.038.

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Al-Betar, Mohammed Azmi, Ibrahim Aljarah, Mohammed A. Awadallah, Hossam Faris, and Seyedali Mirjalili. "Adaptive $$\beta -$$ β - hill climbing for optimization." Soft Computing 23, no. 24 (March 9, 2019): 13489–512. http://dx.doi.org/10.1007/s00500-019-03887-7.

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Rjoub, Abdoul. "Courses timetabling based on hill climbing algorithm." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 6558. http://dx.doi.org/10.11591/ijece.v10i6.pp6558-6573.

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In addition to its monotonous nature and excessive time requirements, the manual school timetable scheduling often leads to more than one class being assigned to the same instructor, or more than one instructor being assigned to the same classroom during the same slot time, or even leads to exercise in intentional partialities in favor of a particular group of instructors. In this paper, an automated school timetable scheduling is presented to help overcome the traditional conflicts inherent in the manual scheduling approach. In this approach, hill climbing algorithms have been modified to transact hard and soft constraints. Soft constraints are not easy to be satisfied typically, but hard constraints are obligated. The implementation of this technique has been successfully experimented in different schools with various kinds of side constraints. Results show that the initial solution can be improved by 72% towards the optimal solution within the first 5 seconds and by 50% from the second iteration while the optimal solution will be achieved after 15 iterations ensuring that more than 50% of scientific courses will take place in the early slots time while more than 50% of non-scientific courses will take place during the later time's slots.

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Wallenberg, Nils, Fredrik Lindberg, and David Rayner. "Locating trees to mitigate outdoor radiant load of humans in urban areas using a metaheuristic hill-climbing algorithm – introducing TreePlanter v1.0." Geoscientific Model Development 15, no. 3 (February 7, 2022): 1107–28. http://dx.doi.org/10.5194/gmd-15-1107-2022.

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Abstract. Mean radiant temperature (Tmrt) is a frequently used measure of outdoor radiant heat conditions. Excessive Tmrt, linked especially to clear and warm days, has a negative effect on human wellbeing. The highest Tmrt on such days is found in sunlit areas, whereas shaded areas have significantly lower values. One way of alleviating high Tmrt is by planting trees to provide shade in exposed areas. Achieving the most efficient mitigation of excessive Tmrt by tree shade with multiple trees requires optimized positioning of the trees, which is a computationally extensive procedure. By utilizing metaheuristics, the number of calculations can be reduced. Here, we present TreePlanter v1.0, which applies a metaheuristic hill-climbing algorithm on input raster data of Tmrt and shadow patterns to position trees in complex urban areas. The hill-climbing algorithm enables dynamic exploration of the input data to position trees, compared with very computationally demanding brute-force calculations. The hill-climbing algorithm has been evaluated with a static greedy algorithm that positions trees one at a time based on ranking and is expected to always find relevant locations for trees. The results show that the hill-climbing algorithm, in relatively low model runtime, can find positions for several trees simultaneously, which lowers Tmrt substantially. TreePlanter, with its two algorithms, can assist in optimization of tree planting in urban areas to decrease thermal discomfort.

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Fronita, Mona, Rahmat Gernowo, and Vincencius Gunawan. "Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping." E3S Web of Conferences 31 (2018): 11017. http://dx.doi.org/10.1051/e3sconf/20183111017.

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Traveling Salesman Problem (TSP) is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour’s to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.

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Shen, Guohui, Jianfeng Yao, Wenjuan Lou, Yong Chen, Yong Guo, and Yuelong Xing. "An Experimental Investigation of Streamwise and Vertical Wind Fields on a Typical Three-Dimensional Hill." Applied Sciences 10, no. 4 (February 21, 2020): 1463. http://dx.doi.org/10.3390/app10041463.

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To study the streamwise and vertical wind fields on a typical three-dimensional hill, wind tunnel tests were performed. The mean values and turbulence intensities of the streamwise and vertical wind speeds of the typical positions above the hill were measured, and they are presented in the form of contour maps for design. Furthermore, the speed-up of the mean wind speeds in the streamwise direction was compared with codes. Finally, the windage yaw of a jumper cable was examined as an example of how to take into account the streamwise and vertical wind field influence on the wind load in the analysis of wind-induced responses. The results show that the most significant speed-up effect in the streamwise direction occurs on the hill crest, and the wind speed-up decreases with the increase of the height. Overall, the wind speed-up along the crosswind center line is larger than that along the along-wind center line of the hill. In the codes, the speed-up effect specified for the structure at half the height of the upstream side of the hill is relatively conservative. With regard to the mean wind speed in the vertical direction, the wind climbing effect located at half the height of the upstream side of the hill is the most significant. The area with the stronger turbulence intensity appears at the foot of the upstream and downstream sides of the hill. The influence of the vertical wind on the jumper cable is remarkable where the wind climbing effect is the most significant, which is worthy of attention in the design of the structure immersed in a hilly terrain-disturbed wind field.

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Wei, Chih Chiang. "Receiver Operating Characteristic for Diagnosis of Wine Quality by Bayesian Network Classifiers." Advanced Materials Research 591-593 (November 2012): 1168–73. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.1168.

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This paper is dedicated to demonstrate the use of the receiver operating characteristic (ROC) and the area under the ROC curve (AUC) for diagnosing forecast skill. Several local search heuristic algorithms to discover which one performs better for learning a certain Bayesian networks (BN). Five heuristic search algorithms, including K2, Hill Climbing, Repeated Hill Climber, LAGD Hill Climbing, and TAN, were empirically evaluated and compared. This study tests BN models in a real-world case, the Vinho Verde wine taste preferences. An average AUC of 0.746 and 0.727 respectively in red wine and white wine were obtained by TAN algorithm. The results show that the use of TAN can effectively improve the AUC measures for predicting quality grade.

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John, Collether. "High Speed Hill Climbing Algorithm for Portfolio Optimization." Tanzania Journal of Science 47, no. 3 (August 15, 2021): 1236–42. http://dx.doi.org/10.4314/tjs.v47i3.31.

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Portfolio can be defined as a collection of investments. Portfolio optimization usually is about maximizing expected return and/or minimising risk of a portfolio. The mean-variance model makes simplifying assumptions to solve portfolio optimization problem. Presence of realistic constraints leads to a significant different and complex problem. Also, the optimal solution under realistic constraints cannot always be derived from the solution for the frictionless market. The heuristic algorithms are alternative approaches to solve the extended problem. In this research, a heuristic algorithm is presented and improved for higher efficiency and speed. It is a hill climbing algorithm to tackle the extended portfolio optimization problem. The improved algorithm is Hill Climbing Simple–with Reducing Thresh-hold Percentage, named HC-S-R. It is applied in standard portfolio optimization problem and benchmarked with the quadratic programing method and the Threshold Accepting algorithm, a well-known heuristic algorithm for portfolio optimization problem. The results are also compared with its original algorithm HC-S. HC-S-R proves to be a lot faster than HC-S and TA and more effective and efficient than TA. Keywords: Portfolio optimization; Hill climbing algorithm; Threshold percentage; Reducing sequence; Threshold Acceptance algorithm

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Gent, I. P., and T. Walsh. "An Empirical Analysis of Search in GSAT." Journal of Artificial Intelligence Research 1 (September 1, 1993): 47–59. http://dx.doi.org/10.1613/jair.7.

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We describe an extensive study of search in GSAT, an approximation procedure for propositional satisfiability. GSAT performs greedy hill-climbing on the number of satisfied clauses in a truth assignment. Our experiments provide a more complete picture of GSAT's search than previous accounts. We describe in detail the two phases of search: rapid hill-climbing followed by a long plateau search. We demonstrate that when applied to randomly generated 3SAT problems, there is a very simple scaling with problem size for both the mean number of satisfied clauses and the mean branching rate. Our results allow us to make detailed numerical conjectures about the length of the hill-climbing phase, the average gradient of this phase, and to conjecture that both the average score and average branching rate decay exponentially during plateau search. We end by showing how these results can be used to direct future theoretical analysis. This work provides a case study of how computer experiments can be used to improve understanding of the theoretical properties of algorithms.

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Dunn, N. A., J. S. Conery, and S. R. Lockery. "Circuit Motifs for Spatial Orientation Behaviors Identified by Neural Network Optimization." Journal of Neurophysiology 98, no. 2 (August 2007): 888–97. http://dx.doi.org/10.1152/jn.00074.2007.

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Spatial orientation behavior is universal among animals, but its neuronal basis is poorly understood. The main objective of the present study was to identify candidate patterns of neuronal connectivity (motifs) for two widely recognized classes of spatial orientation behaviors: hill climbing, in which the organism seeks the highest point in a spatial gradient, and goal seeking, in which the organism seeks an intermediate point in the gradient. Focusing on simple networks of graded processing neurons characteristic of Caenorhabditis elegans and other nematodes, we used an unbiased optimization algorithm to seek values of neuronal time constants, resting potentials, and synaptic strengths sufficient for each type of behavior. We found many different hill-climbing and goal-seeking networks that performed equally well in the two tasks. Surprisingly, however, each hill-climbing network represented one of just three fundamental circuit motifs, and each goal-seeking network comprised two of these motifs acting in concert. These motifs are likely to inform the search for the real circuits that underlie these behaviors in nematodes and other organisms.

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Lizun, Damian. "From Paris and Shanghai to Singapore: A Multidisciplinary Study in Evaluating the Provenance and Dating of Two of Liu Kang’s Paintings." Journal of Conservation Science 37, no. 4 (August 31, 2021): 322–39. http://dx.doi.org/10.12654/jcs.2021.37.4.02.

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This paper focuses on the dating and provenance of two paintings, Climbing the hill and View from St. John’s Fort by the prominent Singaporean artist Liu Kang (1911–2004). Climbing the hill, from the National Gallery Singapore collection, was believed to have been created in 1937, based on the date painted by the artist. However, a non-invasive examination unveiled evidence of an underlying paint scheme and a mysterious date, 1948 or 1949. These findings prompted a comprehensive technical study of the artwork in conjunction with comparative analyses of View from St. John’s Fort (1948), from the Liu family collection. The latter artwork is considered to be depicting the same subject matter. The investigation was carried out with UVF, NIR, IRFC, XRR, digital microscopy, PLM and SEM-EDS to elucidate the materials and technique of both artworks and find characteristic patterns that could indicate a relationship between both paintings and assist in correctly dating Climbing the hill. The technical analyses were supplemented with the historical information derived from the Liu family archives. The results showed that Climbing the hill was created in 1948 or 1949 on top of an earlier composition painted in Shanghai between 1933 and 1937. As for the companion View from St. John’s Fort from 1948, the artist reused an earlier painting created in France in 1931. The analytical methods suggested that Liu Kang used almost identical pigment mixtures for creating new artworks. However, their painting technique demonstrates some differences. Overall, this study contributes to the understanding of Liu Kang’s painting materials and his working practice.

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Kun, Huang, and Wu Jun. "A botnet detection method based on FARIMA and hill-climbing algorithm." International Journal of Modern Physics B 32, no. 32 (December 30, 2018): 1850356. http://dx.doi.org/10.1142/s0217979218503563.

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In order to solve the problem of detection efficiency and the detection speed in botnet detection, a novel botnet detection method is proposed based on hill-climbing algorithm and FARIMA. At first, the evaluation indexes are presented in this method, and botnet and infection hosts are quickly searched with hill-climbing algorithm. Then, FARIMA model is introduced to cut down the long-correlation of detection index. Finally, a simulation was conducted to research on the key factors with MATLAB. The result shows that, compared to other algorithms, it has good adaptability, and it could effectively search for infected hosts and botnets.

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Rattaphun, Munlika, Amorntip Prayoonwong, Chih-Yi Chiu, and Kritaphat Songsri-in. "Improving Cluster-Based Index Structure for Approximate Nearest Neighbor Graph Search by Deep Learning-Based Hill-Climbing." ASEAN Journal of Scientific and Technological Reports 25, no. 3 (September 28, 2022): 25–33. http://dx.doi.org/10.55164/ajstr.v25i3.247183.

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This study presents a novel approach to archive an excellent tradeoff between search accuracy and computation cost in approximate nearest neighbor search. Usually, the k-nearest neighbor (kNN) graph and hill-climbing algorithm are adopted to accelerate the search process. However, using random seeds in the original hill-climbing is inefficient as they initiate an unsuitable search with inappropriate sources. Instead, we propose a neural network model to generate high-quality seeds that can boost query assignment efficiency. We evaluated the experiment on the benchmarks of SIFT1M and GIST1M datasets and showed the proposed seed prediction model effectively improves the search performance.

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Zhang, Xing Wen. "Does Complex Metaheuristic Out-Perform Simple Hill-Climbing for Optimization Problems? A Simulation Evaluation." Advanced Materials Research 748 (August 2013): 666–69. http://dx.doi.org/10.4028/www.scientific.net/amr.748.666.

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In this paper we compare the performance of metaheuristic methods, namely simulated annealing and Tabu Search, against simple hill climbing heuristic on a supply chain optimization problem. The benchmark problem we consider is the retailer replenishment optimization problem for a retailer selling multiple products. Computation and simulation results demonstrate that simulated annealing and Tabu search improve solution quality. However, the performance improvement is less in simulations with random noise. Lastly, simulated annealing appears to be more robust than Tabu search, and the results justify its extra implementation effort and computation time when compared against hill climbing.

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Alsukni, Emad, Omar Suleiman Arabeyyat, Mohammed A. Awadallah, Laaly Alsamarraie, Iyad Abu-Doush, and Mohammed Azmi Al-Betar. "Multiple-Reservoir Scheduling Using β-Hill Climbing Algorithm." Journal of Intelligent Systems 28, no. 4 (September 25, 2019): 559–70. http://dx.doi.org/10.1515/jisys-2017-0159.

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Abstract The multi-reservoir systems optimization problem requires defining a set of rules to recognize the water amount stored and released in accordance with the system constraints. Traditional methods are not suitable for complex multi-reservoir systems with high dimensionality. Recently, metaheuristic-based algorithms such as evolutionary algorithms and local search-based algorithms are successfully used to solve the multi-reservoir systems. β-hill climbing is a recent metaheuristic local search-based algorithm. In this paper, the multi-reservoir systems optimization problem is tackled using β-hill climbing. In order to validate the proposed method, four-reservoir systems used in the literature to evaluate the algorithm are utilized. A comparative evaluation is conducted to evaluate the proposed method against other methods found in the literature. The obtained results show the competitiveness of the proposed algorithm.

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Weekes, Ann Owens, and Lilian R. Furst. "Women Healers & Physicians: Climbing a Long Hill." Rocky Mountain Review of Language and Literature 52, no. 1 (1998): 76. http://dx.doi.org/10.2307/1348299.

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Schaars, Mary Jo. "Hill-Climbing with Thoreau: Creating Meaningful Carry-over." English Journal 79, no. 7 (November 1990): 52. http://dx.doi.org/10.2307/818718.

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Tabor, W. "Learning exponential state-growth languages by hill climbing." IEEE Transactions on Neural Networks 14, no. 2 (March 2003): 444–46. http://dx.doi.org/10.1109/tnn.2003.809421.

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Ghai, Saloni, and Sarabjit Kaur. "A Hill-Climbing Approach for Test Case Prioritization." International Journal of Software Engineering and Its Applications 11, no. 3 (March 31, 2017): 13–20. http://dx.doi.org/10.14257/ijseia.2017.11.3.02.

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Journal articles on the topic 'Hill climbing'

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