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Journal articles on the topic 'Hill-climbing optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

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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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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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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4

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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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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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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7

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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8

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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9

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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10

Jacobson, Sheldon H., and Enver Y¨cesan. "Global Optimization Performance Measures for Generalized Hill Climbing Algorithms." Journal of Global Optimization 29, no. 2 (June 2004): 173–90. http://dx.doi.org/10.1023/b:jogo.0000042111.72036.11.

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11

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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12

Nagarajan, Karthikeyan. "A Predictive Hill Climbing Algorithm for Real Valued multi-Variable Optimization Problem like PID Tuning." International Journal of Machine Learning and Computing 8, no. 1 (February 2018): 14–19. http://dx.doi.org/10.18178/ijmlc.2018.8.1.656.

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13

Huang, Yunqing, and Kai Jiang. "Hill-Climbing Algorithm with a Stick for Unconstrained Optimization Problems." Advances in Applied Mathematics and Mechanics 9, no. 2 (January 9, 2017): 307–23. http://dx.doi.org/10.4208/aamm.2016.m1481.

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AbstractInspired by the behavior of the blind for hill-climbing using a stick to detect a higher place by drawing a circle, we propose a heuristic direct search method to solve the unconstrained optimization problems. Instead of searching a neighbourhood of the current point as done in the traditional hill-climbing, or along specified search directions in standard direct search methods, the new algorithm searches on a surface with radius determined by the motion of the stick. The significant feature of the proposed algorithm is that it only has one parameter, the search radius, which makes the algorithm convenient in practical implementation. The developed method can shrink the search space to a closed ball, or seek for the final optimal point by adjusting search radius. Furthermore our algorithm possesses multi-resolution feature to distinguish the local and global optimum points with different search radii. Therefore, it can be used by itself or integrated with other optimization methods flexibly as a mathematical optimization technique. A series of numerical tests, including high-dimensional problems, have been well designed to demonstrate its performance.

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14

Kazarlis, S. A., S. E. Papadakis, J. B. Theocharis, and V. Petridis. "Microgenetic algorithms as generalized hill-climbing operators for GA optimization." IEEE Transactions on Evolutionary Computation 5, no. 3 (June 2001): 204–17. http://dx.doi.org/10.1109/4235.930311.

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15

Lu, Yong Hong, Ji Hua Dou, Xing Bao Yang, and Chuan Wei Zhu. "Firepower Assignment of Ship-to-Air Missile System." Advanced Materials Research 905 (April 2014): 702–5. http://dx.doi.org/10.4028/www.scientific.net/amr.905.702.

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Hybrid genetic algorithm has been proposed in this paper, which is proposed by combining standard genetic algorithm with hill climbing to solve the unconstrained optimization problem, which can get global optimization results of the firepower assignment, and provide decision support for the firepower assignment.

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16

Liang, Xiao Wen, Wei Gong, Wen Long Fu, and Jing Qi. "Research on the Initial Value of the Simulated Annealing." Advanced Materials Research 774-776 (September 2013): 1770–73. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1770.

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Simulated Annealing Algorithm is one of the top ten classical optimization algorithm, and it has been successfully applied to various fields. Simulated annealing is a optimization algorithm which can find the global optimal solution, compares to neural network algorithm, it is so easily to implement that has higher probability to be adopted, but it has own shortcomings like other optimization algorithms, its result largely depends on initial value, The initial value of the traditional simulated annealing algorithm began with a random number, its convergence speed is often slow very much and the effect is bad. In this paper, a new simulated annealing algorithm that based on genetic algorithm and hill-climbing method was brought up, because of hill-climbing algorithm was easy to fall into local optimum, and simulated annealing can just solve the problem, it not only escaped from local optimum, but also got good convergence speed and results.

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17

Abu Doush, Iyad, and Eugene Santos. "Best Polynomial Harmony Search with Best β-Hill Climbing Algorithm." Journal of Intelligent Systems 30, no. 1 (May 30, 2020): 1–17. http://dx.doi.org/10.1515/jisys-2019-0101.

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Abstract Harmony Search Algorithm (HSA) is an evolutionary algorithm which mimics the process of music improvisation to obtain a nice harmony. The algorithm has been successfully applied to solve optimization problems in different domains. A significant shortcoming of the algorithm is inadequate exploitation when trying to solve complex problems. The algorithm relies on three operators for performing improvisation: memory consideration, pitch adjustment, and random consideration. In order to improve algorithm efficiency, we use roulette wheel and tournament selection in memory consideration, replace the pitch adjustment and random consideration with a modified polynomial mutation, and enhance the obtained new harmony with a modified β-hill climbing algorithm. Such modification can help to maintain the diversity and enhance the convergence speed of the modified HS algorithm. β-hill climbing is a recently introduced local search algorithm that is able to effectively solve different optimization problems. β-hill climbing is utilized in the modified HS algorithm as a local search technique to improve the generated solution by HS. Two algorithms are proposed: the first one is called PHSβ–HC and the second one is called Imp. PHSβ–HC. The two algorithms are evaluated using 13 global optimization classical benchmark function with various ranges and complexities. The proposed algorithms are compared against five other HSA using the same test functions. Using Friedman test, the two proposed algorithms ranked 2nd (Imp. PHSβ–HC) and 3rd (PHSβ–HC). Furthermore, the two proposed algorithms are compared against four versions of particle swarm optimization (PSO). The results show that the proposed PHSβ–HC algorithm generates the best results for three test functions. In addition, the proposed Imp. PHSβ–HC algorithm is able to overcome the other algorithms for two test functions. Finally, the two proposed algorithms are compared with four variations of differential evolution (DE). The proposed PHSβ–HC algorithm produces the best results for three test functions, and the proposed Imp. PHSβ–HC algorithm outperforms the other algorithms for two test functions. In a nutshell, the two modified HSA are considered as an efficient extension to HSA which can be used to solve several optimization applications in the future.

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Fitriati, Desti, and Nura Meutia Nessrayasa. "IMPLEMENTASI ALGORITMA HILL CLIMBING PADA PENENTUAN JARAK TERPENDEK KOTA WISATA DI INDONESIA." Jurnal Riset Informatika 1, no. 3 (July 7, 2019): 127–32. http://dx.doi.org/10.34288/jri.v1i3.35.

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Searching and determining the shortest route is a complex problem, looking for the shortest route from a number of attractions and the distance between attractions. With varying access paths, the shortest route search becomes the right choice using a website-based app that provides the closest route on a map using the SAHC (Steepest Ascent Hill Climbing) algorithm. Steepest Ascent Hill Climbing is a method of an algorithm that is widely used for optimization problems. One application is to find the shortest route by maximizing or minimizing the value of the existing optimization function. In research ii study using 34 provinces in Indonesia and every province, there are 5 most popular tour, accuracy value obtained in research determination of the shortest distance of tourist city in Indonesia is 93,3%.

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Lim, Andrew, Jing Lin, and Fei Xiao. "Particle Swarm Optimization and Hill Climbing for the bandwidth minimization problem." Applied Intelligence 26, no. 3 (January 18, 2007): 175–82. http://dx.doi.org/10.1007/s10489-006-0019-x.

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20

Lim, Andrew, Jing Lin, Brian Rodrigues, and Fei Xiao. "Ant colony optimization with hill climbing for the bandwidth minimization problem." Applied Soft Computing 6, no. 2 (January 2006): 180–88. http://dx.doi.org/10.1016/j.asoc.2005.01.001.

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21

Vaughan, Diane E., Sheldon H. Jacobson, and Hemanshu Kaul. "Analyzing the performance of simultaneous generalized hill climbing algorithms." Computational Optimization and Applications 37, no. 1 (March 20, 2007): 103–19. http://dx.doi.org/10.1007/s10589-007-9019-y.

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22

Vieira, Heitor Albuquerque, Rafael Salema Marques, and Paulo André Lima De Castro. "Otimização paramétrica através de busca Hill Climbing distribuída em sistemas multiagentes / Parametric optimization via distributed Hill Climbing search in multi-agent systems." Brazilian Journal of Development 7, no. 8 (August 4, 2021): 76736–47. http://dx.doi.org/10.34117/bjdv7n8-070.

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23

Raska, Pavel, and Ulrych Zdenek. "Simulation Optimization – Testing Selected Optimization Methods and their Setting of the Parameters." Advanced Materials Research 980 (June 2014): 198–202. http://dx.doi.org/10.4028/www.scientific.net/amr.980.198.

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The paper deals with testing optimization methods and their setting of the parameters used to search for the global optimum of specified objective functions. The objective functions were specified considering the objectives of the discrete event simulation models. We specified the evaluation methods considering the success of finding the global optimum (or the best found objective function value) the in defined search space. We tested Random Search, Hill Climbing, Tabu Search, Local Search, Downhill Simplex, Simulated Annealing, Differential Evolution and Evolution Strategy. After the testing we proposed some slight modifications of the Downhill Simplex and Differential Evolution optimization methods.

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Wang, Xue Lian, Man Xu, Jing Xiao, and Ran Guo. "Research on Optimization Scheduling Problem in Complex Conditions." Applied Mechanics and Materials 519-520 (February 2014): 1520–24. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1520.

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The paper presented an optimization scheduling problem in complex conditions. A genetic algorithm and tabu search hybrid algorithm (GATS) was designed to solve this problem. The algorithm used the global optimization capacity of genetic algorithm and the local hill climbing advantage of tabu search in the search process. The principium of the algorithm was introduced and a contrast experiment was carried out. The experiment and the analysis indicate the validity of the GATS to the optimization scheduling problem in complex conditions.

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25

Kharb, Seema, and Anita Singhrova. "Slot-frame Length Optimization using Hill Climbing for Energy Efficient TSCH Network." Procedia Computer Science 132 (2018): 541–50. http://dx.doi.org/10.1016/j.procs.2018.05.007.

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26

Vaughan, Diane E., Sheldon H. Jacobson, Shane N. Hall, and Laura A. McLay. "Simultaneous Generalized Hill-Climbing Algorithms for Addressing Sets of Discrete Optimization Problems." INFORMS Journal on Computing 17, no. 4 (November 2005): 438–50. http://dx.doi.org/10.1287/ijoc.1040.0064.

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27

Ceylan, Halim. "Developing Combined Genetic Algorithm—Hill-Climbing Optimization Method for Area Traffic Control." Journal of Transportation Engineering 132, no. 8 (August 2006): 663–71. http://dx.doi.org/10.1061/(asce)0733-947x(2006)132:8(663).

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28

Jiang, Tuping, Gang Ren, and Xing Zhao. "Evacuation Route Optimization based on Tabu Search Algorithm and Hill-climbing Algorithm." Procedia - Social and Behavioral Sciences 96 (November 2013): 865–72. http://dx.doi.org/10.1016/j.sbspro.2013.08.098.

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29

Shen, Chang-Yu, Xiao-Rong Yu, Qian Li, and Hai-Mei Li. "Gate Location Optimization in Injection Molding By Using Modified Hill-Climbing Algorithm." Polymer-Plastics Technology and Engineering 43, no. 3 (January 8, 2004): 649–59. http://dx.doi.org/10.1081/ppt-120038056.

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Wang, Hongfeng, Dingwei Wang, and Shengxiang Yang. "A memetic algorithm with adaptive hill climbing strategy for dynamic optimization problems." Soft Computing 13, no. 8-9 (August 5, 2008): 763–80. http://dx.doi.org/10.1007/s00500-008-0347-3.

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31

Ju, Xinglong, Victoria C. P. Chen, Jay M. Rosenberger, and Feng Liu. "Fast knot optimization for multivariate adaptive regression splines using hill climbing methods." Expert Systems with Applications 171 (June 2021): 114565. http://dx.doi.org/10.1016/j.eswa.2021.114565.

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32

Icasia, Gabriella, Raras Tyasnurita, and Etria Sepwardhani Purba. "Application of Heuristic Combinations in Hyper-Heuristic Framework for Exam Scheduling Problems." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 4 (August 17, 2020): 664–71. http://dx.doi.org/10.29207/resti.v4i4.2066.

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Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem. On a large scale of data, the examination timetabling problem becomes a complex problem and takes time if it solved manually. Therefore, heuristics exist to provide reasonable enough solutions and meet the constraints of the problem. In this study, a real-world dataset of Examination Timetabling (Toronto dataset) is solved using a Hill-Climbing and Tabu Search algorithm. Different from the approach in the literature, Tabu Search is a meta-heuristic method, but we implemented a Tabu Search within the hyper-heuristic framework. The main objective of this study is to provide a better understanding of the application of Hill-Climbing and Tabu Search in hyper-heuristics to solve timetabling problems. The results of the experiments show that Hill-Climbing and Tabu Search succeeded in automating the timetabling process by reducing the penalty 18-65% from the initial solution. Besides, we tested the algorithms within 10,000-100,000 iterations, and the results were compared with a previous study. Most of the solutions generated from this experiment are better compared to the previous study that also used Tabu Search algorithm.

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Y. M., Mr Raghavendra, and Dr U. B. Mahadevaswamy. "Energy efficient routing in wireless sensor network based on mobile sink guided by stochastic hill climbing." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 5965. http://dx.doi.org/10.11591/ijece.v10i6.pp5965-5973.

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In Wireless Sensor Networks (WSNs), the reduction of energy consumption in the batteries of a sensor node is an important task. Sensor nodes of WSNs perform three significant functions such as data sensing, data transmitting and data relaying. Routing technique is one of the methods to enhance the sensor nodes battery lifetime. Energy optimization is done by using one of the heuristic routing methods for sensing and transmitting the data. To enhance the energy optimization mainly concentrated on data relaying. In this work stochastic hill climbing is adapted. The proposed solution for data relaying utilizes geographical routing and mobile sink technique. The sink collects the data from cluster heads and movement of the sink is routed by stochastic hill climbing. Network simulator 2 is used for experimentation purpose. This work also compares with the existing routing protocols like Energy-efficient Low Duty Cycle (ELDC), Threshold sensitive Energy Efficient sensor Network (TEEN) and Adaptive clustering protocol. The proposed work shows promising results with respect to lifetime, average energy of nodes and packet delivery ratio.

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Jacobson, Sheldon H., and Enver Yücesan. "Analyzing the Performance of Generalized Hill Climbing Algorithms." Journal of Heuristics 10, no. 4 (July 2004): 387–405. http://dx.doi.org/10.1023/b:heur.0000034712.48917.a9.

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35

Bao, Danwen, Jiayu Gu, Zhiwei Di, and Tianxuan Zhang. "Optimization of Airport Shuttle Bus Routes Based on Travel Time Reliability." Mathematical Problems in Engineering 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/2369350.

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An optimization model of airport shuttle bus routes is constructed by taking operational reliability maximization as a main goal in this paper. Also, a hybrid genetic algorithm is designed to solve this problem. Then the theoretical method is applied to the case of Nanjing Lukou International Airport. During the research, a travel time reliability estimation method is proposed based on back propagation (BP) neural network. Absolute error and regression fitting methods are used to test the measurement results. It is proved that this method has higher accuracy and is applicable to calculate airport bus routes reliability. In algorithm design, the hill-climbing algorithm with strong local search ability is integrated into genetic algorithm. Initial solution is determined by hill-climbing algorithm so as to avoid the search process falling into a local optimal solution, which makes the accuracy of calculation result improved. However, the calculation results show that the optimization process of hybrid genetic algorithm is greatly affected by both the crossover rate and mutation rate. A higher mutation rate or lower crossover rate will decrease the stability of the optimization process. Multiple trials are required to determine the optimal crossover rate and mutation rate. The proposed method provides a scientific basis for optimizing the airport bus routes and improving the efficiency of airport’s external transportation services.

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36

Gonzaga de Oliveira, Sanderson L., and Libério M. Silva. "Low-cost heuristics for matrix bandwidth reduction combined with a Hill-Climbing strategy." RAIRO - Operations Research 55, no. 4 (July 2021): 2247–64. http://dx.doi.org/10.1051/ro/2021102.

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This paper studies heuristics for the bandwidth reduction of large-scale matrices in serial computations. Bandwidth optimization is a demanding subject for a large number of scientific and engineering applications. A heuristic for bandwidth reduction labels the rows and columns of a given sparse matrix. The algorithm arranges entries with a nonzero coefficient as close to the main diagonal as possible. This paper modifies an ant colony hyper-heuristic approach to generate expert-level heuristics for bandwidth reduction combined with a Hill-Climbing strategy when applied to matrices arising from specific application areas. Specifically, this paper uses low-cost state-of-the-art heuristics for bandwidth reduction in tandem with a Hill-Climbing procedure. The results yielded on a wide-ranging set of standard benchmark matrices showed that the proposed strategy outperformed low-cost state-of-the-art heuristics for bandwidth reduction when applied to matrices with symmetric sparsity patterns.

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37

Raska, Pavel, and Ulrych Zdenek. "Comparison of Selected Optimization Methods Used for Discrete Event Simulation Models and Testing Functions." Advanced Materials Research 816-817 (September 2013): 629–33. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.629.

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The paper deals with the comparison of selected optimization methods - Random Search, Hill Climbing, Tabu Search, Local Search, Downhill Simplex, Simulated Annealing, Differential Evolution and Evolution Strategy-used to search for the global optimum of the objective function specified for each simulation model. These optimization methods have to be modified in such a way that they are applicable for discrete event simulation optimization purposes. Three discrete event simulation models were built (using ARENA simulation software) which reflect real industrial systems. Then the optimization methods were tested on four testing functions. The evaluation method which uses information from the box plot characteristics was specified.

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Ryma, Guefrouchi, and Kholladi Mohamed-Khireddine. "Genetic Algorithm With Hill Climbing for Correspondences Discovery in Ontology Mapping." Journal of Information Technology Research 12, no. 4 (October 2019): 153–70. http://dx.doi.org/10.4018/jitr.2019100108.

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Meta-heuristics are used as a tool for ontology mapping process in order to improve their performance in mapping quality and computational time. In this article, ontology mapping is resolved as an optimization problem. It aims at optimizing correspondences discovery between similar concepts of source and target ontologies. For better guiding and accelerating the concepts correspondences discovery, the article proposes a meta-heuristic hybridization which incorporates the Hill Climbing method within the mutation operator in the genetic algorithm. For test concerns, syntactic and lexical similarities are used to validate correspondences in candidate mappings. The obtained results show the effectiveness of the proposition for improving mapping performances in quality and computational time even for large OAEI ontologies.

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Sakamoto, Shinji, Admir Barolli, Leonard Barolli, and Shusuke Okamoto. "Implementation of a Web interface for hybrid intelligent systems." International Journal of Web Information Systems 15, no. 4 (October 7, 2019): 420–31. http://dx.doi.org/10.1108/ijwis-10-2018-0071.

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PurposeThe purpose of this paper is to implement a Web interface for hybrid intelligent systems. Using the implemented Web interface, this paper evaluates two hybrid intelligent systems based on particle swarm optimization, hill climbing and distributed genetic algorithm to solve the node placement problem in wireless mesh networks (WMNs).Design/methodology/approachThe node placement problem in WMNs is well-known to be a computationally hard problem. Therefore, the authors use intelligent algorithms to solve this problem. The implemented systems are intelligent systems based on meta-heuristics algorithms: Particle Swarm Optimization (PSO), Hill Climbing (HC) and Distributed Genetic Algorithm (DGA). The authors implement two hybrid intelligent systems: WMN-PSODGA and WMN-PSOHC-DGA.FindingsThe authors carried out simulations using the implemented Web interface. From the simulations results, it was found that the WMN-PSOHC-DGA system has a better performance compared with the WMN-PSODGA system.Research limitations/implicationsFor simulations, the authors considered Normal distribution of mesh clients. In the future, the authors need to consider different client distributions, patterns, number of mesh nodes and communication distance.Originality/valueIn this research work, the authors implemented a Web interface for hybrid intelligent systems. The implemented interface can be extended for other metaheuristic algorithms.

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Sun, Kangjian, Heming Jia, Yao Li, and Zichao Jiang. "Hybrid improved slime mould algorithm with adaptive β hill climbing for numerical optimization." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 1667–79. http://dx.doi.org/10.3233/jifs-201755.

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Slime mould algorithm (SMA) is a novel metaheuristic that simulates foraging behavior of slime mould. Regarding its drawbacks and properties, a hybrid optimization (BTβSMA) based on improved SMA is proposed to produce the higher-quality optimal results. Brownian motion and tournament selection mechanism are introduced into the basic SMA to improve the exploration capability. Moreover, a local search algorithm (Adaptive β-hill climbing, AβHC) is hybridized with the improved SMA. It is considered from boosting the exploitation trend. The proposed BTβSMA algorithm is evaluated in two main phases. Firstly, the two improved hybrid variants (BTβSMA-1 and BTβSMA-2) are compared with the basic SMA algorithm through 16 benchmark functions. Also, the performance of winner is further evaluated through comparisons with 7 state-of-the-art algorithms. The simulation results report fitness and computation time. The convergence curve and boxplot visualize the effects of fitness. The comparison results on the function optimization suggest that BTβSMA is superior to competitors. Wilcoxon rank-sum test is also employed to investigate the significance of the results. Secondly, the applicability on real-world tasks is proved by solving structure engineering design problems and training multilayer perceptrons. The numerical results indicate the merits of the BTβSMA algorithm in terms of solution precision.

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Cardia, João Baptista, and Edilson Reis Rodrigues Kato. "Applying the Particle Swarm Optimization + Hill-climbing in the Flexible Job-Shop problem." Journal on Advances in Theoretical and Applied Informatics 3, no. 2 (December 29, 2017): 40. http://dx.doi.org/10.26729/jadi.v3i2.2430.

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The Flexible Job-Shop problem is a very interesting and important problem. In this paper it is studied an approach of the PSO (Particle Swarm Optimization) in the Flexible Job-Shop problem, the studied and applied approach is derived from a Travelling Salesman Problem solution with a few minor alterations, trying to reach the optimum values discovered by a series of other works. It is used only PSO and not mixed with any other auxiliary meta-heuristic.

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Ou, Ting-Chia, Wei-Fu Su, Xian-Zong Liu, Shyh-Jier Huang, and Te-Yu Tai. "A Modified Bird-Mating Optimization with Hill-Climbing for Connection Decisions of Transformers." Energies 9, no. 9 (August 23, 2016): 671. http://dx.doi.org/10.3390/en9090671.

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Vo, Tuong Quan, Hyoung Seok Kim, and Byung Ryong Lee. "Propulsive Velocity Optimization of 3-Joint Fish Robot Using Genetic-Hill Climbing Algorithm." Journal of Bionic Engineering 6, no. 4 (December 2009): 415–29. http://dx.doi.org/10.1016/s1672-6529(08)60140-7.

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Vo, Tuong Quan, Hyoung Seok Kim, and Byung Ryong Lee. "Smooth gait optimization of a fish robot using the genetic-hill climbing algorithm." Robotica 30, no. 2 (June 14, 2011): 257–78. http://dx.doi.org/10.1017/s0263574711000555.

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SUMMARYThis paper presents a model of a three-joint (four links) carangiform fish robot. The smooth gait or smooth motion of a fish robot is optimized by using a combination of the Genetic Algorithm (GA) and the Hill Climbing Algorithm (HCA) with respect to its dynamic system. Genetic algorithm is used to create an initial set of optimal parameters for the two input torque functions of the system. This set is then optimized by using HCA to ensure that the final set of optimal parameters is a “near” global optimization result. Finally, the simulation results are presented in order to demonstrate that the proposed method is effective.

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JACOBSON, SHELDON H., KELLY A. SULLIVAN, and ALAN W. JOHNSON. "DISCRETE MANUFACTURING PROCESS DESIGN OPTIMIZATION USING COMPUTER SIMULATION AND GENERALIZED HILL CLIMBING ALGORITHMS." Engineering Optimization 31, no. 2 (December 1998): 247–60. http://dx.doi.org/10.1080/03052159808941372.

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Sivakumar, R., and H. Mangalam. "Ensemble hill climbing optimization in adaptive cruise control for safe automated vehicle transportation." Journal of Supercomputing 76, no. 8 (September 18, 2019): 5780–800. http://dx.doi.org/10.1007/s11227-019-02994-4.

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Yıldız, Ali Rıza. "An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry." Journal of Materials Processing Technology 209, no. 6 (March 2009): 2773–80. http://dx.doi.org/10.1016/j.jmatprotec.2008.06.028.

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Zhang, Sheng, and Wu Sheng Liu. "The Optimization Method to Transfer Bus Routes for Rail Transit Terminal Station." Applied Mechanics and Materials 253-255 (December 2012): 1869–75. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1869.

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The optimization model is framed with a goal to minimize overall consumption of travel time for passengers. A variety of constrains are considered, including time, capacity, stop number, profit and so on. According to the features of the model, the hill-climbing algorithm is adopted to obtain the initial solution, which reduces the time of optimization. Meanwhile, direct order encoding method, namely node method, is introduced for encoding, construct a Hybrid Genetic Algorithm for the solution. The results show that adapter value is more steady and the model result is preferable when the variation rate is increased while the number of iteration is decreased.

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DOWSLAND, KATHRYN A. "HILL-CLIMBING, SIMULATED ANNEALING AND THE STEINER PROBLEM IN GRAPHS." Engineering Optimization 17, no. 1-2 (February 1991): 91–107. http://dx.doi.org/10.1080/03052159108941063.

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KOUTSOUGERAS, CRIS, and PEGGY ISRAEL. "A METHOD FOR PARALLEL SEARCH UNDER HIGHER-ORDER CONSTRAINTS." International Journal of Pattern Recognition and Artificial Intelligence 07, no. 03 (June 1993): 409–29. http://dx.doi.org/10.1142/s0218001493000200.

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There is a relation between the old hill-climbing method and the optimization which is performed by neural nets. This view gives rise to a parallel search method which is presented in this paper. The search mechanism is guided by the optimization of a function which is inspired by the electrostatic field. It is also shown here that this mechanism can naturally accommodate higher-order constraints which the result of the search must satisfy. It is also shown how such constraints can be systematically accommodated by simply expanding the function whose optimization guides the search. This mechanism simulates a dynamic transformation of a cue in which constraints and observational background knowledge compete to influence the transformation.

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