Relevant bibliographies by topics / Backtracking search optimization algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Backtracking search optimization algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Backtracking search optimization algorithm"

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Sikelis, Konstantinos, and George E. Tsekouras. "Feature Selection with a Backtracking Search Optimization Algorithm." ITM Web of Conferences 43 (2022): 01018. http://dx.doi.org/10.1051/itmconf/20224301018.

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Feature selection carries significance in the outcome of any classification or regression task. Exercising evolutionary computation algorithms in feature selection has led to the construction of efficient discrete optimization algorithms. In this paper, a modified backtracking search algorithm is employed to perform wrapper-based feature selection, where two modifications of the standard backtracking search algorithm are adopted. The first one concentrates on utilizing a particle ranking operator regarding the current population. The second one focuses on removing the case of using a single pa
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Xu, Qiu Yan. "Backtracking Search Optimization Algorithm with Low-Discrepancy Sequences for Mechanical Design Optimization Problems." Applied Mechanics and Materials 635-637 (September 2014): 270–73. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.270.

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This paper presents the backtracking search optimization algorithm with low-discrepancy sequences to solve mechanical design optimization problems involving problem-specific constraints and many different variables. Similar to other evolutionary algorithms, backtracking search optimization algorithm is sensitive to the initial population. Generally speaking, since there is no information about the optimization problem, the initial population should be created uniformly. The low-discrepancy sequences are employed to increase the uniformity of the initial population. The benchmark problems widel
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Ghanem, Khadoudja, and Abdesslem Layeb. "Feature Selection and Knapsack Problem Resolution Based on a Discrete Backtracking Optimization Algorithm." International Journal of Applied Evolutionary Computation 12, no. 2 (2021): 1–15. http://dx.doi.org/10.4018/ijaec.2021040101.

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Backtracking search optimization algorithm is a recent stochastic-based global search algorithm for solving real-valued numerical optimization problems. In this paper, a binary version of backtracking algorithm is proposed to deal with 0-1 optimization problems such as feature selection and knapsack problems. Feature selection is the process of selecting a subset of relevant features for use in model construction. Irrelevant features can negatively impact model performances. On the other hand, knapsack problem is a well-known optimization problem used to assess discrete algorithms. The objecti
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Civicioglu, Pinar. "Backtracking Search Optimization Algorithm for numerical optimization problems." Applied Mathematics and Computation 219, no. 15 (2013): 8121–44. http://dx.doi.org/10.1016/j.amc.2013.02.017.

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Wang, Shu, Xinyu Da, Mudong Li, and Tong Han. "Adaptive backtracking search optimization algorithm with pattern search for numerical optimization." Journal of Systems Engineering and Electronics 27, no. 2 (2016): 395–406. http://dx.doi.org/10.1109/jsee.2016.00041.

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Duan, Haibin, and Qinan Luo. "Adaptive Backtracking Search Algorithm for Induction Magnetometer Optimization." IEEE Transactions on Magnetics 50, no. 12 (2014): 1–6. http://dx.doi.org/10.1109/tmag.2014.2342192.

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Wei, Fengtao, Yunpeng Shi, Junyu Li, and Yangyang Zhang. "Multi-strategy synergy-based backtracking search optimization algorithm." Soft Computing 24, no. 19 (2020): 14305–26. http://dx.doi.org/10.1007/s00500-020-05225-8.

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Chen, Debao, Feng Zou, Renquan Lu, and Suwen Li. "Backtracking search optimization algorithm based on knowledge learning." Information Sciences 473 (January 2019): 202–26. http://dx.doi.org/10.1016/j.ins.2018.09.039.

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Guney, Kerim, and Ali Durmus. "Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm." International Journal of Antennas and Propagation 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/713080.

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An evolutionary method based on backtracking search optimization algorithm (BSA) is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the resul
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Pradhan, Moumita, Provas Kumar Roy, and Tandra Pal. "Economic Load Dispatch Using Oppositional Backtracking Search Algorithm." International Journal of Energy Optimization and Engineering 6, no. 2 (2017): 79–97. http://dx.doi.org/10.4018/ijeoe.2017040105.

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In this paper, an oppositional backtracking search algorithm (OBSA) is proposed to solve the large scale economic load dispatch (ELD) problem. The main drawback of the conventional backtracking search algorithm (BSA) is that it produces a local optimal solution rather than the global optimal solution. The proposed OBSA methodology is a highly-constrained optimization problem has to minimize the total generation cost by satisfying several constraints involving load demand, generation limits, prohibited operating zone, ramp rate limits and valve point loading effect. The proposed method is appli
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Dissertations / Theses on the topic "Backtracking search optimization algorithm"

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Sävhammar, Simon. "Tillämpbarheten av Learning Backtracking Search Optimization Algoritmen vid Lösning av Sudoku-problemet." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-14087.

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Den här rapporten undersöker egenskaper hos en algoritm som är baserad på Learning Backtracking Search Optimization Algorithm (LBSA) som introducerades av Chen et. al. (2017). Undersökningen genomfördes genom att tillämpa algoritmen på Sudokuproblemet och jämföra lösningsgraden och diversiteten i den sista populationen med en algoritm som är baserad på Hybrid Genetic Algorithm (HGA) som introducerades av Deng och Li (2011). Resultaten visar att implementationen av den LBSA-baserade algoritmen har en lägre lösningsgrad än den HGA-baserade algoritmen för alla genomförda experiment, men att algor
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Tchvagha, Zeine Ahmed. "Contribution à l’optimisation multi-objectifs sous contraintes : applications à la mécanique des structures." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMIR13/document.

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L’objectif de cette thèse est le développement de méthodes d’optimisation multi-objectif pour la résolution de problèmes de conception des structures mécaniques. En effet, la plupart des problèmes réels dans le domaine de la mécanique des structures ont plusieurs objectifs qui sont souvent antagonistes. Il s’agit, par exemple, de concevoir des structures en optimisant leurs poids, leurs tailles, et leurs coûts de production. Le but des méthodes d’optimisation multi-objectif est la recherche des solutions de compromis entre les objectifs étant donné l’impossibilité de satisfaire tout simultaném
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Rossato, Luciara Vellar. "Otimização de amortecedores de massa sintonizados em estruturas submetidas a um processo estacionário." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/163246.

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Atualmente as estruturas estão sendo avaliadas para um maior número de ações em relação há algumas décadas. Esta melhoria ao longo da fase de concepção é dada devido ao fato de que está se tornando mais competitivo o fornecimento de estruturas leves e esbeltas, sendo solicitados, cada vez mais, projetos com menor custo de implantação. Devido a isto, é necessário avaliar as estruturas não apenas sujeitas a cargas estáticas, mas também a carregamentos dinâmicos. As ações dinâmicas que atuam sobre uma estrutura podem ser muito mais prejudiciais do que as estáticas quando não são bem consideradas
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Sá, Alan Oliveira de. "Localização colaborativa em robótica de enxame." Universidade do Estado do Rio de Janeiro, 2015. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=8895.

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Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro<br>Diversas das possíveis aplicações da robótica de enxame demandam que cada robô seja capaz de estimar a sua posição. A informação de localização dos robôs é necessária, por exemplo, para que cada elemento do enxame possa se posicionar dentro de uma formatura de robôs pré-definida. Da mesma forma, quando os robôs atuam como sensores móveis, a informação de posição é necessária para que seja possível identificar o local dos eventos medidos. Em virtude do tamanho, custo e energia dos dispositivos, bem como limitações impostas pelo ambien
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Malleypally, Vinaya. "Parallelizing Tabu Search Based Optimization Algorithm on GPUs." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7638.

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There are many combinatorial optimization problems such as traveling salesman problem, quadratic-assignment problem, flow shop scheduling, that are computationally intractable. Tabu search based simulated annealing is a stochastic search algorithm that is widely used to solve combinatorial optimization problems. Due to excessive run time, there is a strong demand for a parallel version that can be applied to any problem with minimal modifications. Existing advanced and/or parallel versions of tabu search algorithms are specific to the problem at hand. This leads to a drawback of optimization o
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Bilal, Mohd. "A Heuristic Search Algorithm for Asteroid Tour Missions." Thesis, Luleå tekniska universitet, Rymdteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71361.

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Since the discovery of Ceres, asteroids have been of immense scientific interest and intrigue. They hold answers to many of the fundamental questionsabout the formation and evolution of the Solar System. Therefore, a missionsurveying the asteroid belt with close encounter of carefully chosen asteroidswould be of immense scientific benefit. The trajectory of such an asteroidtour mission needs to be designed such that asteroids of a wide range ofcompositions and sizes are encountered; all with an extremely limited ∆Vbudget.This thesis presents a novel heuristic algorithm to optimize trajectories
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Lianjie, Shen. "Optimization and Search in Model-Based Automotive SW/HW Development." Thesis, Linköpings universitet, Programvara och system, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-105394.

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In this thesis two case studies are performed about solving two design problems we face during the design phase of new Volvo truck. One is to solve the frame packing problem on CAN bus. The other is to solve the LDC allocation problem. Both solutions are targeted to meet as many end-to-end latency requirements as possible. Now the solution is obtained through manually approach and based on the designer experience. But it is still not satisfactory enough. With the development of artificial intelligence method we propose two methods based on genetic algorithm to solve our design problem we face
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Akin, Alper. "Optimum Design Of Reinforced Concrete Plane Frames Using Harmony Search Algorithm." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612293/index.pdf.

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In this thesis, the optimum design algorithm is presented for reinforced concrete special moment frames. The objective function is considered as the total cost of reinforced concrete frame which includes the cost of concrete, formwork and reinforcing steel bars. The cost of any component is inclusive of material, fabrication and labor. The design variables in beams are selected as the width and the depth of beams in each span, the diameter and the number of longitudinal reinforcement bars along the span and supports. In columns the width and the depth of the column section, the number and the
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Cruz, António Manuel Costa. "IMRT beam angle optimization using Tabu search." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/17714.

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Mestrado em Matemática e Aplicações<br>O número de pacientes com cancro continua a crescer no mundo e a Organização Mundial da Saúde considerou mesmo esta como uma das principais ameaças para a saúde e o desenvolvimento humano. Dependendo da localização e das especi cidades do tumor, existem muitos tratamentos que podem ser usados, incluindo cirurgia, quimioterapia, imunoterapia e radioterapia. A Radioterapia de Intensidade Modulada (IMRT | Intensity Modulated Radiation Therapy) é uma das modalidades mais avançadas de radioterapia, onde a otimização pode ter um papel importante no que
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Kim, Jinhyo. "Iterated Grid Search Algorithm on Unimodal Criteria." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/30370.

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The unimodality of a function seems a simple concept. But in the Euclidean space R^m, m=3,4,..., it is not easy to define. We have an easy tool to find the minimum point of a unimodal function. The goal of this project is to formalize and support distinctive strategies that typically guarantee convergence. Support is given both by analytic arguments and simulation study. Application is envisioned in low-dimensional but non-trivial problems. The convergence of the proposed iterated grid search algorithm is presented along with the results of particular application studies. It has been recogn
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Books on the topic "Backtracking search optimization algorithm"

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Geem, Zong Woo. Recent advances in harmony search algorithm. Springer, 2010.

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Kacprzyk, Janusz. Music-Inspired Harmony Search Algorithm: Theory and Applications. Springer Berlin Heidelberg, 2009.

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Virginia, Torczon, and Langley Research Center, eds. A globally convergent augmented Lagrangian pattern search algorithm for optimization with general constraints and simple bounds. National Aeronautics and Space Administration, Langley Research Center, 1998.

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Virginia, Torczon, and Langley Research Center, eds. A globally convergent augmented Lagrangian pattern search algorithm for optimization with general constraints and simple bounds. National Aeronautics and Space Administration, Langley Research Center, 1998.

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Search Algorithm - Essence of Optimization [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.87787.

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Geem, Zong Woo. Recent Advances in Harmony Search Algorithm. Springer, 2011.

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Geem, Zong Woo. Recent Advances in Harmony Search Algorithm. Springer London, Limited, 2010.

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Geem, Zong Woo. Recent Advances in Harmony Search Algorithm. Springer Berlin / Heidelberg, 2012.

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Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms. Packt Publishing, 2021.

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Vose, Michael D. The Simple Genetic Algorithm. The MIT Press, 1999. http://dx.doi.org/10.7551/mitpress/6229.001.0001.

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The Simple Genetic Algorithm (SGA) is a classical form of genetic search. Viewing the SGA as a mathematical object, Michael D. Vose provides an introduction to what is known (i.e., proven) about the theory of the SGA. He also makes available algorithms for the computation of mathematical objects related to the SGA. Although he describes the SGA in terms of heuristic search, the book is not about search or optimization per se. Rather, the focus is on the SGA as an evolutionary system. The author intends the book also to serve as an outline for exploring topics in mathematics and computer scienc
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Book chapters on the topic "Backtracking search optimization algorithm"

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Hassan, Bryar A., Alla A. Hassan, Aram M. Ahmed, Tarik A. Rashid, and Abdulhady A. Abdulla. "Backtracking Search Optimization Algorithm." In Multi-objective Optimization Techniques. CRC Press, 2025. https://doi.org/10.1201/9781003601555-7.

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Gosain, Anjana, and Kavita Sachdeva. "Materialized View Selection Using Backtracking Search Optimization Algorithm." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7566-7_25.

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Zhao, Wenting, Lijin Wang, Bingqing Wang, and Yilong Yin. "Best Guided Backtracking Search Algorithm for Numerical Optimization Problems." In Knowledge Science, Engineering and Management. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47650-6_33.

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Zhao, Wenting, Lijin Wang, Yilong Yin, Bingqing Wang, Yi Wei, and Yushan Yin. "An Improved Backtracking Search Algorithm for Constrained Optimization Problems." In Knowledge Science, Engineering and Management. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12096-6_20.

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Xu, Qingzheng, Lemeng Guo, Na Wang, and Li Xu. "Opposition-Based Backtracking Search Algorithm for Numerical Optimization Problems." In Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23862-3_22.

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Polvirat, Patipan. "HLBSA: Hierarchical Learning Backtracking Search Algorithm for Global Optimization." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90295-6_10.

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Sriram, Mounika, and K. Ravindra. "Backtracking Search Optimization Algorithm Based MPPT Technique for Solar PV System." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24318-0_59.

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de Sá, Alan Oliveira, Nadia Nedjah, and Luiza de Macedo Mourelle. "Genetic and Backtracking Search Optimization Algorithms Applied to Localization Problems." In Computational Science and Its Applications – ICCSA 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09156-3_51.

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Zabinsky, Zelda B. "Backtracking Adaptive Search." In Nonconvex Optimization and Its Applications. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9182-9_5.

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Zolghadr-Asli, Babak. "Cuckoo Search Algorithm." In Computational Intelligence-based Optimization Algorithms. CRC Press, 2023. http://dx.doi.org/10.1201/9781003424765-13.

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Conference papers on the topic "Backtracking search optimization algorithm"

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Din, Khadijetou Maaloum, Fatima Daqaq, and Rachid Ellaia. "Backtracking Search Algorithm with Lemur Optimizer for Numerical and Global Optimization." In 2024 10th International Conference on Optimization and Applications (ICOA). IEEE, 2024. http://dx.doi.org/10.1109/icoa62581.2024.10754131.

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Chew, J. J., Nik Noordini Nik Abd Malik, Najla Ilyana Ab Majid, and Rashidah Siti Saedah binti Arsat. "Circular Virtual Array Beamforming in Wireless Sensor Network Using Backtracking Search Algorithm." In 2024 IEEE International Conference on Advanced Telecommunication and Networking Technologies (ATNT). IEEE, 2024. http://dx.doi.org/10.1109/atnt61688.2024.10719193.

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Peng, Lei. "A backtracking-based combinatorial optimization algorithm for target matching in account reconciliation." In International Conference on Mechatronics and Intelligent Control (ICMIC 2024), edited by Kun Zhang and Pascal Lorenz. SPIE, 2025. https://doi.org/10.1117/12.3052864.

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Coggi, Mirko, Antonio Sgarlata, Guido Walter Di Donato, and Marco Domenico Santambrogio. "On the optimization of GWFA algorithm: enabling real-case applications supporting alignment backtracking." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10781891.

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Xiang, Jiang, Zhezhuang Xu, Jinlong Wang, et al. "Optimization of Crane Scheduling Time for Steel Plate Outbound Operations Based on Backtracking Algorithm." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865238.

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Passos, Leandro Aparecido, Douglas Rodrigues, and Joao Paulo Papa. "Quaternion-Based Backtracking Search Optimization Algorithm." In 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. http://dx.doi.org/10.1109/cec.2019.8790209.

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El Maani, Rabii, Ahmed Tchvagha Zeine, Bouchaib Radi, Abdelkhalak El Hami, and Rachid Ellaia. "Backtracking search optimization algorithm for fluid-structure interaction problems." In 2016 4th IEEE International Colloquium on Information Science and Technology (CIST). IEEE, 2016. http://dx.doi.org/10.1109/cist.2016.7804975.

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Khan, Saad Saleem, Muhammad Awais Rafiq, Hussain Shareef, and Muhammad Khurram Sultan. "Parameter optimization of PEMFC model using backtracking search algorithm." In 2018 5th International Conference on Renewable Energy: Generation and Applications (ICREGA). IEEE, 2018. http://dx.doi.org/10.1109/icrega.2018.8337625.

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Barkouki, Bouthaina El, Mohamed Laamim, Mohammed Ouassaid, and Aboubakr BENAZZOuz. "Voltage Stability in DC Microgrid Using Backtracking Search Algorithm PI Controllers." In 2023 9th International Conference on Optimization and Applications (ICOA). IEEE, 2023. http://dx.doi.org/10.1109/icoa58279.2023.10308845.

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Pain, Santigopal, and Parimal Acharjee. "AGC of practical power system using backtracking search optimization algorithm." In 2016 International Conference and Exposition on Electrical and Power Engineering (EPE). IEEE, 2016. http://dx.doi.org/10.1109/icepe.2016.7781426.

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Reports on the topic "Backtracking search optimization algorithm"

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Homaifar, Abdollah, Albert Esterline, and Bahram Kimiaghalam. Hybrid Projected Gradient-Evolutionary Search Algorithm for Mixed Integer Nonlinear Optimization Problems. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada455904.

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Abramson, Mark A. Mixed Variable Optimization of a Load-Bearing Thermal Insulation System Using a Filter Pattern Search Algorithm. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada451457.

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Radvand, Tina, and Alireza Talebpour. A Quantum Optimization Algorithm for Optimal Electric Vehicle Charging Station Placement for Intercity Trips. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-028.

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Electric vehicles (EVs) play a significant role in enhancing the sustainability of transportation systems. However, their widespread adoption is hindered by inadequate public charging infrastructure, particularly to support long-distance travel. Identifying optimal charging station locations in large transportation networks presents a well-known NP-hard combinatorial optimization problem, as the search space grows exponentially with the number of potential charging station locations. This report introduces a quantum search-based optimization algorithm designed to enhance the efficiency of solv
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Lewis, Robert Michael, Virginia Joanne Torczon, and Tamara Gibson Kolda. A generating set direct search augmented Lagrangian algorithm for optimization with a combination of general and linear constraints. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/893121.

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Wenren, Yonghu, Joon Lim, Luke Allen, Robert Haehnel, and Ian Dettwiler. Helicopter rotor blade planform optimization using parametric design and multi-objective genetic algorithm. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/46261.

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In this paper, an automated framework is presented to perform helicopter rotor blade planform optimization. This framework contains three elements, Dakota, ParBlade, and RCAS. These elements are integrated into an environment control tool, Galaxy Simulation Builder, which is used to carry out the optimization. The main objective of this work is to conduct rotor performance design optimizations for forward flight and hover. The blade design variables manipulated by ParBlade are twist, sweep, and anhedral. The multi-objective genetic algorithm method is used in this study to search for the optim
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Qi, Fei, Zhaohui Xia, Gaoyang Tang, et al. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate th
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