Relevant bibliographies by topics / Backtracking search optimization algorithm

Contents

  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: 1 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Backtracking search optimization algorithm.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Backtracking search optimization algorithm"

1

Civicioglu, Pinar. "Backtracking Search Optimization Algorithm for numerical optimization problems." Applied Mathematics and Computation 219, no. 15 (April 2013): 8121–44. http://dx.doi.org/10.1016/j.amc.2013.02.017.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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 widely used in the literature of mechanical design optimization are used to evaluate the performance of the presented algorithm. Results show that the proposed algorithm is effective and efficient for solving the mechanical design optimization problems considered.
APA, Harvard, Vancouver, ISO, and other styles
3

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 (April 2021): 1–15. http://dx.doi.org/10.4018/ijaec.2021040101.

Full text
Abstract:
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 objective of this research is to evaluate the discrete version of backtracking algorithm on the two mentioned problems and compare obtained results with other binary optimization algorithms using four usual classifiers: logistic regression, decision tree, random forest, and support vector machine. Empirical study on biological microarray data and experiments on 0-1 knapsack problems show the effectiveness of the binary algorithm and its ability to achieve good quality solutions for both problems.
APA, Harvard, Vancouver, ISO, and other styles
4

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 (April 20, 2016): 395–406. http://dx.doi.org/10.1109/jsee.2016.00041.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Wei, Fengtao, Yunpeng Shi, Junyu Li, and Yangyang Zhang. "Multi-strategy synergy-based backtracking search optimization algorithm." Soft Computing 24, no. 19 (August 5, 2020): 14305–26. http://dx.doi.org/10.1007/s00500-020-05225-8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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 results of the following seventeen algorithms: particle swarm optimization (PSO), genetic algorithm (GA), modified touring ant colony algorithm (MTACO), quadratic programming method (QPM), bacterial foraging algorithm (BFA), bees algorithm (BA), clonal selection algorithm (CLONALG), plant growth simulation algorithm (PGSA), tabu search algorithm (TSA), memetic algorithm (MA), nondominated sorting GA-2 (NSGA-2), multiobjective differential evolution (MODE), decomposition with differential evolution (MOEA/D-DE), comprehensive learning PSO (CLPSO), harmony search algorithm (HSA), seeker optimization algorithm (SOA), and mean variance mapping optimization (MVMO). The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels.
APA, Harvard, Vancouver, ISO, and other styles
9

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 (April 2017): 79–97. http://dx.doi.org/10.4018/ijeoe.2017040105.

Full text
Abstract:
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 applied for three test systems and provides the unique and fast solutions. The new heuristic OBSA approach is successfully applied in three test systems consisting of 13 and 140 thermal generators. The test results are judged against various methods. The simulation results show the effectiveness and accuracy of the proposed OBSA algorithm over other methods like conventional BSA, oppositional invasive weed optimization (OIWO), Shuffled differential evolution (SDE) and oppositional real coded chemical reaction optimization (ORCCRO). This clearly suggests that the new OBSA method can achieve effective and feasible solutions of nonlinear ELD problems.
APA, Harvard, Vancouver, ISO, and other styles
10

Li, Zheng, Zhongbo Hu, Yongfei Miao, Zenggang Xiong, Xinlin Xu, and Canyun Dai. "Deep-Mining Backtracking Search Optimization Algorithm Guided by Collective Wisdom." Mathematical Problems in Engineering 2019 (December 26, 2019): 1–30. http://dx.doi.org/10.1155/2019/2540102.

Full text
Abstract:
The backtracking search optimization algorithm (BSA) is a recently proposed evolutionary algorithm with simple structure and well global exploration capability, which has been widely used to solve optimization problems. However, the exploitation capability of the BSA is poor. This paper proposes a deep-mining backtracking search optimization algorithm guided by collective wisdom (MBSAgC) to improve its performance. The proposed algorithm develops two learning mechanisms, i.e., a novel topological opposition-based learning operator and a linear combination strategy, by deeply mining the winner-tendency of collective wisdom. The topological opposition-based learning operator guides MBSAgC to search the vertices in a hypercube about the best individual. The linear combination strategy contains a difference vector guiding individuals learning from the best individual. In addition, in order to balance the overall performance, MBSAgC simulates the clusterity-tendency strategy of collective wisdom to develop another difference vector in the above linear combination strategy. The vector guides individuals to learn from the mean value of the current generation. The performance of MBSAgC is tested on CEC2005 benchmark functions (including 10-dimension and 30-dimension), CEC2014 benchmark functions, and a test suite composed of five engineering design problems. The experimental results of MBSAgC are very competitive compared with those of the original BSA and state-of-the-art algorithms.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Backtracking search optimization algorithm"

1

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.

Full text
Abstract:
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 algoritmen håller en högre diversitet i den sista populationen för tre av de fem gjorda experimenten. Slutsatsen är att den LBSA-baserade algoritmen inte är lämplig för att lösa Sudokuproblemet på grund av en låg lösningsgrad och att implementationen har en hög komplexitet.
This report examines the properties of an algorithm based on the Learning Backtracking Optimization Algorithm (LBSA) introduced by Chen et. al. (2017). The examination was performed by applying the algorithm on the Sudoku problem and then comparing the solution rate and the diversity in the final population with an algorithm based on the Hybrid Genetic Algorithm introduced by Deng and Li (2011). The results show the implementation of the LBSA based algorithm have a lower solution rate than the HGA based algorithm for all executed experiments. But the LBSA based algorithm manage to keep a higher diversity in the final population in three of the five performed experiments. The conclusion is that the LBSA based algorithm is not suitable for solving the Sudoku problem since the algorithm has a lower solution rate and the implementation have a high complexity.
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:
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 simultanément. Les métaheuristiques sont des méthodes d’optimisation capables de résoudre les problèmes d’optimisation multi-objective en un temps de calcul raisonnable sans garantie de l’optimalité de (s) solution (s). Au cours des dernières années, ces algorithmes ont été appliqués avec succès pour résoudre le problème des mécaniques des structures. Dans cette thèse deux métaheuristiques ont été développées pour la résolution des problèmes d’optimisation multi-objectif en général et de conception de structures mécaniques en particulier. Le premier algorithme baptisé MOBSA utilise les opérateurs de croisement et de mutation de l’algorithme BSA. Le deuxième algorithme nommé NNIA+X est une hybridation d’un algorithme immunitaire et de trois croisements inspirés de l’opérateur de croisement original de l’algorithme BSA. Pour évaluer l’efficacité et l’efficience de ces deux algorithmes, des tests sur quelques problèmes dans littérature ont été réalisés avec une comparaison avec des algorithmes bien connus dans le domaine de l’optimisation multi-objectif. Les résultats de comparaison en utilisant des métriques très utilisées dans la littérature ont démontré que ces deux algorithmes peuvent concurrencer leurs prédécesseurs
The objective of this thesis is the development of multi-objective optimization methods for solving mechanical design problems. Indeed, most of the real problems in the field of mechanical structures have several objectives that are often antagonistic. For example, it is about designing structures by optimizing their weight, their size, and their production costs. The goal of multi-objective optimization methods is the search for compromise solutions between objectives given the impossibility to satisfy all simultaneously. Metaheuristics are optimization methods capable of solving multi-objective optimization problems in a reasonable calculation time without guaranteeing the optimality of the solution (s). In recent years, these algorithms have been successfully applied to solve the problem of structural mechanics. In this thesis, two metaheuristics have been developed for the resolution of multi-objective optimization problems in general and of mechanical structures design in particular. The first algorithm called MOBSA used the crossover and mutation operators of the BSA algorithm. The second one named NNIA+X is a hybridization of an immune algorithm and three crossover inspired by the original crossover operator of the BSA algorithm. To evaluate the effectiveness and efficiency of these two algorithms, tests on some problems in literature have been made with a comparison with algorithms well known in the field of multi-objective optimization. The comparison results using metrics widely used in the literature have shown that our two algorithms can compete with their predecessors
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
Abstract:
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 e dimensionadas. Ações dinâmicas podem ser provenientes de tremores de terra, vento, equipamentos em funcionamento, deslocamento de pessoas, veículos em movimento, motores desbalanceados, entre outras fontes, o que pode causar vibrações na estrutura, podendo levar a mesma ao colapso. A fim de controlar e reduzir as amplitudes de vibração, entre outras alternativas é possível a instalação de amortecedores de massa sintonizado (AMS), que é um dispositivo de controle passivo. O AMS tem várias vantagens, tais como a grande capacidade de reduzir a amplitude de vibração, fácil instalação, baixa manutenção, baixo custo, entre outras. Para se obter a melhor relação custo-benefício, ou seja, a maior redução de amplitude aliada a um menor número de amortecedores ou a uma menor massa, a otimização dos parâmetros do AMS tornase fundamental. Neste contexto, este trabalho visa, através de simulação numérica, propor um método para otimizar parâmetros de AMSs quando estes devem ser instalados em edifícios submetidos à excitação sísmica. Inicialmente é considerado apenas um único AMS instalado no topo do edifício e em seguida também são feitas simulações com múltiplos AMSs (MAMS), e por fim são descartados os AMSs desnecessários, obtendo assim a melhor resposta da estrutura. Para tanto, uma rotina computacional é desenvolvida em MatLab usando o método de integração direta das equações de movimento de Newmark para determinar a resposta dinâmica da estrutura. Para fins de análise podem ser considerados tanto sismos reais quanto artificiais. Os acelerogramas artificias são gerados a partir do espectro proposto por Kanai e Tajimi. Primeiramente, a estrutura é analisada somente com o seu amortecimento próprio para fins comparativos e de referência. Em seguida, a otimização do ou dos AMSs é feita, na qual a função objetivo é minimizar o deslocamento máximo no topo do edifício, e as variáveis de projeto, são a relação de massas (AMS - Estrutura), rigidez e amortecimento do ou dos AMSs. Para a otimização são utilizados os algoritmos Firefly Algotithm e Backtracking Search Optimization Algorithm. De acordo com as configurações do AMS, após a otimização dos seus parâmetros são determinadas as novas respostas dinâmicas da estrutura. Finalmente, pode-se observar que o método proposto foi capaz de otimizar os parâmetros do ou dos AMSs, reduzindo consideravelmente as respostas da estrutura após a instalação do mesmo, minimizando o risco de dano e colapso do edifício. Desta forma, este trabalho mostra que é possível projetar AMS e MAMS de forma econômica e eficaz.
Currently, structures are being evaluated for a greater number of actions when compared to a few decades ago. This improvement in designing stage is happening because projects providing lightweight and slender structures, with lower implantation costs, are being more requested. Thus, evaluating structures not only subjected to static loads, but also to dynamic loads has become necessary. Dynamic loads acting on a structure are more damaging than static loads, if they are not well considered and dimensioned. Dynamic loads could occur from earthquakes, wind, equipment, movement of people or vehicles, among other sources, which cause vibrations in structures and may lead to a collapse. Tuned mass damper (TMD), a passive control device, can be installed as an alternative to reduce vibration amplitudes. TMD has several advantages, such as large capacity to reduce amplitude of vibration, easy installation, low maintenance, low cost, among others. Optimizing TMD parameters is fundamental for obtaining best cost-benefit relation, i.e., greater amplitude reduction along with lower number of dampers or lower mass. In this context, this study aims at proposing, through numerical simulation, a method for optimizing TMD parameters when installing them on buildings under seismic excitation. Initially, a single-TMD case is considered, then simulations with multiple-TMDs (MTMDs) are run; lastly, unnecessary TMDs are discarded, obtaining the best structural response. For this purpose, a computational routine is developed on MatLab using Newmark direct integration method for equations of motion to determine the dynamic structural response. Both real and artificial earthquakes are considered for purposes of analysis. Artificial accelerograms are generated from proposed Kanai-Tajimi spectrum. First, structure is analyzed only with its own damping for comparison and reference. Second, a single or multiple-TMD optimization is carried out, in which the objective function is to minimize the maximum displacement at the top of the building, and the design variables are modal mass ratio (Structure-TMD), stiffness and damping of a single or multiple-TMD. Firefly and Backtracking Optimization algorithms are used for optimization. According to TMD settings, new dynamic structural responses are determined after optimizing parameters. Finally, the proposed method could optimize parameters of single or multiple-TMDs, considerably reducing structural responses after their installation, minimizing the risk of damage and building collapse. Thus, this study shows the possibility of designing TMDs or MTMDs both economically and effectively.
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro
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 ambiente de operação, a solução mais evidente, i.e. utilizar um Sistema de Posicionamento Global (GPS), torna-se muitas vezes inviável. O método proposto neste trabalho permite que as posições absolutas de um conjunto de nós desconhecidos sejam estimadas, com base nas coordenadas de um conjunto de nós de referência e nas medidas de distância tomadas entre os nós da rede. A solução é obtida por meio de uma estratégia de processamento distribuído, onde cada nó desconhecido estima sua própria posição e ajuda os seus vizinhos a calcular as suas respectivas coordenadas. A solução conta com um novo método denominado Multi-hop Collaborative Min-Max Localization (MCMM), ora proposto com o objetivo de melhorar a qualidade da posição inicial dos nós desconhecidos em caso de falhas durante o reconhecimento dos nós de referência. O refinamento das posições é feito com base nos algoritmos de busca por retrocesso (BSA) e de otimização por enxame de partículas (PSO), cujos desempenhos são comparados. Para compor a função objetivo, é introduzido um novo método para o cálculo do fator de confiança dos nós da rede, o Fator de Confiança pela Área Min-Max (MMA-CF), o qual é comparado com o Fator de Confiança por Saltos às Referências (HTA-CF), previamente existente. Com base no método de localização proposto, foram desenvolvidos quatro algoritmos, os quais são avaliados por meio de simulações realizadas no MATLABr e experimentos conduzidos em enxames de robôs do tipo Kilobot. O desempenho dos algoritmos é avaliado em problemas com diferentes topologias, quantidades de nós e proporção de nós de referência. O desempenho dos algoritmos é também comparado com o de outros algoritmos de localização, tendo apresentado resultados 40% a 51% melhores. Os resultados das simulações e dos experimentos demonstram a eficácia do método proposto.
Many applications of Swarm Robotic Systems (SRSs) require that a robot is able to discover its position. The location information of the robots is required, for example, to allow them to be correctly positioned within a predefined swarm formation. Similarly, when the robots act as mobile sensors, the position information is needed to allow the identification of the location of the measured events. Due to the size, cost and energy source restrictions of these devices, or even limitations imposed by the operating environment, the straightforward solution, i.e. the use of a Global Positioning System (GPS), is often not feasible. The method proposed in this work allows the estimation of the absolute positions of a set of unknown nodes, based on the coordinates of a set of reference nodes and the distances measured between nodes. The solution is achieved by means of a distributed processing strategy, where each unknown node estimates its own position and helps its neighbors to compute their respective coordinates. The solution makes use of a new method called Multi-hop Collaborative Min-Max Localization (MCMM), herein proposed, aiming to improve the quality of the initial positions estimated by the unknown nodes in case of failure during the recognition of the reference nodes. The positions refinement is achieved based on the Backtracking Search Optimization Algorithm (BSA) and the Particle Swarm Optimization (PSO), whose performances are compared. To compose the objective function, a new method to compute the confidence factor of the network nodes is introduced, the Min-max Area Confidence Factor (MMA-CF), which is compared with the existing Hops to Anchor Confidence Factor (HTA-CF). Based on the proposed localization method, four algorithms were developed and further evaluated through a set of simulations in MATLABr and experiments in swarms of type Kilobot robots. The performance of the algorithms is evaluated on problems with different topologies, quantities of nodes and proportion of reference nodes. The performance of the algorithms is also compared with the performance of other localization algorithms, showing improvements between 40% to 51%. The simulations and experiments outcomes demonstrate the effectiveness of the proposed method.
APA, Harvard, Vancouver, ISO, and other styles
5

Malleypally, Vinaya. "Parallelizing Tabu Search Based Optimization Algorithm on GPUs." Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7638.

Full text
Abstract:
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 only for that particular problem. In this work, we propose a parallel version of tabu search based SA on the Graphics Processing Unit (GPU) platform. We propose two variants of the algorithm based on where the tabu list is stored (global vs. local). In the first version, the list is stored in the global shared memory such that all threads can access this list. Multiple random walks in solution space are carried out. Each walk avoids the moves made in rest of the walks due to their access to global tabu list at the expense of more time. In the second version, the list is stored at the block level and is shared by only the block threads. Groups of random walks are performed in parallel and a walk in a group avoids the moves made by the rest of the walks within that group due to their access to shared local tabu list. This version is better than the first version in terms of execution time. On the other hand, the first version finds the global optima more often. We present experimental results for six difficult optimization functions with known global optima. Compared to the CPU implementation with similar workload, the proposed GPU versions are faster by approximately three orders of magnitude and often find the global optima.
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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 trajectoriesfor an asteroid tour mission with close range flybys (≤ 1000 km). The coresearch algorithm efficiently decouples combinatorial (i.e. choosing the asteroids to flyby)and continuous optimization (i.e. optimizing critical maneuversand events) of what is essentially a mixed integer programming problem.Additionally, different methods to generate a healthy initial population forthe combinatorial optimization are presented.The algorithm is used to generate a set of 1800 feasible trajectories withina 2029+ launch frame. A statistical analysis of these set of trajectories isperformed and important metrics for the search are set based on the statistics.Trajectories allowing flybys to prominent families of asteroids like Flora andNysa with ∆V as low as 4.99 km/s are obtained.Two modified implementations of the algorithm are presented. In a firstiteration, a large sample of trajectories is generated with a limited numberof encounters to the most scientifically interesting targets. While, a posteriori, trajectories are filled in with as many small targets as possible. Thisis achieved in two different ways, namely single step extension and multiplestep extension. The former fills in the trajectories with small targets in onestep, while the latter optimizes the trajectory by filling in with one asteroid per step. The thesis also presents detection of asteroids for successfullyperforming flybys. A photometric filter is developed which prunes out badlyilluminated asteroids. The best trajectory is found to perform well againstthis filter such that nine out of the ten planned flybys are feasible.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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 today. In first case study about frame packing we perform one single genetic algorithm process to find the optimal solution. In second case study about LDC allocation we proposed how to handle two genetic algorithm processes together to reach the optimal solution. In this thesis we show the feasibility of adopting artificial intelligence concept in some activities of the truck design phases like we do in both case studies.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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 diameter of bars in x and y directions are selected as design variables. The column section database is prepared which includes the width and height of column section, the diameter and the number of reinforcing bars in the column section is constructed. This database is used by the design algorithm to select appropriate sections for the columns of the frame under consideration. The design constraints are implemented from ACI 318-05 which covers the flexural and shear strength, serviceability, the minimum and maximum steel percentage for flexural and shear reinforcement, the spacing requirements for the reinforcing bars and the upper and lower bound requirements for the concrete sections. The optimum design problem formulated according to ACI 318-05 provisions with the design variables mentioned above turns out to be a combinatorial optimization problem. The solution of the design problem is obtained by using the harmony search algorithm (HS) which is one of the recent additions to meta-heuristic optimization techniques which are widely used in obtaining the solution of combinatorial optimization problems. The HS algorithm is quite simple and has few parameters to initialize and consists of simple steps which make it easy to implement. Number of design examples is presented to demonstrate the efficiency and robustness of the optimum design algorithm developed.
APA, Harvard, Vancouver, ISO, and other styles
9

Cruz, António Manuel Costa. "IMRT beam angle optimization using Tabu search." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/17714.

Full text
Abstract:
Mestrado em Matemática e Aplicações
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 diz respeito à qualidade do tratamento aplicado. Em IMRT, o feixe de radiação pode ser visto como se fosse constituído por vários pequenos feixes, pelo uso de um colimador multifolhas, que permite que a intensidade seja modulada. Este complexo problema de otimização pode ser dividido em três subproblemas, que estão relacionados entre si e que podem ser resolvidos sequencialmente. Para cada paciente, os ângulos de onde a radiação ir a ocorrer têm de ser determinados (problema geométrico | otimização angular). Depois, para cada um desses ângulos, o mapa de intensidades (ou fluências) tem de ser calculado (problema das intensidades | otimização das fluências). Finalmente, e necessário determinar o comportamento do colimador multifolhas, de forma a garantir que as intensidades são, de facto, atribuídas (problema de realiza ção). Em cada um destes problemas de otimização, a qualidade do tratamento atribuído depende dos modelos e algoritmos usados. Neste trabalho, a nossa atenção estará particularmente focada na otimização angular, um problema conhecido por ser altamente não-convexo, com muitos mínimos locais e com uma função objetivo que requer muito tempo de computação para ser calculada. Tal significa, respetivamente, que os algoritmos que sejam baseados no cálculo de gradientes ou que requeiram muitas avaliações da função objetivo podem não ser adequados. Assim, os procedimentos metaheurísticos podem ser uma boa alternativa para abordar este problema, visto que são capazes de escapar de mínimos locais e são conhecidos por conseguirem calcular boas soluções em problemas complexos. Neste trabalho ser a descrita uma aplicação para Pesquisa Tabu. Serão ainda apresentados os testes computacionais realizados, considerando dez casos clínicos de pacientes previamente tratados por radioterapia, pretendendo-se mostrar que a Pesquisa Tabu e capaz de melhorar os resultados obtidos através da solução equidistante, cujo uso e comum na prática clínica.
The number of cancer patients continues to grow worldwide and the World Health Organization has even considered cancer as one of the main threats to human health and development. Depending on the location and speci cities of the tumor, there are many treatments that can be used, including surgery, chemotherapy, immunotherapy and radiation therapy. Intensity Modulated Radiation Therapy (IMRT) is one of the most advanced radiation therapy modalities, and optimization can have a key role in the quality of the treatment delivered. In IMRT, the radiation beam can be thought of as being composed by several small beams, through the use of a multileaf collimator, allowing radiation intensity to be modulated. This complex optimization problem can be divided in three related subproblems that can be solved sequentially. For each patient, the angles from which the radiation will be delivered have to be determined (geometric problem | beam angle optimization). Then, for each of these angles, the radiation intensity map is calculated ( uence or intensity optimization). Finally, it is necessary to determine the behavior of the multileaf collimator that guarantees that the desired radiation intensities are, indeed, delivered (realization problem). In each of these optimization problems, the quality of the treatment delivered depends on the models and algorithms used. In this work the attention will be focused in beam angle optimization, a problem known to be highly non{convex, with many local minima and with an objective function that is time expensive to calculate, which, respectively, means that algorithms that are gradient{based or that require many objective function evaluations will not be adequate. Metaheuristics can be the right tool to tackle this problem, since they are capable of escaping local minima and are known to be able to calculate good solutions for complex problems. In this work, an application of Tabu Search to beam angle optimization is described. Computational results considering ten clinical cases of head{and{neck cancer patients are presented, showing that Tabu Search is capable of improving the equidistant solution usually used in clinical practice.
APA, Harvard, Vancouver, ISO, and other styles
10

Kim, Jinhyo. "Iterated Grid Search Algorithm on Unimodal Criteria." Diss., Virginia Tech, 1997. http://hdl.handle.net/10919/30370.

Full text
Abstract:
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 recognized that the derivative methods, such as the Newton-type method, are not entirely satisfactory, so a variety of other tools are being considered as alternatives. Many other tools have been rejected because of apparent manipulative difficulties. But in our current research, we focus on the simple algorithm and the guaranteed convergence for unimodal function to avoid the possible chaotic behavior of the function. Furthermore, in case the loss function to be optimized is not unimodal, we suggest a weaker condition: almost (noisy) unimodality, under which the iterated grid search finds an estimated optimum point.
Ph. D.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Backtracking search optimization algorithm"

1

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

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

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Levitin, Anany, and Maria Levitin. Algorithmic Puzzles. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780199740444.001.0001.

Full text
Abstract:
While many think of algorithms as specific to computer science, at its core algorithmic thinking is defined by the use of analytical logic to solve problems. This logic extends far beyond the realm of computer science and into the wide and entertaining world of puzzles. In Algorithmic Puzzles, Anany and Maria Levitin use many classic brainteasers as well as newer examples from job interviews with major corporations to show readers how to apply analytical thinking to solve puzzles requiring well-defined procedures. The book's unique collection of puzzles is supplemented with carefully developed tutorials on algorithm design strategies and analysis techniques intended to walk the reader step-by-step through the various approaches to algorithmic problem solving. Mastery of these strategies--exhaustive search, backtracking, and divide-and-conquer, among others--will aid the reader in solving not only the puzzles contained in this book, but also others encountered in interviews, puzzle collections, and throughout everyday life. Each of the 150 puzzles contains hints and solutions, along with commentary on the puzzle's origins and solution methods. The only book of its kind, Algorithmic Puzzles houses puzzles for all skill levels. Readers with only middle school mathematics will develop their algorithmic problem-solving skills through puzzles at the elementary level, while seasoned puzzle solvers will enjoy the challenge of thinking through more difficult puzzles.
APA, Harvard, Vancouver, ISO, and other styles
8

Bäck, Thomas. Evolutionary Algorithms in Theory and Practice. Oxford University Press, 1996. http://dx.doi.org/10.1093/oso/9780195099713.001.0001.

Full text
Abstract:
This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Backtracking search optimization algorithm"

1

Gosain, Anjana, and Kavita Sachdeva. "Materialized View Selection Using Backtracking Search Optimization Algorithm." In Advances in Intelligent Systems and Computing, 241–51. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7566-7_25.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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, 223–34. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23862-3_22.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Zhao, Wenting, Lijin Wang, Bingqing Wang, and Yilong Yin. "Best Guided Backtracking Search Algorithm for Numerical Optimization Problems." In Knowledge Science, Engineering and Management, 414–25. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47650-6_33.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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, 222–33. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12096-6_20.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Sriram, Mounika, and K. Ravindra. "Backtracking Search Optimization Algorithm Based MPPT Technique for Solar PV System." In Learning and Analytics in Intelligent Systems, 498–506. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24318-0_59.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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, 738–46. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09156-3_51.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Zabinsky, Zelda B. "Backtracking Adaptive Search." In Nonconvex Optimization and Its Applications, 105–28. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9182-9_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Das, Subhankar. "Search Engine Algorithm and Search Engine Marketing." In Search Engine Optimization and Marketing, 117–80. First edition. | Boca Raton : CRC Press, 2021.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429298509-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Du, Ke-Lin, and M. N. S. Swamy. "Bacterial Foraging Algorithm." In Search and Optimization by Metaheuristics, 217–25. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Zolghadr-Asli, Babak, Omid Bozorg-Haddad, and Xuefeng Chu. "Crow Search Algorithm (CSA)." In Advanced Optimization by Nature-Inspired Algorithms, 143–49. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5221-7_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Backtracking search optimization algorithm"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Jia, Dongbao, Yining Tong, Yang Yu, Zonghui Cai, and Shangce Gao. "A Novel Backtracking Search with Grey Wolf Algorithm for Optimization." In 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2018. http://dx.doi.org/10.1109/ihmsc.2018.00024.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Wu, Shihong, Zhigang Wang, and Darong Ling. "Echo State Network prediction based on Backtracking Search optimization Algorithm." In 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2019. http://dx.doi.org/10.1109/itnec.2019.8729414.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kolawole, Soyinka Olukunle, and Haibin Duan. "Backtracking search algorithm for non-aligned thrust optimization for satellite formation." In 2014 11th IEEE International Conference on Control & Automation (ICCA). IEEE, 2014. http://dx.doi.org/10.1109/icca.2014.6871013.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Fengtao, Wei, Zhang Yangyang, Shi Yunpeng, and LI Junyu. "Research on Optimization Method of Facilities Arrangement Based on Backtracking Search Algorithm." In Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/pntim-19.2019.8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Elomary, Imad, Ahmed Abbou, and Lhassane Idoumghar. "Backtracking Search Algorithm Optimization for the Brushless Direct Current (BLDC) Motor Parameter Design." In 2017 International Renewable and Sustainable Energy Conference (IRSEC). IEEE, 2017. http://dx.doi.org/10.1109/irsec.2017.8477573.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Gunen, Mehmet Akif, Umit Haluk Atasever, and Erkan Besdok. "A novel edge detection approach based on backtracking search optimization algorithm (BSA) clustering." In 2017 8th International Conference on Information Technology (ICIT). IEEE, 2017. http://dx.doi.org/10.1109/icitech.2017.8079987.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Backtracking search optimization algorithm"

1

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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), August 2006. http://dx.doi.org/10.2172/893121.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin, and Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

Full text
Abstract:
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 the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Backtracking search optimization algorithm' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography