Relevant bibliographies by topics / OPTIMIZER 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 'OPTIMIZER ALGORITHM'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "OPTIMIZER ALGORITHM"

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Mehta, Pranav, Betul Sultan Yildiz, Sadiq M. Sait, and Ali Riza Yildiz. "Hunger games search algorithm for global optimization of engineering design problems." Materials Testing 64, no. 4 (April 1, 2022): 524–32. http://dx.doi.org/10.1515/mt-2022-0013.

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Abstract The modernization in automobile industries has been booming in recent times, which has led to the development of lightweight and fuel-efficient design of different automobile components. Furthermore, metaheuristic algorithms play a significant role in obtaining superior optimized designs for different vehicle components. Hence, a hunger game search (HGS) algorithm is applied to optimize the automobile suspension arm (SA) by reduction of mass vis-à-vis volume. The performance of the HGS algorithm was accomplished by comparing the achieved results with the well-established metaheuristics (MHs), such as salp swarm optimizer, equilibrium optimizer, Harris Hawks optimizer (HHO), chaotic HHO, slime mould optimizer, marine predator optimizer, artificial bee colony optimizer, ant lion optimizer, and it was found that the HGS algorithm is able to pursue the best optimized solution subjecting to critical constraints. Moreover, the HGS algorithm can realize the least weight of the SA subjected to maximum stress values. Hence, the adopted algorithm can be found robust in terms of obtaining the best global optimum solution.
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Abdel-Basset, Mohamed, Reda Mohamed, Karam M. Sallam, and Ripon K. Chakrabortty. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm." Mathematics 10, no. 19 (September 23, 2022): 3466. http://dx.doi.org/10.3390/math10193466.

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This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge–Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm.
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Khan, Muhammad Fahad, Muqaddas Bibi, Farhan Aadil, and Jong-Weon Lee. "Adaptive Node Clustering for Underwater Sensor Networks." Sensors 21, no. 13 (June 30, 2021): 4514. http://dx.doi.org/10.3390/s21134514.

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Monitoring of an underwater environment and communication is essential for many applications, such as sea habitat monitoring, offshore investigation and mineral exploration, but due to underwater current, low bandwidth, high water pressure, propagation delay and error probability, underwater communication is challenging. In this paper, we proposed a sensor node clustering technique for UWSNs named as adaptive node clustering technique (ANC-UWSNs). It uses a dragonfly optimization (DFO) algorithm for selecting ideal measure of clusters needed for routing. The DFO algorithm is inspired by the swarming behavior of dragons. The proposed methodology correlates with other algorithms, for example the ant colony optimizer (ACO), comprehensive learning particle swarm optimizer (CLPSO), gray wolf optimizer (GWO) and moth flame optimizer (MFO). Grid size, transmission range and nodes density are used in a performance matrix, which varies during simulation. Results show that DFO outperform the other algorithms. It produces a higher optimized number of clusters as compared to other algorithms and hence optimizes overall routing and increases the life span of a network.
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Sameh, Mariam A., Mostafa I. Marei, M. A. Badr, and Mahmoud A. Attia. "An Optimized PV Control System Based on the Emperor Penguin Optimizer." Energies 14, no. 3 (February 1, 2021): 751. http://dx.doi.org/10.3390/en14030751.

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During the day, photovoltaic (PV) systems are exposed to different sunlight conditions in addition to partial shading (PS). Accordingly, maximum power point tracking (MPPT) techniques have become essential for PV systems to secure harvesting the maximum possible power from the PV modules. In this paper, optimized control is performed through the application of relatively newly developed optimization algorithms to PV systems under Partial Shading (PS) conditions. The initial value of the duty cycle of the boost converter is optimized for maximizing the amount of power extracted from the PV arrays. The emperor penguin optimizer (EPO) is proposed not only to optimize the initial setting of duty cycle but to tune the gains of controllers used for the boost converter and the grid-connected inverter of the PV system. In addition, the performance of the proposed system based on the EPO algorithm is compared with another newly developed optimization technique based on the cuttlefish algorithm (CFA). Moreover, particle swarm optimization (PSO) algorithm is used as a reference algorithm to compare results with both EPO and CFA. PSO is chosen since it is an old, well-tested, and effective algorithm. For the evaluation of performance of the proposed PV system using the proposed algorithms under different PS conditions, results are recorded and introduced.
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Mehta, Pranav, Betül Sultan Yıldız, Nantiwat Pholdee, Sumit Kumar, Ali Riza Yildiz, Sadiq M. Sait, and Sujin Bureerat. "A novel generalized normal distribution optimizer with elite oppositional based learning for optimization of mechanical engineering problems." Materials Testing 65, no. 2 (February 1, 2023): 210–23. http://dx.doi.org/10.1515/mt-2022-0259.

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Abstract Optimization of engineering discipline problems are quite a challenging task as they carry design parameters and various constraints. Metaheuristic algorithms can able to handle those complex problems and realize the global optimum solution for engineering problems. In this article, a novel generalized normal distribution algorithm that is integrated with elite oppositional-based learning (HGNDO-EOBL) is studied and employed to optimize the design of the eight benchmark engineering functions. Moreover, the statistical results obtained from the HGNDO-EOBL are collated with the data obtained from the well-established algorithms such as whale optimizer, salp swarm optimizer, LFD optimizer, manta ray foraging optimization algorithm, hunger games search algorithm, reptile search algorithm, and INFO algorithm. For each of the cases, a comparison of the statistical results suggests that HGNDO-EOBL is superior in terms of realizing the prominent values of the fitness function compared to established algorithms. Accordingly, the HGNDO-EOBL can be adopted for a wide range of engineering optimization problems.
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Ewees, Ahmed A., Zakariya Yahya Algamal, Laith Abualigah, Mohammed A. A. Al-qaness, Dalia Yousri, Rania M. Ghoniem, and Mohamed Abd Elaziz. "A Cox Proportional-Hazards Model Based on an Improved Aquila Optimizer with Whale Optimization Algorithm Operators." Mathematics 10, no. 8 (April 12, 2022): 1273. http://dx.doi.org/10.3390/math10081273.

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Recently, a new optimizer, called the Aquila Optimizer (AO), was developed to solve different optimization problems. Although the AO has a significant performance in various problems, like other optimization algorithms, the AO suffers from certain limitations in its search mechanism, such as local optima stagnation and convergence speed. This is a general problem that faces almost all optimization problems, which can be solved by enhancing the search process of an optimizer using an assistant search tool, such as using hybridizing with another optimizer or applying other search techniques to boost the search capability of an optimizer. Following this concept to address this critical problem, in this paper, we present an alternative version of the AO to alleviate the shortcomings of the traditional one. The main idea of the improved AO (IAO) is to use the search strategy of the Whale Optimization Algorithm (WOA) to boost the search process of the AO. Thus, the IAO benefits from the advantages of the AO and WOA, and it avoids the limitations of the local search as well as losing solutions diversity through the search process. Moreover, we apply the developed IAO optimization algorithm as a feature selection technique using different benchmark functions. More so, it is tested with extensive experimental comparisons to the traditional AO and WOA algorithms, as well as several well-known optimizers used as feature selection techniques, like the particle swarm optimization (PSO), differential evaluation (DE), mouth flame optimizer (MFO), firefly algorithm, and genetic algorithm (GA). The outcomes confirmed that the using of the WOA operators has a significant impact on the AO performance. Thus the combined IAO obtained better results compared to other optimizers.
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Yıldız, Betül Sultan, Vivek Patel, Nantiwat Pholdee, Sadiq M. Sait, Sujin Bureerat, and Ali Rıza Yıldız. "Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design." Materials Testing 63, no. 4 (April 1, 2021): 336–40. http://dx.doi.org/10.1515/mt-2020-0049.

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Abstract Vehicle component design is crucial for developing a vehicle prototype, as optimum parts can lead to cost reduction and performance enhancement of the vehicle system. The use of metaheuristics for vehicle component optimization has been commonplace due to several advantages: robustness and simplicity. This paper aims to demonstrate the shape design of a vehicle bracket by using a newly invented metaheuristic. The new optimizer is termed the ecogeography-based optimization algorithm (EBO). This is arguably the first vehicle design application of the new optimizer. The optimization problem is posed while EBO is implemented to solve the problem. It is found that the design results obtained from EBO are better when compared to other optimizers such as the equilibrium optimization algorithm, marine predators algorithm, slime mold algorithm.
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Zhang, Runze, and Yujie Zhu. "Predicting the Mechanical Properties of Heat-Treated Woods Using Optimization-Algorithm-Based BPNN." Forests 14, no. 5 (May 2, 2023): 935. http://dx.doi.org/10.3390/f14050935.

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This paper aims to enhance the accuracy of predicting the mechanical behavior of wood subjected to thermal modification using an improved dung beetle optimization (IDBO) model. The IDBO algorithm improves the original DBO algorithm via three main steps: (1) using piece-wise linear chaotic mapping (PWLCM) to generate the initial dung beetle species and increase its heterogeneity; (2) adopting an adaptive nonlinear decreasing producer ratio model to control the number of producers and boost the algorithm’s convergence rate; and (3) applying a dimensional learning-enhanced foraging (DLF) search strategy that optimizes the algorithm’s ability to explore and exploit the search space. The IDBO algorithm is evaluated on 14 benchmark functions and outperforms other algorithms. The IDBO algorithm is then applied to optimize a back-propagation (BP) neural network for predicting five mechanical property parameters of heat-treated larch-sawn timber. The results indicate that the IDBO-BP model significantly reduces the error compared with the BP, tent-sparrow search algorithm (TSSA)-BP, grey wolf optimizer (GWO)-BP, nonlinear adaptive grouping grey wolf optimizer (IGWO)-BP and DBO-BP models, demonstrating its superiority in predicting the physical characteristics of lumber after heat treatment.
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AlRassas, Ayman Mutahar, Mohammed A. A. Al-qaness, Ahmed A. Ewees, Shaoran Ren, Mohamed Abd Elaziz, Robertas Damaševičius, and Tomas Krilavičius. "Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting." Processes 9, no. 7 (July 9, 2021): 1194. http://dx.doi.org/10.3390/pr9071194.

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Oil production forecasting is one of the essential processes for organizations and governments to make necessary economic plans. This paper proposes a novel hybrid intelligence time series model to forecast oil production from two different oil fields in China and Yemen. This model is a modified ANFIS (Adaptive Neuro-Fuzzy Inference System), which is developed by applying a new optimization algorithm called the Aquila Optimizer (AO). The AO is a recently proposed optimization algorithm that was inspired by the behavior of Aquila in nature. The developed model, called AO-ANFIS, was evaluated using real-world datasets provided by local partners. In addition, extensive comparisons to the traditional ANFIS model and several modified ANFIS models using different optimization algorithms. Numeric results and statistics have confirmed the superiority of the AO-ANFIS over traditional ANFIS and several modified models. Additionally, the results reveal that AO is significantly improved ANFIS prediction accuracy. Thus, AO-ANFIS can be considered as an efficient time series tool.
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Guerra, Juan F., Ramon Garcia-Hernandez, Miguel A. Llama, and Victor Santibañez. "A Comparative Study of Swarm Intelligence Metaheuristics in UKF-Based Neural Training Applied to the Identification and Control of Robotic Manipulator." Algorithms 16, no. 8 (August 21, 2023): 393. http://dx.doi.org/10.3390/a16080393.

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This work presents a comprehensive comparative analysis of four prominent swarm intelligence (SI) optimization algorithms: Ant Lion Optimizer (ALO), Bat Algorithm (BA), Grey Wolf Optimizer (GWO), and Moth Flame Optimization (MFO). When compared under the same conditions with other SI algorithms, the Particle Swarm Optimization (PSO) stands out. First, the Unscented Kalman Filter (UKF) parameters to be optimized are selected, and then each SI optimization algorithm is executed within an off-line simulation. Once the UKF initialization parameters P0, Q0, and R0 are obtained, they are applied in real-time in the decentralized neural block control (DNBC) scheme for the trajectory tracking task of a 2-DOF robot manipulator. Finally, the results are compared according to the criteria performance evaluation using each algorithm, along with CPU cost.
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Dissertations / Theses on the topic "OPTIMIZER ALGORITHM"

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Bhandare, Ashray Sadashiv. "Bio-inspired Algorithms for Evolving the Architecture of Convolutional Neural Networks." University of Toledo / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1513273210921513.

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Lakshminarayanan, Srivathsan. "Nature Inspired Grey Wolf Optimizer Algorithm for Minimizing Operating Cost in Green Smart Home." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1438102173.

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Martz, Matthew. "Preliminary Design of an Autonomous Underwater Vehicle Using a Multiple-Objective Genetic Optimizer." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/33291.

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The process developed herein uses a Multiple Objective Genetic Optimization (MOGO) algorithm. The optimization is implemented in ModelCenter (MC) from Phoenix Integration. It uses a genetic algorithm that searches the design space for optimal, feasible designs by considering three Measures of Performance (MOPs): Cost, Effectiveness, and Risk. The complete synthesis model is comprised of an input module, the three primary AUV synthesis modules, a constraint module, three objective modules, and a genetic algorithm. The effectiveness rating determined by the synthesis model is based on nine attributes identified in the US Navyâ s UUV Master Plan and four performance-based attributes calculated by the synthesis model. To solve multi-attribute decision problems the Analytical Hierarchy Process (AHP) is used. Once the MOGO has generated a final generation of optimal, feasible designs the decision-maker(s) can choose candidate designs for further analysis. A sample AUV Synthesis was performed and five candidate AUVs were analyzed.
Master of Science
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Parandekar, Amey V. "Development of a Decision Support Framework forIntegrated Watershed Water Quality Management and a Generic Genetic Algorithm Based Optimizer." NCSU, 1999. http://www.lib.ncsu.edu/theses/available/etd-19990822-032656.

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PARANDEKAR, AMEY, VIJAY. Development of a Decision Support Framework for Integrated Watershed Water Quality Management and a Generic Genetic Algorithm Based Optimizer. (Under the direction of Dr. S. Ranji Ranjithan.)The watershed management approach is a framework for addressing water quality problems at a watershed scale in an integrated manner that considers many conflicting issues including cost, environmental impact and equity in evaluating alternative control strategies. This framework enhances the capabilities of current environmental analysis frameworks by the inclusion of additional systems analytic tools such as optimization algorithms that enable efficient search for cost effective control strategies and uncertainty analysis procedures that estimate the reliability in achieving water quality targets. Traditional optimization procedures impose severe restrictions in using complex nonlinear environmental processes within a systematic search. Hence, genetic algorithms (GAs), a class of general, probabilistic, heuristic, global, search procedures, are used. Current implementation of this framework is coupled with US EPA's BASINS software system. A component of the current research is also the development of GA object classes and optimization model classes for generic use. A graphical user interface allows users to formulate mathematical programming problems and solve them using GA methodology. This set of GA object and the user interface classes together comprise the Generic Genetic Algorithm Based Optimizer (GeGAOpt), which is demonstrated through applications in solving interactively several unconstrained as well as constrained function optimization problems.Design of these systems is based on object oriented paradigm and current software engineering practices such as object oriented analysis (OOA) and object oriented design (OOD). The development follows the waterfall model for software development. The Unified Modeling Language (UML) is used for the design. The implementation is carried out using the JavaTM programming environment

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Parandekar, Amey V. "Development of a decision support framework for integrated watershed water quality management and a Generic Genetic Algorithm Based Optimizer." Raleigh, NC : North Carolina State University, 1999. http://www.lib.ncsu.edu/etd/public/etd-492632279902331/etd.pdf.

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Pillai, Ajit Chitharanjan. "On the optimization of offshore wind farm layouts." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/25470.

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Layout optimization of offshore wind farms seeks to automate the design of the wind farm and the placement of wind turbines such that the proposed wind farm maximizes its potential. The optimization of an offshore wind farm layout therefore seeks to minimize the costs of the wind farm while maximizing the energy extraction while considering the effects of wakes on the resource; the electrical infrastructure required to collect the energy generated; the cost variation across the site; and all technical and consenting constraints that the wind farm developer must adhere to. As wakes, electrical losses, and costs are non-linear, this produces a complex optimization problem. This thesis describes the design, development, validation, and initial application of a new framework for the optimization of offshore wind farm layouts using either a genetic algorithm or a particle swarm optimizer. The developed methodology and analysis tool have been developed such that individual components can either be used to analyze a particular wind farm layout or used in conjunction with the optimization algorithms to design and optimize wind farm layouts. To accomplish this, separate modules have been developed and validated for the design and optimization of the necessary electrical infrastructure, the assessment of the energy production considering energy losses, and the estimation of the project costs. By including site-dependent parameters and project specific constraints, the framework is capable of exploring the influence the wind farm layout has on the levelized cost of energy of the project. Deploying the integrated framework using two common engineering metaheuristic algorithms to hypothetical, existing, and future wind farms highlights the advantages of this holistic layout optimization framework over the industry standard approaches commonly deployed in offshore wind farm design leading to a reduction in LCOE. Application of the tool to a UK Round 3 site recently under development has also highlighted how the use of this tool can aid in the development of future regulations by considering various constraints on the placement of wind turbines within the site and exploring how these impact the levelized cost of energy.
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Luo, Hui Long. "Optimized firefly algorithm and application." Thesis, University of Macau, 2015. http://umaclib3.umac.mo/record=b3335707.

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Thulo, Motlatsi Isaac. "Optimized Security-aware VM placement algorithm." Diss., University of Pretoria, 2019. http://hdl.handle.net/2263/73387.

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The rapidly increasing dependency on use of clouds results in Cloud Service Providers (CSPs) having to deal with high cloud services demands. To meet these demands, CSPs take advantage of virtualization technology to provide a seemingly unlimited pool of computing resources. This technology consolidates multiple instances of Virtual Machines (VMs) into the same Physical Machines (PMs) and share physical computing resources. To guarantee customer satisfaction, CSPs need to ensure optimized cloud environment that provides good Quality of Services (QoS) which conform to the performance levels stipulated in Service Level Agreements (SLAs). However, vulnerabilities associated with virtualization make it difficult to ensure optimization, more especially in multi-tenant clouds. In multi-tenant clouds, there are possibilities of consolidating VMs belonging to adversary users into the same PMs. This promotes inter-VM attacks that take advantage of shared resources to either spy, disrupt or corrupt co-located VMs. With this regard, it is important to consider placement of VMs in a manner that minimizes inter-VM attacks. This placement must, however, ensure initial objectives of providing good QoS. The aim of this study is to implement a VM placement algorithm that reduces architectural vulnerabilities brought by multi-tenancy while observing optimization objectives. It focuses on currently available VM placement algorithms and evaluates them to identify the algorithm that assumes highest optimization objectives. The identified VM placement algorithm is further augmented with security features to implement Optimized Security-aware (O-Sec) VM Placement algorithms. CloudSim Plus is used to evaluate and validate the implemented O-Sec VM placement algorithms. The evaluations in this study show that O-sec VM placement algorithm retains optimization objectives inherited from the identified VM placement algorithm. This is an algorithm that is augmented towards O-sec VM placement algorithm.
dissertation (MSc)--University of Pretoria, 2019.
Computer Science
MSc
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陳從輝 and Chung-fai Chan. "MOS parameter extraction globally optimized with genetic algorithm." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31212785.

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Pomerleau, François. "Registration algorithm optimized for simultaneous localization and mapping." Mémoire, Université de Sherbrooke, 2008. http://savoirs.usherbrooke.ca/handle/11143/1465.

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Building maps within an unknown environment while keeping track of the current position is a major step to accomplish safe and autonomous robot navigation. Within the last 20 years, Simultaneous Localization And Mapping (SLAM) became a topic of great interest in robotics. The basic idea of this technique is to combine proprioceptive robot motion information with external environmental information to minimize global positioning errors. Because the robot is moving in its environment, exteroceptive data comes from different points of view and must be expressed in the same coordinate system to be combined. The latter process is called registration. Iterative Closest Point (ICP) is a registration algorithm with very good performances in several 3D model reconstruction applications, and was recently applied to SLAM. However, SLAM has specific needs in terms of real-time and robustness comparatively to 3D model reconstructions, leaving room for specialized robotic mapping optimizations in relation to robot mapping. After reviewing existing SLAM approaches, this thesis introduces a new registration variant called Kd-ICP. This referencing technique iteratively decreases the error between misaligned point clouds without extracting specific environmental features. Results demonstrate that the new rejection technique used to achieve mapping registration is more robust to large initial positioning errors. Experiments with simulated and real environments suggest that Kd-ICP is more robust compared to other ICP variants. Moreover, the Kd-ICP is fast enough for real-time applications and is able to deal with sensor occlusions and partially overlapping maps. Realizing fast and robust local map registrations opens the door to new opportunities in SLAM. It becomes feasible to minimize the cumulation of robot positioning errors, to fuse local environmental information, to reduce memory usage when the robot is revisiting the same location. It is also possible to evaluate network constrains needed to minimize global mapping errors.
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Books on the topic "OPTIMIZER ALGORITHM"

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W, Longman Richard, and Langley Research Center, eds. Optimized system identification. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1999.

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W, Longman Richard, and Langley Research Center, eds. Optimized system identification. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1999.

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C, Chung Wilson, Smith Mark J. T, and United States. National Aeronautics and Space Administration., eds. Subband image coding with jointly optimized quantizers. [Washington, DC: National Aeronautics and Space Administration, 1995.

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Oliva, Diego, and Erik Cuevas. Advances and Applications of Optimised Algorithms in Image Processing. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-48550-8.

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Elliott, Donald M. Application of a genetic algorithm to optimize quality assurance in software development. Monterey, Calif: Naval Postgraduate School, 1993.

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Martins, Tiago, and Rui Neves. Stock Exchange Trading Using Grid Pattern Optimized by A Genetic Algorithm with Speciation. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76680-1.

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Jet Propulsion Laboratory (U.S.), ed. A Doppler centroid estimation algorithm for SAR systems optimized for the quasi-homogeneous source. Pasadena, Calif: National Aeronautics and Space Administration, Jet Propulsion Laboratory, California Institute of Technology, 1990.

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L, Palumbo Daniel, and Langley Research Center, eds. Performance of optimized actuator and sensor arrays in an active noise control system. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1996.

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Cujec, Anne-Marie. An optimized bit cell design for a pipelined current-mode algorithmic A/D converter. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1992.

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Fenn, Sebastian. Optimised algorithms and circuit architectures for performance finite field arithmetic in Reed-Solomon codecs. Huddersfield: The University, 1993.

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Book chapters on the topic "OPTIMIZER ALGORITHM"

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Xing, Bo, and Wen-Jing Gao. "Group Search Optimizer Algorithm." In Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms, 171–76. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03404-1_12.

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Mani, Melika, Omid Bozorg-Haddad, and Xuefeng Chu. "Ant Lion Optimizer (ALO) Algorithm." In Advanced Optimization by Nature-Inspired Algorithms, 105–16. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5221-7_11.

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Abualigah, Laith, Nada Khalil Al-Okbi, Seyedali Mirjalili, Mohammad Alshinwan, Husam Al Hamad, Ahmad M. Khasawneh, Waheeb Abu-Ulbeh, Mohamed Abd Elaziz, Heming Jia, and Amir H. Gandomi. "Moth-Flame Optimization Algorithm, Arithmetic Optimization Algorithm, Aquila Optimizer, Gray Wolf Optimizer, and Sine Cosine Algorithm." In Handbook of Moth-Flame Optimization Algorithm, 241–63. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003205326-16.

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Meiwal, Priyanka, Harish Sharma, and Nirmala Sharma. "Fully Informed Grey Wolf Optimizer Algorithm." In Algorithms for Intelligent Systems, 497–512. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4936-6_55.

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Sulaiman, Mohd Herwan, Zuriani Mustaffa, Mohd Mawardi Saari, Hamdan Daniyal, Ahmad Johari Mohamad, Mohd Rizal Othman, and Mohd Rusllim Mohamed. "Barnacles Mating Optimizer Algorithm for Optimization." In Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018, 211–18. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3708-6_18.

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Shen, Hai, Yunlong Zhu, Wenping Zou, and Zhu Zhu. "Group Search Optimizer Algorithm for Constrained Optimization." In Computer Science for Environmental Engineering and EcoInformatics, 48–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22691-5_9.

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Bhesdadiya, R. H., Indrajit N. Trivedi, Pradeep Jangir, Arvind Kumar, Narottam Jangir, and Rahul Totlani. "A Novel Hybrid Approach Particle Swarm Optimizer with Moth-Flame Optimizer Algorithm." In Advances in Computer and Computational Sciences, 569–77. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3770-2_53.

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Hemmasian, Amir Pouya, Kazem Meidani, Seyedali Mirjalili, and Amir Barati Farimani. "Accelerating Optimization Using Vectorized Moth-Flame Optimizer (vMFO)." In Handbook of Moth-Flame Optimization Algorithm, 97–109. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003205326-8.

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Doufene, Dyhia, Slimane Bouazabia, Sid A. Bessedik, and Khaled Ouzzir. "Grey Wolf Optimizer Algorithm for Suspension Insulator Designing." In Proceedings of Sixth International Congress on Information and Communication Technology, 763–71. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2380-6_67.

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Leke, Collins Achepsah, and Tshilidzi Marwala. "Missing Data Estimation Using Ant-Lion Optimizer Algorithm." In Studies in Big Data, 103–14. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01180-2_7.

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Conference papers on the topic "OPTIMIZER ALGORITHM"

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Guangyou, Yang. "A Modified Particle Swarm Optimizer Algorithm." In 2007 8th International Conference on Electronic Measurement and Instruments. IEEE, 2007. http://dx.doi.org/10.1109/icemi.2007.4350772.

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Zhao Xiaoqiang, Liu Weirong, and Wang Jun. "Genetic Algorithm optimizer for blend planning." In 2008 Chinese Control Conference (CCC). IEEE, 2008. http://dx.doi.org/10.1109/chicc.2008.4605751.

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Fang, Zhou, Xiangxiang Xu, Xin Li, Huizhen Yang, and Chenglong Gong. "SPGD algorithm optimization based on Adam optimizer." In Conference on Optical Sensing and Imaging Technology, edited by Dong Liu, Xiangang Luo, Yadong Jiang, and Jin Lu. SPIE, 2020. http://dx.doi.org/10.1117/12.2579991.

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Zhang, Kang, and Xingsheng Gu. "A Fast Global Group Search Optimizer algorithm." In 2014 IEEE International Conference on Information and Automation (ICIA). IEEE, 2014. http://dx.doi.org/10.1109/icinfa.2014.6932626.

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Bo, Wang, Liang GuoQiang, and Wang ChanLin. "D-S Algorithm Based on Particle Swarm Optimizer." In 2007 8th International Conference on Electronic Measurement and Instruments. IEEE, 2007. http://dx.doi.org/10.1109/icemi.2007.4350681.

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Wang, Tianlei, Leqing Chen, Qimei Zhang, Xiaoxi Hao, Renju Liu, and Yongwen Xie. "Improved sparrow search algorithm by hybrid equalization optimizer." In 2022 IEEE 4th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2022. http://dx.doi.org/10.1109/icpics55264.2022.9873760.

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Haynes, David D., and Steven M. Corns. "Algorithm for a Tabu — Ant Colony Optimizer." In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7256935.

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R, Karthikeyan. "Grey Wolf Optimizer algorithm-based unit commitment problem." In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2020. http://dx.doi.org/10.1109/i-smac49090.2020.9243458.

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Hou, Chunzhi, Jiarui Shi, and Baohang Zhang. "Evolving Dendritic Neuron Model by Equilibrium Optimizer Algorithm." In 2021 IEEE International Conference on Progress in Informatics and Computing (PIC). IEEE, 2021. http://dx.doi.org/10.1109/pic53636.2021.9687084.

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Giftson Joy, John Abish, and Robello Samuel. "Fast Drilling Optimizer for Drilling Automation." In SPE Western Regional Meeting. SPE, 2021. http://dx.doi.org/10.2118/200881-ms.

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Abstract The rate of penetration (ROP) was optimized using a particle swarm optimization algorithm for real-time field data to reduce drilling time and increase efficiency. ROP is directly related to drilling costs and is a major factor in determining mechanical specific energy, which is often used to quantify drilling efficiency. Optimization of ROP can therefore help cut down costs associated with drilling. ROP values were chosen from real-time field data, accounting for weight on bit, bit rotation, flow rate variation along with bit wear. A random forest regressor was used to find correlations between the dependent parameters. The parameters were then optimized for the given constraints to find the optimal solution space. The boundary constraints for the ROP function were determined from the real-time data. The function parameters were optimized using a particle swarm optimization algorithm. This is a meta-heuristic model used to optimize an objective function for its maximum or minimum within given constraints. The optimization method makes use of a population of solution particles which act as the particle swarm. These particles move collectively in the given solution space controlled by a mathematical model based on their position and velocity. This model makes use of the best-known solution for each particle and the global best position of the system to guide the swarm towards the optimal solution. The function was optimized for each well, providing optimal ROP values during real-time drilling. A fast drilling optimizer is crucial to automate and streamline the drilling process. This simultaneous optimization of ROP based on real-time data can be implemented during the process thereby increasing the efficiency of drilling as well as reducing the required drilling time.
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Reports on the topic "OPTIMIZER ALGORITHM"

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Bennaoui, Ahmed, AISSA AMEUR, and SLAMI SAADI. Moth-Flame Optimizer Algorithm For Optimal Of Fuzzy Logic Controller for nonlinear system. Peeref, April 2023. http://dx.doi.org/10.54985/peeref.2304p4802037.

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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.), December 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 optimum blade design; the optimization objective is to minimize the rotor power required. Following design parameter substitution, ParBlade generates the modified blade shape and updates the rotor blade properties in the RCAS script before running RCAS. After the RCAS simulations are complete, the desired performance metrics (objectives and constraints) are extracted and returned to the Dakota optimizer. Demonstrative optimization case studies were conducted using a UH-60A main rotor as the base case. Rotor power in hover and forward flight, at advance ratio 𝜇𝜇 = 0.3, are used as objective functions. The results of this study show improvement in rotor power of 6.13% and 8.52% in hover and an advance ratio of 0.3, respectively. This configuration also yields greater reductions in rotor power for high advance ratios, e.g., 12.42% reduction at 𝜇𝜇 = 0.4.
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Horrocks, Ian, and Ulrike Sattler. Optimised Reasoning for SHIQ. Aachen University of Technology, 2001. http://dx.doi.org/10.25368/2022.118.

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The tableau algorithm implemented in the FaCT knowledge representation system decides satisfiability and subsumption in SHIQ, a very expressive description logic providing, e.g., inverse and transitive roles, number restrictions, and general axioms. Intuitively, the algorithm searches for a tree-shaped abstraction of a model. To ensure termination of this algorithm without comprimising correctness, it stops expanding paths in the search tree using a so-called 'double-blocking' condition.
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Moore, Frank, Brendan Babb, Steven Becke, Heather Koyuk, Earl Lamson, Wedge III, and Christopher. Genetic Algorithms Evolve Optimized Transforms for Signal Processing Applications. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada437529.

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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.

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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 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.
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Johnson, V. M., and L. L. Rogers. Using artifical neutral networks and the genetic algorithm to optimize well-field design: Phase I. Office of Scientific and Technical Information (OSTI), March 1998. http://dx.doi.org/10.2172/3385.

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Cowell, Luke, and Ivan Carlos. PR-283-18202-R01 Improved SoLoNox T70S and T130S Controls to Reduce Part Load Emissions. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), February 2021. http://dx.doi.org/10.55274/r0012019.

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An executed NDA is required from Solar Turbines to have access to this document. The low load control algorithm for Solar Turbines' Taurus 70-10802S and Titan 130-20502S has been modified and evaluated in two field trials at the Kinder Morgan Wharton 301 and Enbridge Sabal Trail Alexander City Compressor Stations. The new algorithm extends temperature control, via bleed valve modulation, to lower engine speed settings now covering operation from full load to idle vs full load to 50% load with the prior production control method. The pilot fuel control schedule has also been optimized along with the temperature control schedule. The new control algorithm is designated as Enhanced Emissions Control (EEC). A Mobile Emissions Lab was deployed for the Taurus 70S site and for the Titan 130S site. The field trials spanned 12 months for the Taurus 70S and 8 months for the Titan 130S. Data was collected over a wide range of temperatures.
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Sullivan, Jr, Lai John M., and Q. Application of Neural Networks Coupled with Genetic Algorithms to Optimize Soil Cleanup Operations in Cold Climates. Fort Belvoir, VA: Defense Technical Information Center, December 1998. http://dx.doi.org/10.21236/ada637453.

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Baader, Franz, and Barbara Morawska. SAT Encoding of Unification in EL. Technische Universität Dresden, 2010. http://dx.doi.org/10.25368/2022.177.

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The Description Logic EL is an inexpressive knowledge representation language, which nevertheless has recently drawn considerable attention in the knowledge representation and the ontology community since, on the one hand, important inference problems such as the subsumption problem are polynomial. On the other hand, EL is used to define large biomedical ontologies. Unification in Description Logics has been proposed as a novel inference service that can, for example, be used to detect redundancies in ontologies. In a recent paper, we have shown that unification in EL is NP-complete, and thus of a complexity that is considerably lower than in other Description Logics of comparably restricted expressive power. In this paper, we introduce a new NP-algorithm for solving unification problem in EL, which is based on a reduction to satisfiability in propositional logic (SAT). The advantage of this new algorithm is, on the one hand, that it allows us to employ highly optimized state of the art SAT solverswhen implementing an EL-unification algorithm. On the other hand, this reduction provides us with a proof of the fact that EL-unification is in NP that is much simpler than the one given in our previous paper on EL-unification.
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Mahowald, Natalie. Collaborative Project: Building improved optimized parameter estimation algorithms to improve methane and nitrogen fluxes in a climate model. Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1333698.

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