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Journal articles on the topic 'Algorithme Design'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Meita, Rumbayan *. Muhamad Dwisnanto Putro. "A CONCEPT OF SOLAR TRACKER SYSTEM DESIGN." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 11 (2017): 440–50. https://doi.org/10.5281/zenodo.1066204.

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Improvement of solar panel efficiency is an ongoing research work recently. Maximizing the output power by integrating with the solar tracker system becomes a interest point of the research. This paper presents the concept in designing a solar tracker system applied to solar panel. The development of solar panel tracker system design that consist of system display prototype design, hardware design, and algorithm design. This concept is useful as the control system for solar tracker to improve the efficency of solar panel as the green energy system. For future work, the prototype of solar track

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2

John-Mathews, Jean-Marie. "Un cadre d’autorégulation pour l’éthique de L’IA : opportunités et défis." Vie & sciences de l'entreprise N° 216-217, no. 1 (2023): 288–311. http://dx.doi.org/10.3917/vse.216.0288.

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Nous proposons un outil d&#8217;autor&#233;gulation pour la conception d&#8217;IA qui int&#232;gre des mesures soci&#233;tales telles que l&#8217;&#233;quit&#233;, l&#8217;interpr&#233;tabilit&#233; et la confidentialit&#233;. Pour ce faire, nous cr&#233;ons une interface qui permet aux praticiens de l&#8217;IA (experts en m&#233;gadonn&#233;es/ data scientists ) de choisir visuellement l&#8217;algorithme d&#8217;apprentissage (Machine Learning/ML) qui correspond le mieux aux pr&#233;f&#233;rences &#233;thiques

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Goulenok, Cyril, Marc Grassin, Robin Cremer, et al. "Introduction aux enjeux éthiques de l’Intelligence Artificielle en réanimation." Médecine Intensive Réanimation 33, Hors-série 1 (2024): 101–10. http://dx.doi.org/10.37051/mir-00211.

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L’Intelligence Artificielle (IA) va occuper une place grandissante en réanimation dans les années à venir. Les possibilités d’utilisation seront très larges, couvrant notamment les champs des prédictions, de l’aide à la décision, de l’imagerie, de la recherche et la formation des soignants. L’implémentation de l’IA en réanimation pourrait significativement bouleverser la prise en charge des patients, mais aussi l’activité des médecins. Ces changements à venir amènent des questionnements éthiques spécifiques qu’il est nécessaire et urgent d’évoquer. De la mise au point d’un algorithme jusqu’à s

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Dharani Pragada, Venkata Aditya, Akanistha Banerjee, and Srinivasan Venkataraman. "OPTIMISATION OF NAVAL SHIP COMPARTMENT LAYOUT DESIGN USING GENETIC ALGORITHM." Proceedings of the Design Society 1 (July 27, 2021): 2339–48. http://dx.doi.org/10.1017/pds.2021.495.

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AbstractAn efficient general arrangement is a cornerstone of a good ship design. A big part of the whole general arrangement process is finding an optimized compartment layout. This task is especially tricky since the multiple needs are often conflicting, and it becomes a serious challenge for the ship designers. To aid the ship designers, improved and reliable statistical and computation methods have come to the fore. Genetic algorithms are one of the most widely used methods. Islier's algorithm for the multi-facility layout problem and an improved genetic algorithm for the ship layout design

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An Ouyang. "Visual Attention Analysis and Optimization Algorithm in Packaging Design." Journal of Electrical Systems 20, no. 6s (2024): 96–105. http://dx.doi.org/10.52783/jes.2619.

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Visual attention analysis and optimization algorithms in packaging design refer to computational methods aimed at understanding and improving the visual appeal and effectiveness of packaging materials. This study proposes a novel approach for enhancing packaging design through the integration of visual attention analysis and optimization algorithms, employing autoencoder and whale optimization techniques. Visual attention analysis and optimization algorithm in packaging design is to enhance the effectiveness of packaging materials by maximizing their ability to capture and retain consumer atte

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Ciric, Vladimir, Aleksandar Cvetkovic, Ivan Milentijevic, and Oliver Vojinovic. "All-Pairs Shortest Paths Algorithm for Regular 2D Mesh Topologies." JUCS - Journal of Universal Computer Science 22, no. (11) (2016): 1437–55. https://doi.org/10.3217/jucs-022-11-1437.

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Motivated by the large number of vertices that future technologies will put in the front of path-search algorithms, and inspired by highly regular 2D mesh structures that exist in the domain applications, in this paper we propose a new allpairs shortest paths algorithm, for any given regular 2D mesh topology, with complexity Ο(|V|2), where |V| is the number of vertices in the graph. The proposed algorithm can achieve better runtime than other known algorithms at the cost of narrowing the scope of the graphs that it can process to the graphs with regular 2D topology. The algorithm is developed

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LI, JIANG, MICHAEL T. MANRY, CHANGHUA YU, and D. RANDALL WILSON. "PROTOTYPE CLASSIFIER DESIGN WITH PRUNING." International Journal on Artificial Intelligence Tools 14, no. 01n02 (2005): 261–80. http://dx.doi.org/10.1142/s0218213005002090.

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Algorithms reducing the storage requirement of the nearest neighbor classifier (NNC) can be divided into three main categories: Fast searching algorithms, Instance-based learning algorithms and Prototype based algorithms. We propose an algorithm, LVQPRU, for pruning NNC prototype vectors and a compact classifier with good performance is obtained. The basic condensing algorithm is applied to the initial prototypes to speed up the learning process. The learning vector quantization (LVQ) algorithm is utilized to fine tune the remaining prototypes during each pruning iteration. We evaluate LVQPRU

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Sakri, Leena I., Deepa Bendigeri, Sachin Joshi, et al. "Visualizer for Algorithms and Path Finding with User Interface Design." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 5902–7. http://dx.doi.org/10.22214/ijraset.2023.52237.

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Abstract: A graphical description of how algorithms like sorting and path finding work is provided by the Algorithm and Path Finding visualizer. The system thoroughly explains each process using both text and animation. Dots, lines, dimensional bars, and other graphic elements are combined to create an algorithm visualisation, which shows some of the algorithm's more "interesting events." The benefits of the algorithm visualizer include a user-friendly UI. It's easy to use the UI. Users move easily and without exertion. input of data entry by users is possible in the system. User command Users

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Al-Bazoon, Mustafa, and Jasbir Arora. "A Hybrid Stochastic Algorithm with Domain Reduction for Discrete Structural Optimization." Misan Journal of Engineering Sciences 1, no. 2 (2020): 16–36. http://dx.doi.org/10.61263/mjes.v1i2.14.

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In recent years, many nature-inspired metaheuristic optimization algorithms have been proposed in an effort to develop efficient and robust algorithms. The drawback in most of them is the large number of simulations required to obtain good designs. To reduce the number of structural analyses to reach the best design, a new two-phase algorithm is proposed and evaluated. This hybrid algorithm is based on the well-known Harmony Search (HS) algorithm and recently developed Colliding Bodied Optimization (CBO). HS analyzes and improves one design in every iteration whereas CBO generates and analyzes

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Narasimha Nayak, V., M. Ravi Kumar, K. Anusha, and Ch Kranthi Kiran. "FPGA based asymmetric crypto system design." International Journal of Engineering & Technology 7, no. 1.1 (2017): 612. http://dx.doi.org/10.14419/ijet.v7i1.1.10788.

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In the network security system cryptography plays a vital role for the secure transmission of information. Cryptography is a process of integrating and transferring the data to the genuine users against any attacks. There are two types of Cryptographic algorithm: Symmetric and Asymmetric algorithms. In the symmetric type cryptography, single key is used for both encryption and decryption. Symmetric algorithms are fast and simple. Asymmetric cryptographic algorithm uses different keys such as public key to encrypt the message at sender and private key which is known only to receiver for decrypt

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Tian, Fangzheng. "Research And Design of A 3D Reconstruction Algorithm with Lio Algorithm." Highlights in Science, Engineering and Technology 71 (November 28, 2023): 121–29. http://dx.doi.org/10.54097/hset.v71i.12679.

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Based on existing research results, this article uses the WheelTev Ackermann-type robot to implement the Fast-Lio and related improved algorithms and initializes system parameters under the condition of tight coupling of laser radar and inertial navigation unit data. At the same time, the obtained point cloud map is subjected to backend loop detection to remove graphic distortion generated during mapping and achieve 3D structure reproduction in multiple semi-open environments. Selected scenes with structural features in the campus of Southeast University were used to test the algorithm's effec

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Mounica, P., R. Umamaheswari, and R. Madhavi R. Nischala N. Ramesh Babu. "Design A Congestion Aware Routing Algorithm for Synchronous Cam Design." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 1596–99. http://dx.doi.org/10.31142/ijtsrd11547.

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赵, 子安. "Research on UAV Modeling Design Based on Interactive Genetic Algorithm." Design 09, no. 03 (2024): 17–26. http://dx.doi.org/10.12677/design.2024.93283.

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Dangi, Siddharth, Amy L. Orsborn, Helene G. Moorman, and Jose M. Carmena. "Design and Analysis of Closed-Loop Decoder Adaptation Algorithms for Brain-Machine Interfaces." Neural Computation 25, no. 7 (2013): 1693–731. http://dx.doi.org/10.1162/neco_a_00460.

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Closed-loop decoder adaptation (CLDA) is an emerging paradigm for achieving rapid performance improvements in online brain-machine interface (BMI) operation. Designing an effective CLDA algorithm requires making multiple important decisions, including choosing the timescale of adaptation, selecting which decoder parameters to adapt, crafting the corresponding update rules, and designing CLDA parameters. These design choices, combined with the specific settings of CLDA parameters, will directly affect the algorithm's ability to make decoder parameters converge to values that optimize performanc

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Johns, Matthew B., Edward Keedwell, and Dragan Savic. "Adaptive locally constrained genetic algorithm for least-cost water distribution network design." Journal of Hydroinformatics 16, no. 2 (2013): 288–301. http://dx.doi.org/10.2166/hydro.2013.218.

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This paper describes the development of an adaptive locally constrained genetic algorithm (ALCO-GA) and its application to the problem of least cost water distribution network design. Genetic algorithms have been used widely for the optimisation of both theoretical and real-world nonlinear optimisation problems, including water system design and maintenance problems. In this work we propose a heuristic-based approach to the mutation of chromosomes with the algorithm employing an adaptive mutation operator which utilises hydraulic head information and an elementary heuristic to increase the eff

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Mramba, Lazarus, and Salvador Gezan. "Evaluating Algorithm Efficiency for Optimizing Experimental Designs with Correlated Data." Algorithms 11, no. 12 (2018): 212. http://dx.doi.org/10.3390/a11120212.

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The search for efficient methods and procedures to optimize experimental designs is a vital process in field trials that is often challenged by computational bottlenecks. Most existing methods ignore the presence of some form of correlations in the data to simplify the optimization process at the design stage. This study explores several algorithms for improving field experimental designs using a linear mixed models statistical framework adjusting for both spatial and genetic correlations based on A- and D-optimality criteria. Relative design efficiencies are estimated for an array of algorith

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Üstüner, Betül, and Erkan Doğan. "Solution of design optimization problems via metaheuristic search methods." Journal of Structural Engineering & Applied Mechanics 5, no. 2 (2022): 96–116. http://dx.doi.org/10.31462/jseam.2022.02096116.

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Metaheuristic algorithms inspired by natural phenomena are frequently used in solving optimization problems recently. Just as every problem has its characteristics, every algorithm has its unique structure. Therefore, problem-specific algorithm selection is an important issue. In addition, metaheuristic algorithms are very open to development. Therefore, improved/modified versions of algorithms are common. Working with benchmarking problems and engineering design problems is the best way to compare the performance and reliability of metaheuristic algorithms. In this study, the performances of

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VUKIĆEVIĆ, MILAN, MILOŠ JOVANOVIĆ, BORIS DELIBAŠIĆ, SONJA IŠLJAMOVIĆ, and MILIJA SUKNOVIĆ. "REUSABLE COMPONENT-BASED ARCHITECTURE FOR DECISION TREE ALGORITHM DESIGN." International Journal on Artificial Intelligence Tools 21, no. 05 (2012): 1250022. http://dx.doi.org/10.1142/s0218213012500224.

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Many decision tree algorithms were proposed over the last few decades. A lack of publishing standards for decision tree algorithm software produced a large time gap between algorithm proposals and their wider application in practice. Non-existence of common repository for storing algorithms and their parts led to a need to re-implement these algorithms from a scratch when they had to be implemented on a different platform. This makes the comparison between algorithms and their partial improvements vague. In addition, combinations and interactions between different algorithm parts haven't been

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Chittineni, Suresh, A. N. S. Pradeep, Dinesh Godavarthi, Suresh Chandra Satapathy, S. Mohan Krishna, and P. V. G. D. Prasad Reddy. "Design of Fixed and Ladder Mutation Factor-Based Clonal Selection Algorithm for Solving Unimodal and Multimodal Functions." Applied Computational Intelligence and Soft Computing 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/210918.

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Clonal selection algorithms (CSAs) is a special class of immune algorithms (IA), inspired by the clonal selection principle of the human immune system. To improve the algorithm's ability to perform better, this CSA has been modified by implementing two new concepts called fixed mutation factor and ladder mutation factor. Fixed mutation factor maintains a constant factor throughout the process, where as ladder mutation factor changes adaptively based on the affinity of antibodies. This paper compared the conventional CLONALG, with the two proposed approaches and tested on several standard bench

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Singh, Surya Partap, Amitesh Srivastava, Suryansh Dwivedi, and Mr Anil Kumar Pandey. "AI Based Recruitment Tool." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2815–19. http://dx.doi.org/10.22214/ijraset.2023.52193.

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Abstract: In this study, the researchers narrowed their focus to the application of algorithmic decision-making in ranking job applicants. Instead of comparing algorithms to human decision-makers, the study examined participants' perceptions of different types of algorithms. The researchers varied the complexity and transparency of the algorithm to understand how these factors influenced participants' perceptions. The study explored participants' trust in the algorithm's decision-making abilities, fairness of the decisions, and emotional responses to the situation. Unlike previous work, the st

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Barbulescu, L., A. E. Howe, L. D. Whitley, and M. Roberts. "Understanding Algorithm Performance on an Oversubscribed Scheduling Application." Journal of Artificial Intelligence Research 27 (December 28, 2006): 577–615. http://dx.doi.org/10.1613/jair.2038.

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The best performing algorithms for a particular oversubscribed scheduling application, Air Force Satellite Control Network (AFSCN) scheduling, appear to have little in common. Yet, through careful experimentation and modeling of performance in real problem instances, we can relate characteristics of the best algorithms to characteristics of the application. In particular, we find that plateaus dominate the search spaces (thus favoring algorithms that make larger changes to solutions) and that some randomization in exploration is critical to good performance (due to the lack of gradient informa

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Ludmir, Jason Zev, Yuqian Huo, Nicholas S. DiBrita, and Tirthak Patel. "Modeling and Simulating Rydberg Atom Quantum Computers for Hardware-Software Co-design with PachinQo." Proceedings of the ACM on Measurement and Analysis of Computing Systems 8, no. 3 (2024): 1–25. https://doi.org/10.1145/3700421.

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Quantum computing has the potential to accelerate various domains: scientific computation, machine learning, and optimization. Recently, Rydberg atom quantum computing has emerged as a promising quantum computing technology, especially with the demonstration of the zonal addressing architecture. However, this demonstration is only compatible with one type of quantum algorithm, and extending it to compile and execute general quantum algorithms is a challenge. To address it, we propose PachinQo, a framework to co-design the architecture and compilation for zonal addressing systems for any given

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Ludmir, Jason, Yuqian Huo, Nicholas S. DiBrita, and Tirthak Patel. "Modeling and Simulating Rydberg Atom Quantum Computers for Hardware-Software Co-design with PachinQo." ACM SIGMETRICS Performance Evaluation Review 53, no. 1 (2025): 136–38. https://doi.org/10.1145/3744970.3727295.

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Quantum computing has the potential to accelerate various domains: scientific computation, machine learning, and optimization. Recently, Rydberg atom quantum computing has emerged as a promising quantum computing technology, especially with the demonstration of the zonal addressing architecture. However, this demonstration is only compatible with one type of quantum algorithm, and extending it to compile and execute general quantum algorithms is a challenge. To address it, we propose PachinQo, a framework to co-design the architecture and compilation for zonal addressing systems for any given

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Karapetyan, Daniel, and Gregory Gutin. "A New Approach to Population Sizing for Memetic Algorithms: A Case Study for the Multidimensional Assignment Problem." Evolutionary Computation 19, no. 3 (2011): 345–71. http://dx.doi.org/10.1162/evco_a_00026.

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Memetic algorithms are known to be a powerful technique in solving hard optimization problems. To design a memetic algorithm, one needs to make a host of decisions. Selecting the population size is one of the most important among them. Most of the algorithms in the literature fix the population size to a certain constant value. This reduces the algorithm's quality since the optimal population size varies for different instances, local search procedures, and runtimes. In this paper we propose an adjustable population size. It is calculated as a function of the runtime of the whole algorithm and

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ALISANTOSO, D., L. P. KHOO, and I. B. H. LEE. "An approach to the analysis of design concepts using the rough set theory." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 18, no. 4 (2004): 343–55. http://dx.doi.org/10.1017/s0890060404040211.

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This paper describes an approach to the analysis of design concepts (DCs) using the rough set theory. The proposed approach attempts to extract design knowledge from past designs, and used the knowledge obtained to perform DC–capability mapping in a dynamic design evolution environment. The mapping enables designers to estimate the feasibility of a DC to meet stipulated design specifications. The proposed approach encompasses two algorithms, namely, the dissimilar objects algorithm and the attribute decomposition algorithm, to deal with an information system with unavailable information and mu

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Almufti, Saman M. "Exploring the Impact of Big Bang-Big Crunch Algorithm Parameters on Welded Beam Design Problem Resolution." Academic Journal of Nawroz University 12, no. 4 (2023): 1–16. http://dx.doi.org/10.25007/ajnu.v12n4a1903.

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A Metaheuristic Optimization is a group of algorithms that are widely studied and employed in the scientific literature. Typically, metaheuristics algorithms utilize stochastic operators that make each iteration unique, and they frequently contain controlling parameters that have an impact on the convergence process since their impacts are mostly neglected in most optimization literature, making it difficult to draw conclusions. This paper introduced the Big Bang-Big Crunch (BB-BC) metaheuristic algorithm to evaluate the performance of a metaheuristic algorithm in relation to its control param

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P., Mounica, Umamaheswari R., Madhavi R., Ramesh Babu N., and Nischala R. "Design A Congestion Aware Routing Algorithm for Synchronous Cam Design." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 1596–99. https://doi.org/10.31142/ijtsrd11547.

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The effect of process variation PV on delay is a major reason to decay the performance in advanced technologies. The performance of front routing algorithms is determined with or without PV for different traffic patterns. The saturation throughput and average message delay are used as performance metrics to evaluate the throughput. PV decreases the saturation throughput and increases the average message delay. Adaptive routing algorithm should be manipulated with the PV. A novel PV delay and congestion aware routing PDCR algorithm is presented for asynchronous network on chip NOC design. The r

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Sadeeq, Haval Tariq, Araz Abrahim, Thamer Hameed, Najdavan Kako, Reber Mohammed, and Dindar Ahmed. "An improved pelican optimization algorithm for function optimization and constrained engineering design problems." Decision Science Letters 14, no. 3 (2025): 623–40. https://doi.org/10.5267/j.dsl.2025.4.004.

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Metaheuristic algorithms are a class of optimization techniques that have revolutionized problem-solving across various domains. These algorithms provide a versatile and powerful approach to finding near-optimal solutions for complex, combinatorial, and computationally intensive problems. They draw inspiration from natural processes, such as evolution, swarm behavior, or annealing, to iteratively refine solutions by intelligently navigating the problem space. Metaheuristics have become indispensable tools in both academia and industry, helping researchers and practitioners address real-world p

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Yahya, Randa Jalaa, and Nizar Hadi Abbas. "Optimal integral sliding mode controller controller design for 2-RLFJ manipulator based on hybrid optimization algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 293. http://dx.doi.org/10.11591/ijece.v12i1.pp293-302.

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A newly hybrid nature-inspired algorithm called HSSGWOA is presented with the combination of the salp swarm algorithm (SSA) and grey wolf optimizer (GWO). The major idea is to combine the salp swarm algorithm's exploitation ability with the grey wolf optimizer's exploration ability to generate both variants' strength. The proposed algorithm uses to tune the parameters of the integral sliding mode controller (ISMC) that design to improve the dynamic performance of the two-link flexible joint manipulator. The efficiency and the capability of the proposed hybrid algorithm are evaluated based on t

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Qian-Han Zhang, Qian-Han Zhang, Bing-Yan Wei Qian-Han Zhang, Dong-Liang Fan Bing-Yan Wei, Xiao-Ying Wu Dong-Liang Fan, and Jin-Ping Du Xiao-Ying Wu. "Design of An Intelligent Monitoring and Control System for Photovoltaic Microgrids." 電腦學刊 34, no. 6 (2023): 145–52. http://dx.doi.org/10.53106/199115992023123406011.

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<p>This article focuses on the problems of imperfect models and slow convergence speed of optimization algorithms in the use of photovoltaic microgrids. Firstly, accurate mathematical models are established based on the composition of photovoltaic microgrids, namely photovoltaic power generation systems and energy storage systems. Then, an improved cat swarm algorithm is used to solve the model, ultimately achieving an increase in solving speed during the process while avoiding the algorithm from falling into local optima. Finally, an intelligent monitoring system for photovoltaic microg

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Seufert, Philipp, Jan Schwientek, and Michael Bortz. "Model-Based Design of Experiments for High-Dimensional Inputs Supported by Machine-Learning Methods." Processes 9, no. 3 (2021): 508. http://dx.doi.org/10.3390/pr9030508.

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Algorithms that compute locally optimal continuous designs often rely on a finite design space or on the repeated solution of difficult non-linear programs. Both approaches require extensive evaluations of the Jacobian Df of the underlying model. These evaluations are a heavy computational burden. Based on the Kiefer-Wolfowitz Equivalence Theorem, we present a novel design of experiments algorithm that computes optimal designs in a continuous design space. For this iterative algorithm, we combine an adaptive Bayes-like sampling scheme with Gaussian process regression to approximate the directi

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Milenković, Branislav, Đorđe Jovanović, and Mladen Krstić. "Mechanical engineering design optimization using reptile search algorithm." Scientific Technical Review 72, no. 1 (2022): 22–26. http://dx.doi.org/10.5937/str2201022m.

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Optimization algorithms plays a vital role in mechanical engineering. In this paper we have demonstrated how the Reptile Search Algorithm (RSA) algorithm is able to solve classical engineering design problems. In the first part, the biological reference, as well as a detailed overview of the algorithm is given. Afterwards, the RSA algorithm and the potential to solve the machine engineering design class of problems is given. The source code for this algorithm was written using MATLAB R2020a software suite. The Reptile Search Algorithm (RSA) algorithm was used for optimization problems in the f

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Liu, Xiao Yong. "Design of RBF Neural Networks Based on Adjustable Radius." Key Engineering Materials 439-440 (June 2010): 605–10. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.605.

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In this paper, a new RBF neural network (RBFNN) algorithm, called ar-RBFNN, is presented. In traditional RBFNNs based on clustering algorithm, called oRBFNN in this paper, the width of the basis function-Gaussian function, or called radius, ignored the effect of numbers in different clusters, or density of data points. New algorithm considers radius is effect to performance of algorithms in problem of function approximation. Mean Square Error is used to evaluate performances of two algorithms, oRBFNN and ar-RBFNN algorithms. Several experiments in function approximation show ar-RBFNN is better

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Thomas, Maxime, Lorenzo Nicoletti, Pascal Le Masson, and Benoit Weil. "CAN PARETO FRONTS MEET THE SPLITTING CONDITION? COMPARING TWO GENERATIVE DESIGN ALGORITHMS BASED ON THE VARIETY OF DESIGN PARAMETERS COMBINATIONS THEY GENERATE." Proceedings of the Design Society 3 (June 19, 2023): 827–36. http://dx.doi.org/10.1017/pds.2023.83.

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AbstractGenerative Design (GD) is a design approach that uses algorithms to generate designs. This paper investigates the role of optimisation algorithms in GD process. We study how Pareto Fronts – a classical optimization algorithm output – help designers to browse the variety associated with a design problem. Thanks to the “splitting condition” from design theory, we show that valuable Pareto Fronts for designers are those that allow the exploration of a variety of design parameters without modifying substantially the performance of the designed solution. We call “Splitting Pareto Front” the

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Kaur, Pradeep, and Pankaj Bhambri. "To Design an Algorithm for Text Watermarking." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 03, no. 03 (2015): 01–06. http://dx.doi.org/10.9756/sijcsea/v3i3/03050150101.

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Tiwari, Pooja, Vishnu Narayan Mishra, and Raghav Prasad Parouha. "Design Optimization Using Modified Differential Evolution Algorithm." Indian Journal Of Science And Technology 16, no. 43 (2023): 3917–26. http://dx.doi.org/10.17485/ijst/v16i43.1392.

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An, Jianjing, Dezheng Zhang, Ke Xu, and Dong Wang. "An OpenCL-Based FPGA Accelerator for Faster R-CNN." Entropy 24, no. 10 (2022): 1346. http://dx.doi.org/10.3390/e24101346.

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In recent years, convolutional neural network (CNN)-based object detection algorithms have made breakthroughs, and much of the research corresponds to hardware accelerator designs. Although many previous works have proposed efficient FPGA designs for one-stage detectors such as Yolo, there are still few accelerator designs for faster regions with CNN features (Faster R-CNN) algorithms. Moreover, CNN’s inherently high computational complexity and high memory complexity bring challenges to the design of efficient accelerators. This paper proposes a software-hardware co-design scheme based on Ope

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Wen, Xiaodong, Xiangdong Liu, Cunhui Yu, et al. "IOOA: A multi-strategy fusion improved Osprey Optimization Algorithm for global optimization." Electronic Research Archive 32, no. 3 (2024): 2033–74. http://dx.doi.org/10.3934/era.2024093.

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<abstract><p>With the widespread application of metaheuristic algorithms in engineering and scientific research, finding algorithms with efficient global search capabilities and precise local search performance has become a hot topic in research. The osprey optimization algorithm (OOA) was first proposed in 2023, characterized by its simple structure and strong optimization capability. However, practical tests have revealed that the OOA algorithm inevitably encounters common issues faced by metaheuristic algorithms, such as the tendency to fall into local optima and reduced populat

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Chen, Cong, Fanxin Wang, Jiayin Pan, Lang Xu, and Hongming Gao. "Algorithm Design for an Online Berth Allocation Problem." Journal of Marine Science and Engineering 12, no. 10 (2024): 1722. http://dx.doi.org/10.3390/jmse12101722.

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In this paper, we investigate an online berth allocation problem, where vessels arrive one by one and their information is revealed upon arrival. Our objective is to design online algorithms to minimize the maximum load of all berths (makespan). We first demonstrate that the widely used Greedy algorithm has a very poor theoretical guarantee; specifically, the competitive ratio of the Greedy algorithm for this problem is lower bounded by Ω(log⁡m/log⁡log⁡m), which increases with the number of berths m. On account of this, we borrow an idea from algorithms for the online strip packing problem and

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Olsson, Ralph J., Zoran Kapelan, and Dragan A. Savic. "Probabilistic building block identification for the optimal design and rehabilitation of water distribution systems." Journal of Hydroinformatics 11, no. 2 (2009): 89–105. http://dx.doi.org/10.2166/hydro.2009.047.

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The multi-objective design and rehabilitation of water distribution systems (WDS) is defined as the search for the set of system designs which offers the best trade-off between competing design objectives. Typically these objectives will consist of the cost of implementing a system design and a measure of the performance of that system. These measures are often in competition since improvements in the performance of a system generally come at a cost. Here three genetic algorithms which use probabilistic methods to identify building blocks—the Univariate Marginal Distribution Algorithm (UMDA) (

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Ghorpade, Bhairavsingh, and Shivakumar Raman. "Communication of Design Data in Manufacturing Democratization." Journal of Manufacturing and Materials Processing 7, no. 3 (2023): 108. http://dx.doi.org/10.3390/jmmp7030108.

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Part design is the principal source of communicating design intent to manufacturing and inspection. Design data are often communicated through computer-aided design (CAD) systems. Modern analytics tools and artificial intelligence integration into manufacturing have significantly advanced machine recognition of design specification and manufacturing constraints. These algorithms require data to be uniformly structured and easily consumable; however, the design data are represented in a graphical structure and contain a nonuniform structure, which limits the use of machine learning algorithms f

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Sabery, Ghulam Ali, Ghulam Hassan Danishyar, and Ghulam Sarwar Mubarez. "A Comparative Study of Metaheuristic Optimization Algorithms for Solving Engineering Design Problems." Journal of Mathematics and Statistics Studies 4, no. 4 (2023): 56–69. http://dx.doi.org/10.32996/jmss.2023.4.4.6.

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Metaheuristic optimization algorithms (Nature-Inspired Optimization Algorithms) are a class of algorithms that mimic the behavior of natural systems such as evolution process, swarm intelligence, human activity and physical phenomena to find the optimal solution. Since the introduction of meta-heuristic optimization algorithms, they have shown their profound impact in solving the high-scale and non-differentiable engineering problems. This paper presents a comparative study of the most widely used nature-inspired optimization algorithms for solving engineering classical design problems, which

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Koçak, Can, and Sema Alaçam. "Marco de diseño asistido por algoritmos para BIM: luz natural en las primeras fases del diseño." ESTOA 12, no. 24 (2023): 67–79. http://dx.doi.org/10.18537/est.v012.n024.a06.

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This study proposes an algorithm-aided design (AAD) framework for using daylight in the early phases of architectural design. Within the scope of the proposed AAD, a performance-based design approach has been adopted that can suggest design solutions by optimising with the Genetic Algorithm (GA) that evaluates the results obtained from daylight calculation. The AAD framework was developed using the visual programming application (Dynamo), which interoperates with BIM Software Autodesk Revit. A case study is simulated to show how the algorithm is used to generate, evaluate and evolve a massing

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Ali, Syed M., Yanling Guo, Syed Tahir Hussain Rizvi, Roohul Amin, and Awais Yasin. "Interval Type 2 Fuzzy Adaptive Motion Drive Algorithm Design." Electronics 12, no. 13 (2023): 2946. http://dx.doi.org/10.3390/electronics12132946.

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Motion drive algorithms are a set of filters designed to simulate realistic motion and are an integral part of contemporary vehicle simulators. This paper presents the design of a novel intelligent interval type 2 fuzzy adaptive motion drive algorithm for an off-road uphill vehicle simulator. The off-road, uphill vehicle simulator is used to train and assess the driver’s behavior under varying operational and environmental conditions in mountainous terrain. The proposed algorithm is the first of its kind to be proposed for off-road uphill vehicle simulators, and it offers numerous benefits ove

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ÇOŞUT, Muhammed, Gebrail BEKDAŞ, and Sinan Melih NİGDELİ. "Comparison of different regulations and metaheuristic algorithms in beam design." International Journal of Engineering and Applied Sciences 15, no. 1 (2023): 1–18. http://dx.doi.org/10.24107/ijeas.1232408.

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In this first study, the rectangular reinforced concrete beam’s costs and cross-section sizes are found by using Harmony Search (HS), Differential Evolution Algorithm (DE), Jaya Algorithm, Teaching- Learning Based Algorithm (TLBO), Hybrid algorithm (Jaya-TLBO) and Flower Pollination Algorithm (FPA) separately by using ACI 318 building code. In addition, in order to better see how successful the algorithms are, the standard deviation of the algorithms used in the project in a certain number of iterations, price changes and in which iteration the minimum cost is compared. As a result of running

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Garg, Vanita, Kusum Deep, Khalid Abdulaziz Alnowibet, Ali Wagdy Mohamed, Mohammad Shokouhifar, and Frank Werner. "LX-BBSCA: Laplacian biogeography-based sine cosine algorithm for structural engineering design optimization." AIMS Mathematics 8, no. 12 (2023): 30610–38. http://dx.doi.org/10.3934/math.20231565.

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<abstract> <p>In this paper, an ensemble metaheuristic algorithm (denoted as LX-BBSCA) is introduced. It combines the strengths of Laplacian biogeography-based optimization (LX-BBO) and the sine cosine algorithm (SCA) to address structural engineering design optimization problems. Our primary objective is to mitigate the risk of getting stuck in local minima and accelerate the algorithm's convergence rate. We evaluate the proposed LX-BBSCA algorithm on a set of 23 benchmark functions, including both unimodal and multimodal problems of varying complexity and dimensions. Additionally

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Ikhwani, Yusri, As`ary Ramadhan, Muhammad Bahit, and Taufik Hidayat Faesal. "Single elimination tournament design using dynamic programming algorithm." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 23, no. 1 (2023): 113–30. http://dx.doi.org/10.30812/matrik.v23i1.3290.

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Finding the best single-elimination tournament design is important in scientific inquiry because it can have major financial implications for event organizers and participants. This research aims to create an optimal single-elimination tournament design using binary tree modeling with dummy techniques. Dynamic programming algorithms have been used to compute optimal single-elimination designs to overcome this effectively. This research method uses various implementations of sub-optimal algorithms and then compares their performance in terms of runtime and optimality as a solution to measure th

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Rao, R. V., and R. B. Pawar. "Quasi-oppositional-based Rao algorithms for multi-objective design optimization of selected heat sinks." Journal of Computational Design and Engineering 7, no. 6 (2020): 830–63. http://dx.doi.org/10.1093/jcde/qwaa060.

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Abstract In this paper, an endeavor is made to enhance the convergence speed of the recently proposed Rao algorithms. The new upgraded versions of Rao algorithms named as “quasi-oppositional-based Rao algorithms” are proposed in this paper. The quasi-oppositional-based learning is incorporated in the basic Rao algorithms to diversify the searching process of the algorithms. The performance of the proposed algorithms is tested on 51 unconstrained benchmark functions. Also, three multi-objective optimization case studies of different heat sinks such as a single-layered microchannel heat sink (SL

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Gopalakrishnan, Gayathri, Barbara S. Minsker, and David E. Goldberg. "Optimal sampling in a noisy genetic algorithm for risk-based remediation design." Journal of Hydroinformatics 5, no. 1 (2003): 11–25. http://dx.doi.org/10.2166/hydro.2003.0002.

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A groundwater management model has been developed that predicts human health risks and uses a noisy genetic algorithm to identify promising risk-based corrective action (RBCA) designs. Noisy genetic algorithms are simple genetic algorithms that operate in noisy environments. The noisy genetic algorithm uses a type of noisy fitness function (objective function) called the sampling fitness function, which utilises Monte-Carlo-type sampling to find robust designs. Unlike Monte Carlo simulation modelling, however, the noisy genetic algorithm is highly efficient and can identify robust designs with

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Wang, Zhe. "Audio Signal Acquisition and Processing System Based on Model DSP Rapid Design." Security and Communication Networks 2022 (May 19, 2022): 1–9. http://dx.doi.org/10.1155/2022/4593339.

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When using DSP technology, technicians can easily and conveniently replace DSP audio processors, make second-party equipment, improve processor performance, reduce application costs, and receive and make music to meet the needs of different signals. Introduces knowledge of speech comprehension, which includes theoretical and cognitive processes, such as pre-speech characterization, final discovery, behavior, structure, and knowledge. By analyzing and comparing various common features, Mel frequency cepstrum coefficient is used to determine the physical features, and the results of traditional

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