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Journal articles on the topic 'Implementation and Optimization'

Author: Grafiati

Published: 25 May 2024

Last updated: 19 July 2025

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1

Bassil, Youssef. "Implementation of Combinatorial Algorithms using Optimization Techniques." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (2019): 660–66. http://dx.doi.org/10.31142/ijtsrd22925.

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2

Ballantyne, Michael, Mitch Gamburg, and Jason Hemann. "Compiled, Extensible, Multi-language DSLs (Functional Pearl)." Proceedings of the ACM on Programming Languages 8, ICFP (2024): 64–87. http://dx.doi.org/10.1145/3674627.

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Implementations of domain-specific languages should offer both extensibility and performance optimizations. With the new syntax-spec metalanguage in Racket, programmers can easily create DSL implementations that are both automatically macro-extensible and subject to conventional compiler optimizations. This pearl illustrates this approach through a new implementation of miniKanren, a widely used relational programming DSL. The miniKanren community has explored, in separate implementations, optimization techniques and a wide range of extensions. We demonstrate how our new miniKanren implementation with syntax-spec reconciles these features in a single implementation that comes with both an optimizing compiler and an extension mechanism. Furthermore, programmers using the new implementation benefit from the same seamless integration between Racket and miniKanren as in existing shallow embeddings.

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3

Gianamore, David. "Advanced control implementation optimization." ISA Transactions 30, no. 2 (1991): 69–74. http://dx.doi.org/10.1016/0019-0578(91)90042-4.

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4

Youssef, Bassil. "Implementation of Combinatorial Algorithms using Optimization Techniques." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 660–66. https://doi.org/10.31142/ijtsrd22925.

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In theoretical computer science, combinatorial optimization problems are about finding an optimal item from a finite set of objects. Combinatorial optimization is the process of searching for maxima or minima of an unbiased function whose domain is a discrete and large configuration space. It often involves determining the way to efficiently allocate resources used to find solutions to mathematical problems. Applications for combinatorial optimization include determining the optimal way to deliver packages in logistics applications, determining taxis best route to reach a destination address, and determining the best allocation of jobs to people. Some common problems involving combinatorial optimizations are the Knapsack problem, the Job Assignment problem, and the Travelling Salesman problem. This paper proposes three new optimized algorithms for solving three combinatorial optimization problems namely the Knapsack problem, the Job Assignment problem, and the Traveling Salesman respectively. The Knapsack problem is about finding the most valuable subset of items that fit into the knapsack. The Job Assignment problem is about assigning a person to a job with the lowest total cost possible. The Traveling Salesman problem is about finding the shortest tour to a destination city through travelling a given set of cities. Each problem is to be tackled separately. First, the design is proposed, then the pseudo code is created along with analyzing its time complexity. Finally, the algorithm is implemented using a high level programming language. As future work, the proposed algorithms are to be parallelized so that they can execute on multiprocessing environments making their execution time faster and more scalable. Youssef Bassil "Implementation of Combinatorial Algorithms using Optimization Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22925.pdf

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Vaughn, Susan. "Optimization Education After Project Implementation." Journal for Nurses in Staff Development (JNSD) 27, no. 2 (2011): E1—E4. http://dx.doi.org/10.1097/nnd.0b013e31820eefe4.

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6

Lindquist, Will, David Darwin, JoAnn Browning, Heather A. K. McLeod, Jiqiu Yuan, and Diane Reynolds. "Implementation of concrete aggregate optimization." Construction and Building Materials 74 (January 2015): 49–56. http://dx.doi.org/10.1016/j.conbuildmat.2014.10.027.

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7

Abed, Sa’ed, Reem Jaffal, Bassam Mohd, and Mohammad Alshayeji. "FPGA Modeling and Optimization of a SIMON Lightweight Block Cipher." Sensors 19, no. 4 (2019): 913. http://dx.doi.org/10.3390/s19040913.

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Security of sensitive data exchanged between devices is essential. Low-resource devices (LRDs), designed for constrained environments, are increasingly becoming ubiquitous. Lightweight block ciphers provide confidentiality for LRDs by balancing the required security with minimal resource overhead. SIMON is a lightweight block cipher targeted for hardware implementations. The objective of this research is to implement, optimize, and model SIMON cipher design for LRDs, with an emphasis on energy and power, which are critical metrics for LRDs. Various implementations use field-programmable gate array (FPGA) technology. Two types of design implementations are examined: scalar and pipelined. Results show that scalar implementations require 39% less resources and 45% less power consumption. The pipelined implementations demonstrate 12 times the throughput and consume 31% less energy. Moreover, the most energy-efficient and optimum design is a two-round pipelined implementation, which consumes 31% of the best scalar’s implementation energy. The scalar design that consumes the least energy is a four-round implementation. The scalar design that uses the least area and power is the one-round implementation. Balancing energy and area, the two-round pipelined implementation is optimal for a continuous stream of data. One-round and two-round scalar implementations are recommended for intermittent data applications.

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Liu, Guoxiao, Keting Jia, Puwen Wei, and Lei Ju. "High-Performance Hardware Implementation of MPCitH and Picnic3." IACR Transactions on Cryptographic Hardware and Embedded Systems 2024, no. 2 (2024): 190–214. http://dx.doi.org/10.46586/tches.v2024.i2.190-214.

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Picnic is a post-quantum digital signature, the security of which relies solely on symmetric-key primitives such as block ciphers and hash functions instead of number theoretic assumptions. One of the main concerns of Picnic is the large signature size. Although Katz et al.’s protocol (MPCitH-PP) significantly reduces the size of Picnic, the involvement of more parties in MPCitH-PP leads to longer signing/verification times and more hardware resources. This poses new challenges for implementing high-performance Picnic on resource-constrained FPGAs. So far as we know, current works on the hardware implementation of MPCitH-based signatures are compatible with 3 parties only. In this work, we investigate the optimization of the implementation of MPCitH-PP and successfully deploying MPCitH-PP with more than three parties on resource-constrained FPGAs, e.g., Xilinx Artix-7 and Kintex-7, for the first time. In particular, we propose a series of optimizations, which include pipelining and parallel optimization for MPCitH-PP and the optimization of the underlying symmetric primitives. Besides, we make a slight modification to the computation of the offline commitment, which can further reduce the number of computations of Keccak. These optimizations significantly improve the hardware performance of Picnic3. Signing messages on our FPGA takes 0.047 ms for the L1 security level, outperforming Picnic1 with hardware by a factor of about 5.3, which is the fastest implementation of post-quantum signatures as far as we know. Our FPGA implementation for the L5 security level takes 0.146 ms beating Picnic1 by a factor of 8.5, and outperforming Sphincs by a factor of 17.3.

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., Sharath D. "OPTIMIZATION AND IMPLEMENTATION OF PARALLEL SQUARER." International Journal of Research in Engineering and Technology 03, no. 06 (2014): 423–29. http://dx.doi.org/10.15623/ijret.2014.0306078.

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10

Dai, Guangming, Maocai Wang, Lei Peng, and Ruijie Qin. "Implementation And Optimization For Tate Pairing." Intelligent Automation & Soft Computing 17, no. 5 (2011): 607–17. http://dx.doi.org/10.1080/10798587.2011.10643174.

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11

Liu, Yufeng, Xiangyang Xu, and Hao Su. "AES Algorithm Optimization and FPGA Implementation." IOP Conference Series: Earth and Environmental Science 267 (June 8, 2019): 042070. http://dx.doi.org/10.1088/1755-1315/267/4/042070.

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12

Chen, Xueyu, Ralph W. Pike, Thomas A. Hertwig, and Jack R. Hopper. "Optimal implementation of on-line optimization." Computers & Chemical Engineering 22 (March 1998): S435—S442. http://dx.doi.org/10.1016/s0098-1354(98)00085-4.

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13

Well, K. H., and K. Ebert. "Trajectory Optimization Techniques and Software Implementation." IFAC Proceedings Volumes 25, no. 22 (1992): 77–87. http://dx.doi.org/10.1016/s1474-6670(17)49637-6.

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14

Pershin, Yuriy V., and Massimiliano Di Ventra. "Memcomputing Implementation of Ant Colony Optimization." Neural Processing Letters 44, no. 1 (2016): 265–77. http://dx.doi.org/10.1007/s11063-016-9497-y.

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15

Noordin, Nurul Hazlina, Phuah Soon Eu, and Zuwairie Ibrahim. "FPGA Implementation of Metaheuristic Optimization Algorithm." e-Prime - Advances in Electrical Engineering, Electronics and Energy 6 (December 2023): 100377. http://dx.doi.org/10.1016/j.prime.2023.100377.

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16

Narendra Maddukuri. "Workflow optimization for mobile computing." International Journal of Science and Research Archive 14, no. 1 (2025): 340–49. https://doi.org/10.30574/ijsra.2025.14.1.0048.

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Mobile workflow optimization has emerged as a critical factor in modern business operations, transforming how organizations manage and execute their processes across distributed environments. This comprehensive article explores the fundamental components, implementation strategies, and future directions of mobile workflow optimization. The article examines cloud integration, artificial intelligence implementation, security frameworks, and technical considerations that drive successful mobile workflow deployments. Through detailed analysis of real-world implementations, the research demonstrates how organizations improve operational efficiency, user satisfaction, and resource utilization while maintaining robust security measures. The investigation encompasses development methodologies, monitoring strategies, and emerging technologies reshaping the mobile workflow landscape, providing insights into best practices and future considerations for organizations pursuing the mobile-first operational strategy.

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17

Yao, Wei Wei, Liang Gong, and Xia Xu. "Design and Implementation of Security Network Optimization." Advanced Materials Research 977 (June 2014): 507–10. http://dx.doi.org/10.4028/www.scientific.net/amr.977.507.

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Optimization of computer network security technology shift in the relationship is constantly updated. Any computer network security solutions can not be designed once and for all. How seamless integration of network security technology and network security management together to make the network security implementation strategy is very important, this paper proposes a reasonable solution to improve overall network design and optimization of the security system, to develop scientific, reliable, dynamic network security optimization design, the design of enterprise network security solutions, combined with the current firewall, intrusion detection / intrusion prevention technology, VPN technology to achieve rapid escalation of the enterprise information construction in the network security system, thus the basic realization of computer network security optimization design goal.

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18

Han, Ming Hong. "Implementation and Validation of an Improved Collaborative Optimization Method." Advanced Materials Research 143-144 (October 2010): 1445–49. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.1445.

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Collaborative optimization (CO) is the most widely used Multidisciplinary design optimization (MDO) method for the design of complex engineering system. But some serious computational difficulties are found in its application. Reasons that cause computational difficulties in original CO were analyzed and a new improved collaborative optimization method (ICO) was presented. The L1 norm was used to improve subsystem consistency constraint and to avoid discontinuities in subsystem object function derivatives. Penalty function was added to system-level object function to convert constrained optimization into unconstrained optimization. A quick-start strategy was used to make the best use of optimal solution of system-level optimization in subsystem-level optimization. Experimental results show that the robustness, reliability and computing efficiency of ICO are higher than CO.

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19

HOTHAZIE, Mihai-Vladut, Georgiana ICHIM, and Mihai-Victor PRICOP. "Development and validation of constraints handling in a Differential Evolution optimizer." INCAS BULLETIN 12, no. 1 (2020): 59–66. http://dx.doi.org/10.13111/2066-8201.2020.12.1.6.

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Research work requires independent, portable optimization tools for many applications, most often for problems where derivability of objective functions is not satisfied. Differential evolution optimization represents an alternative to the more complex, encryption based genetic algorithms. Various packages are available as freeware, but they lack constraints handling, while constrained optimizations packages are commercially available. However, the literature devoted to constraints treatment is significant and the current work is devoted to the implementation of such an optimizer, to be applied in low-fidelity optimization processes. The parameter free penalty scheme is adopted for implementation, and the code is validated against the CEC2006 benchmark test problems and compared with the genetic algorithm in MATLAB. Our paper underlines the implementation of constrained differential evolution by varying two parameters, a predefined parameter for feasibility and the scaling factor, to ensure the convergence of the solution.

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20

Brekalo, Sanja, Klaudio Pap, and Bruno Trstenjak. "Enhancing Rendering Performance in Complex Visualizations by using Optimization Techniques and Algorithms in Browser Environments." Engineering, Technology & Applied Science Research 14, no. 3 (2024): 14049–55. http://dx.doi.org/10.48084/etasr.7201.

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This research is based on the hypothesis that optimization techniques can significantly improve the performance of complex visualizations in web browsers. The aim of the former was to determine to which extent the optimization can be achieved. Optimizations were coded to improve visualization, reduce the need for visual rendering, and decrease script execution time as well as the needed resources. To test the hypothesis, various optimization methods and algorithms were implemented on the initial visualization script and were tested. The main goal of this implementation was to assess how optimization methods, including quadtrees, spatial hashing, binning, LOD adjustments, and the use of the map data structure, affect the performance of web visualization. The obtained results confirmed the hypothesis and the original animation was significantly improved. The implementation of optimizations had a positive effect on the performance of visualizations. The conducted tests gave concrete evidence confirming the validity of the initial hypothesis. This led to certain conclusions regarding which methods provide the best results when optimizing complex visualizations. Key recommendations for code optimization, which can be used in the development of complex visualizations in web browsers, were derived.

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21

Zhang, Yi, Pengtao Liu, and Yanhong Li. "Implementation of an Enhanced Crayfish Optimization Algorithm." Biomimetics 9, no. 6 (2024): 341. http://dx.doi.org/10.3390/biomimetics9060341.

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This paper presents an enhanced crayfish optimization algorithm (ECOA). The ECOA includes four improvement strategies. Firstly, the Halton sequence was used to improve the population initialization of the crayfish optimization algorithm. Furthermore, the quasi opposition-based learning strategy is introduced to generate the opposite solution of the population, increasing the algorithm’s searching ability. Thirdly, the elite factor guides the predation stage to avoid blindness in this stage. Finally, the fish aggregation device effect is introduced to increase the ability of the algorithm to jump out of the local optimal. This paper performed tests on the widely used IEEE CEC2019 test function set to verify the validity of the proposed ECOA method. The experimental results show that the proposed ECOA has a faster convergence speed, greater performance stability, and a stronger ability to jump out of local optimal compared with other popular algorithms. Finally, the ECOA was applied to two real-world engineering optimization problems, verifying its ability to solve practical optimization problems and its superiority compared to other algorithms.

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22

Kim, Minsang, Yongmin Park, Byung-O. Kim, and Tae-Hwan Kim. "Optimization of Dehazing Method for Efficient Implementation." Journal of the Institute of Electronics and Information Engineers 53, no. 10 (2016): 58–65. http://dx.doi.org/10.5573/ieie.2016.53.10.058.

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23

Qu, Feng Juan, Hui Zhao, and Quan Bo Yuan. "Strategy and Implementation of System Performance Optimization." Advanced Materials Research 383-390 (November 2011): 2276–79. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2276.

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Currently in various fields systems are widely used. With the increasing of visitors, the number of concurrent processes raise as System response. In order to ensure system stability for business support, this article analyses data management strategy, database design strategy, application server design strategy, web server design strategy, and implements the enhancing system performance.

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24

Dhabliya, Mr Dharmesh. "Cloud Computing Security Optimization via Algorithm Implementation." International Journal of New Practices in Management and Engineering 10, no. 01 (2021): 22–24. http://dx.doi.org/10.17762/ijnpme.v10i01.99.

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There has been a growing need for significant improvements in cloud computing security ? to ensure that operations and data interactions in the cloud keep abreast with the dynamic nature of information technology. Motivated by the quest for cloud computing security, this paper has examined different approaches that most of the previous scholarly investigations (which focus on the adoption of machine learning in cloud computing security) have proposed ? towards better threat detection. The paper has begun with a general algorithm responsible for establishing the summation of risk levels before proceeding to more advanced algorithms through which threats to cloud data could be determined. Imperative to note is that the advanced approaches have been found to embrace anomaly detection and signature detection, translating into a proposed hybrid model for threat detection in the cloud. Whereas a major weakness is that the proposed model is not compared to another competitive model, its strength lies in the capacity to give an insight into ways in which certain time frames and profile categories could be specified, leading to a better classification of cloud user profiles and the eventual detection of anomalies.

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25

Pechac, Peter, Milan Saga, Ardeshir Guran, and Leszek Radziszewski. "Implementation of Memetic Algorithms into Structural Optimization." Communications - Scientific letters of the University of Zilina 18, no. 1A (2016): 64–69. http://dx.doi.org/10.26552/com.c.2016.1a.64-69.

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26

Kim, Jayul, Jiheun Ryu, and Daegab Gweon. "Parametric Optimization for High Speed FLIM Implementation." MATEC Web of Conferences 32 (2015): 04011. http://dx.doi.org/10.1051/matecconf/20153204011.

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27

Tao, Mu, Xi Chen, and Dan Qian. "Letters and Visits System Optimization and Implementation." Journal of Software Engineering and Applications 17, no. 09 (2024): 731–39. http://dx.doi.org/10.4236/jsea.2024.179038.

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28

Wang, Kelvin C. P., John Zaniewski, and James Delton. "Microcomputer Implementation of Pavement Network Optimization System." Journal of Computing in Civil Engineering 7, no. 4 (1993): 495–510. http://dx.doi.org/10.1061/(asce)0887-3801(1993)7:4(495).

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29

Kirsch, Uri. "Implementation of combined approximations in structural optimization." Computers & Structures 78, no. 1-3 (2000): 449–57. http://dx.doi.org/10.1016/s0045-7949(00)00093-6.

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30

Gangadhara Sai, Pirati, Chapala Sandhya Rani, and Usha Rani Nelakuditi. "Implementation of Power Optimization Technique for UAVs." Materials Today: Proceedings 5, no. 1 (2018): 132–37. http://dx.doi.org/10.1016/j.matpr.2017.11.063.

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31

Padula, S. L., and S. C. Stone. "Parallel implementation of large-scale structural optimization." Structural Optimization 16, no. 2-3 (1998): 176–85. http://dx.doi.org/10.1007/bf01202828.

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32

Tan, Jian Ding, Siaw Paw Koh, Mau Teng Au, Sieh Kiong Tiong, and Kharudin Ali. "Implementation of Voltage Optimization for Sustainable Energy." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (2018): 341. http://dx.doi.org/10.11591/ijeecs.v12.i1.pp341-347.

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<p>The voltage control in the power distribution network is challenged firstly by constantly increasing in power demand and secondly by a growing number of distributed connections, which significantly changes the load flow in the network. Situation is worsening with the current heavily implemented micro-generation of Solar PV. Consequently, the uncontrollable rapid changes in the power distribution network would affect voltage instability at the feeder with wide area power demand. Voltage instability would affect the energy efficiency of the electrical equipment. Moreover, the lifespan of some equipment would be shortened due to the excessive and unstable voltage supplied. The proposed research aims to implement the low impedance voltage optimization system in solving the existing problem. The voltage optimization system had been tested on both resistive and inductive loads. The power consumption of the loads had been measured at a few discrete values of voltage optimization within the statutory region. Further tests were conducted on industrial water pump, general lightings with mixed loads, office building, restaurant and plastic injection moulding machine. The power measurements were logged and the results in term of power consumption were analysed. The results showed that the proposed optimization mechanism successfully optimize and saved 6.81%, 14.42%, 13.97%, 12.23%, and 26.23% of the power consumptions in respective tests.</p>

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33

Rosenow, Ulf F., and Jadwiga B. Wojcicka. "Clinical implementation of stereotaxic brain implant optimization." Medical Physics 18, no. 2 (1991): 266–72. http://dx.doi.org/10.1118/1.596712.

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34

Padula, S. L., and S. C. Stone. "Parallel implementation of large-scale structural optimization." Structural Optimization 16, no. 2 (1998): 176. http://dx.doi.org/10.1007/s001580050018.

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35

AlJame, Maryam, Imtiaz Ahmad, and Mohammad Alfailakawi. "Apache Spark Implementation of Whale Optimization Algorithm." Cluster Computing 23, no. 3 (2020): 2021–34. http://dx.doi.org/10.1007/s10586-020-03162-7.

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36

Amirkhanov, I. R., E. S. Kaleeva, and D. A. Fatun. "Real-time optimization system: methods and implementation." Automation and informatization of the fuel and energy complex, no. 2 (2023): 13–19. http://dx.doi.org/10.33285/2782-604x-2023-2(595)-13-19.

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37

Randall, Marcus, and Andrew Lewis. "A Parallel Implementation of Ant Colony Optimization." Journal of Parallel and Distributed Computing 62, no. 9 (2002): 1421–32. http://dx.doi.org/10.1006/jpdc.2002.1854.

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38

Jian, Ding Tan, Paw Koh Siaw, Teng Au Mau, Kiong Tiong Sieh, and Ali Kharudin. "Implementation of Voltage Optimization for Sustainable Energy." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (2018): 341–47. https://doi.org/10.11591/ijeecs.v12.i1.pp341-347.

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The voltage control in the power distribution network is challenged firstly by constantly increasing in power demand and secondly by a growing number of distributed connections, which significantly changes the load flow in the network. Situation is worsening with the current heavily implemented microgeneration of Solar PV. Consequently, the uncontrollable rapid changes in the power distribution network would affect voltage instability at the feeder with wide area power demand. Voltage instability would affect the energy efficiency of the electrical equipment. Moreover, the lifespan of some equipment would be shortened due to the excessive and unstable voltage supplied. The proposed research aims to implement the low impedance voltage optimization system in solving the existing problem. The voltage optimization system had been tested on both resistive and inductive loads. The power consumption of the loads had been measured at a few discrete values of voltage optimization within the statutory region. Further tests were conducted on industrial water pump, general lightings with mixed loads, office building, restaurant and plastic injection moulding machine. The power measurements were logged and the results in term of power consumption were analysed. The results showed that the proposed optimization mechanism successfully optimize and saved 6.81%, 14.42%, 13.97%, 12.23%, and 26.23% of the power consumptions in respective tests.

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39

Baraldi, Giovanni L., Carlos S. B. Dias, Francisco M. C. Silva, Hélio C. N. Tolentino, and Eduardo X. Miqueles. "Fast reconstruction tools for ptychography at Sirius, the fourth-generation Brazilian synchrotron." Journal of Applied Crystallography 53, no. 6 (2020): 1550–58. http://dx.doi.org/10.1107/s1600576720013886.

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Described here are image reconstruction optimizations for ptychographic coherent X-ray scattering data and X-ray fluorescence, which have been developed for the new fourth-generation synchrotron light source, Sirius, at the Brazilian Synchrotron Light Laboratory. The optimization strategy has been applied to the standard experimental strategy for ptychographic and fluorescence experiments on the Carnaúba beamline which involves the use of high-speed continuous scans (fly scans) for a fast acquisition time over large areas through the use of a newly proposed trajectory named the alternating linear trajectory. The scientific computing developments presented here target an efficient use of graphical processing units (GPUs) to the point where large fly-scan acquisitions can be processed in real time on a local high-performance computer. Some optimizations involving a custom fast Fourier transform implementation and use of mixed precision can be applied to other algorithms and phase-retrieval techniques, and therefore this work provides a general optimization scheme. Finally, the optimization strategy presented here has improved performance by a factor of ∼2.5 times faster when compared with non-optimized GPU implementations.

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40

Shi, Wei Yi, and Yun Fu Shen. "An Algorithm Design and Implementation of Connect6." Advanced Materials Research 546-547 (July 2012): 1501–6. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1501.

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The new Connect6 game was launched and received the widespread attention in recent years. For the problems existing in pattern judgment way and in 6-8 windows in some literatures, the algorithm of Connect6 is studied by considering the attack and defensive characteristics of the Connect6. By the new 6-8 window approach proposed here, the evaluation function which determines the evaluation value is designed and implemented by calculating the changing quantity of the assessed values of the chessboard before and after the falling a stone. Based on this, an optimal Connect6 program is designed and implemented based on the PVS algorithm and the transposition table optimization, history heuristic optimization, null-move pruning optimization as well as iterative deepening optimization. The pruning rate of nodes in game tree comes to more than 98%. The actual playing of the chess indicates that the program has the strong chess strength.

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41

Blaž, Janez, Klemen Zajc, Samo Zupan, and Miha Ambrož. "Evaluation System for the Implementation of Public Passenger Transport as a Public Service Obligation." Sustainability 11, no. 12 (2019): 3294. http://dx.doi.org/10.3390/su11123294.

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The research in this article relates to an evaluation system for the implementation of lines in a public passenger transport system as a public service obligation. The purpose of this research is to present the methodology for the performance evaluation of a public passenger transport system that serves the public transport authority as a tool for making further decisions. The procedures first refer to the experimental determination of the criteria and then perform the first evaluation in the form of the value of the objective function. This is followed by multi-stage linear regression and optimization procedures that give the relation between the dependent variable ( Y ) and the independent variables ( X , ) that is, the criteria. Optimization is carried out in the coefficients ( β ), which are excluded from the optimization procedures in the case of the calculated statistical degree of insignificance. This research also shows procedures for changing the mathematical form of the criteria records and determining the impact on the final result of optimizations.

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42

Milenković, Branislav. "Implementation of Harris Hawks Optimization (HHO) algorithm to solve engineering problems." Tehnika 76, no. 4 (2021): 439–46. http://dx.doi.org/10.5937/tehnika2104439m.

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Recently, optimization techniques have become very important and popular in different engineering applications. In this paper we demonstrate how Harris Hawks Optimization (HHO) algorithm can be used to solve certain optimization problems in engineering. In the second part, biological fundamentals, as well as method explanation are given. Afterwards, the HHO algorithm and its' applicability is explained in detail. The pseudo code for this algorithm was written using MATLAB R2019a software suite. Harris Hawks Optimization (HHO) algorithm was used for optimization of engineering problems, such as: speed reducer optimization, pressure vessel optimization, cantilever beam optimization and tension/compression spring optimization. The statistical results and comparisons show that the HHO algorithm provides very promising and competitive results compared to others metaheuristic algorithms.

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43

Pusztai, Zoltán, Péter Kőrös, Ferenc Szauter, and Ferenc Friedler. "Implementation of Optimized Regenerative Braking in Energy Efficient Driving Strategies." Energies 16, no. 6 (2023): 2682. http://dx.doi.org/10.3390/en16062682.

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In this paper, determination of optimized regenerative braking-torque function and application in energy efficient driving strategies is presented. The study investigates a lightweight electric vehicle developed for the Shell Eco-Marathon. The measurement-based simulation model was implemented in the MATLAB/Simulink environment and used to establish the optimization. The optimization of braking-torque function was performed to maximize the recuperated energy. The determined braking-torque function was applied in a driving strategy optimization framework. The extended driving strategy optimization model is suitable for energy consumption minimization in a designated track. The driving strategy optimization was created for the TT Circuit Assen, where the 2022 Shell Eco-Marathon competition was hosted. The extended optimization resulted in a 2.97% improvement in energy consumption when compared to the result previously achieved, which shows the feasibility of the proposed methodology and optimization model.

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C.Kavitha, C. Kavitha, and C. Vijayalakshmi C. Vijayalakshmi. "Design and Implementation of Fuzzy Multi Objective Optimization Model for Production Planning." Indian Journal of Applied Research 3, no. 12 (2011): 372–75. http://dx.doi.org/10.15373/2249555x/dec2013/113.

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Amol Ananda Gore, Amol Ananda Gore, and Prof V. V. Deotare Prof. V. V. Deotare. "Implementation of Aes For Image Cryptography Process on Sopc with Area Optimization." Indian Journal of Applied Research 3, no. 7 (2011): 190–92. http://dx.doi.org/10.15373/2249555x/july2013/59.

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Sheijani, Omid Sojoodi, and Ali Izadi. "Time Optimization during Software Implementation for Timely Delivery Using Meta-Heuristic Algorithms." International Journal of Machine Learning and Computing 9, no. 5 (2019): 581–85. http://dx.doi.org/10.18178/ijmlc.2019.9.5.843.

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Grover, Garima, and Ila Chaudhary. "Implementation of Particle Swarm Optimization Algorithm in VHDL for Digital Circuits Optimization." International Journal of Information Engineering and Electronic Business 6, no. 5 (2014): 16–21. http://dx.doi.org/10.5815/ijieeb.2014.05.03.

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Made Hanindia Prami Swari, Dio Farrel Putra Rachmawan, and Chrystia Aji Putra. "Multinomial Optimization of Naïve Bayes Through the Implementation of Particle Swarm Optimization." Technium: Romanian Journal of Applied Sciences and Technology 16 (October 29, 2023): 169–75. http://dx.doi.org/10.47577/technium.v16i.9977.

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Sentiment analysis is widely used in cases of text processing and comments. One of the case studies is about the analysis of a hotel review by the public. The method used in analyzing a sentiment from comments or reviews of a hotel is the Naïve Bayes Classifier. One that can be used is the Multinomial Naïve Bayes method. In improving the results of the accuracy of the method required an optimization method. There are many optimization methods that can be applied to algorithms in sentiment analysis case studies. One well-known method is Particle Swarm Optimization (PSO). This study aims to determine the effect of PSO optimization on the Multinomial Naïve Bayes algorithm in the case of sentiment analysis. From the results of optimization and model testing, the highest accuracy was obtained in the Multinomial Naïve Bayes test with PSO optimization as hyperparameter tunning and feature selection of 97%.

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Shao, Lei, Hai Bin Zuo, and Nan Liu. "GA Implementation Path Optimization of Pneumatic Marking System." Applied Mechanics and Materials 391 (September 2013): 390–93. http://dx.doi.org/10.4028/www.scientific.net/amm.391.390.

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According to the characteristics of pneumatic marking system, and the typing path was seen as a TSP problem. After comparing the Dijkstra optimization algorithm of marking path results, and applying the genetic algorithm (GA) to analysis, research, and solve the optimization problem, reasonable to get print needle typing path. In this case, printing mark time was shorten as much as possible. It was proved by MATLAB simulation that the study can solve the problem of path optimization and improve the efficiency of marking greatly.

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Researcher. "PERFORMANCE OPTIMIZATION TECHNIQUES IN REACT APPLICATIONS: A COMPREHENSIVE ANALYSIS." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1165–77. https://doi.org/10.5281/zenodo.14146734.

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Performance optimization remains a critical challenge in modern React applications, particularly as applications scale in complexity and user base. This comprehensive article analysis examines various optimization techniques across component-level, application-level, and data handling domains. The article presents a systematic evaluation of key optimization strategies including React.memo implementation, hook-based optimizations (useCallback, useMemo), code splitting with React.lazy and Suspense, and efficient large dataset management using React Virtualizer. Through detailed case studies of an e-commerce platform and a social media application, we demonstrate significant performance improvements: a 30% reduction in initial load times and enhanced user interaction responsiveness. The article identifies common implementation pitfalls and provides validated solutions for issues such as memoization overuse and inefficient component hierarchies. Performance metrics analysis reveals substantial improvements in load time, memory usage, and overall user experience. The findings provide a structured framework for implementing optimization strategies while balancing development complexity and maintenance overhead. This article contributes to the growing body of knowledge on React application optimization and offers practical guidelines for developers facing similar performance challenges.

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