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The application of a hybrid algorithm to roll pass design

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第5-8期79卷 1063-1070页

作者： Abhary, Kazem Kovacic, Zlatko Lundberg, Sven-Erik Narayanan, Ramadas Spuzic, Sead Univ S Australia Adelaide SA 5001 Australia Open Polytech New Zealand Lower Hutt New Zealand Assoc Int Roll Pass Designers & Rolling Mill Engi Dortmund Germany Cent Queensland Univ Bundaberg Qld Australia

Human-computer interface was utilized to amplify the power of a hybrid algorithm that combines fuzzy logic, regression analysis, and non-linear optimization to extract knowledge from a roll pass design database. This strategy is applied to grooves with vertical and horizontal symmetry which are employed in initial passes to roll a broad range of steel products. The results presented here from the case study are both statistically significant and valid from the viewpoint of roll pass design theory.

关键词： Roll pass design hybrid algorithm Knowledge extraction Regression analysis

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Improved hybrid algorithm for Real Time Sound Propagation using Intelligent Prioritization

Improved Hybrid Algorithm for Real Time Sound Propagation us...

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Mediterranean Electrotechnical Conference

作者： Panagiotis Charalampous Despina Michael Department of Multimedia and Graphics Arts Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9781509000593

We present a hybrid algorithm which combines image source method, ray tracing and intelligent prioritization for faster calculation of impulse responses to be used in real time sound rendering. We use the image source method to calculate sound reflections from specular surfaces and we use a prioritized ray tracing algorithm for fast detection and evaluation of valid image sources from the tree of candidate image sources. Our algorithm delivers improved acoustical results in most cases when compared to a non prioritized version of it, a ray tracing algorithm and a best first image source algorithms, indicating that prioritization can deliver performance gains in real time sound rendering methods.

关键词： Improved hybrid algorithm

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Optimal Adaptive Genetic algorithm Based hybrid Signcryption algorithm for Information Security

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Computer Modeling in Engineering & Sciences 2015年第5期105卷 47-68页

作者： R.Sujatha M.Ramakrishnan N.Duraipandian B.Ramakrishnan Velammal Engineering College Anna UniversityIndia School of Computer Applications Madurai Kamarajar UniversityIndia Department of Computer Science and Research Centre S.T.Hindu CollegeMadurai Kamarajar UniversityIndia

The functions of digital signature and public key encryption are simultaneously fulfilled by signcryption,which is a cryptographic *** securely communicate very large messages,the cryptographic primitive called signcryption efficiently implements the same and while most of the public key based systems are suitable for small messages,hybrid encryption(KEM-DEM)provides a competent and practical *** this paper,we develop a hybrid signcryption *** hybrid signcryption is based on the KEM and DEM *** KEM algorithm utilizes the KDF technique to encapsulate the symmetric *** DEM algorithm utilizes the Adaptive Genetic algorithm based Elliptic curve cryptography algorithm to encrypt the original ***,for the security purpose,we introduce the three games and we proved the attackers fail to find the security attributes of our proposed signcryption *** proposed algorithm is analyzed with Daniel of Service(DOS),Brute Force attack and Man In Middle(MIM)attacks to ensure the secure data transaction.

关键词： hybrid Signcryption KEM DEM Adaptive Genetic algorithm EllipticCurve Cryptography

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Greedy Constructive Procedure-Based hybrid Differential algorithm for Flexible Flow shop Group Scheduling

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Journal of Donghua University(English Edition) 2015年第4期32卷 577-582页

作者：郑永前于萌萌谢松杭 College of Mechanical Engineering Tongji University

Aiming at the flexible flowshop group scheduling problem,taking sequence dependent setup time and machine skipping into account, a mathematical model for minimizing makespan is established,and a hybrid differential evolution( HDE) algorithm based on greedy constructive procedure( GCP) is proposed,which combines differential evolution( DE) with tabu search( TS). DE is applied to generating the elite individuals of population,while TS is used for finding the optimal value by making perturbation in selected elite individuals. A lower bounding technique is developed to evaluate the quality of proposed algorithm. Experimental results verify the effectiveness and feasibility of proposed algorithm.

关键词： flexible flowshop group scheduling hybrid differential evolution(HDE) algorithm greedy constructive procedure(GCP) lower bound

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Multi-Objective Task Scheduling Using hybrid Whale Genetic Optimization algorithm in Heterogeneous Computing Environment

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WIRELESS PERSONAL COMMUNICATIONS 2020年第4期110卷 1887-1913页

作者： Natesan, Gobalakrishnan Chokkalingam, Arun Sathyabama Univ St Josephs Coll Engn Dept Informat Technol Chennai Tamil Nadu India RMK Coll Engn & Technol Dept Elect & Commun Engn Chennai Tamil Nadu India

The system of cloud computing comprises of several servers that are inter-connected in a datacenter, provisioned dynamically to cater on-demand services through the front-end interface for the clients. Improvement in virtualization technology has made cloud computing a viable option for various application services development. Cloud datacenters process the tasks on the basis of pay as you use manner. Task scheduling is one of the important research challenges in cloud computing. The formulation of task scheduling probes has been depicted to be NP-hard hence identifying the solution for a bigger problem is intractable. The dissimilar feature of cloud resources makes task scheduling non-trivial. NP-hard problem arises due to the dynamic behavior of the dissimilar resources identified in the cloud computing environment. Task scheduling can be optimized using a meta-heuristic algorithm. In this paper, we have combined two meta-heuristic techniques, namely Whale Optimization algorithm (WOA) and Genetic algorithm (GA) to devise a new hybridized algorithm called as Whale Genetic Optimization algorithm. Our aim is to minimize the makespan and cost while scheduling the tasks. The simulation is done by using Cloudsim toolkit. The results obtained shows significant reduction in the execution time that was measured in terms of enactment amelioration rate. These results were compared with the classical WOA and standard GA. The results of the proposed technique provide higher quality solution for task scheduling.

关键词： Cloud computing algorithm NP-hard Task scheduling hybrid Virtualization and optimization

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A genetic algorithm based improved optimal sizing strategy for solar-wind-battery hybrid system using energy filter algorithm

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Frontiers in Energy 2020年第1期14卷 139-151页

作者： Aeidapu MAHESH Kanwarjit Singh SANDHU Department of Electrical Engineering National Institute of TechnologyKurukshetra136119India

In this paper,the genetic algorithm(GA)is applied to optimize a grid connected solar photovoltaic(PV)-wind-battery hybrid system using a novel energy filter *** main objective of this paper is to minimize the total cost of the hybrid system,while maintaining its *** with the reliability constraint,some of the important parameters,such as full utilization of complementary nature of PV and wind systems,fluctuations of power injected into the grid and the battery’s state of charge(SOC),have also been considered for the effective sizing of the hybrid system.A novel energy filter algorithm for smoothing the power injected into the grid has been *** validate the proposed method,a detailed case study has been *** results of the case study for different cases,with and without employing the energy filter algorithm,have been presented to demonstrate the effectiveness of the proposed sizing strategy.

关键词： PV-wind-battery hybrid system size optimization genetic algorithm

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Using a hybrid of AI and Template-Based Method in Automatic Item Generation to Create Multiple-Choice Questions in Medical Education: hybrid AIG

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JMIR Formative Research 2025年 9卷 e65726页

作者： Kıyak, Yavuz Selim Kononowicz, Andrzej A. Department of Medical Education and Informatics Faculty of Medicine Gazi University Ankara Turkey Department of Bioinformatics and Telemedicine Jagiellonian University Medical College Kraków Poland

Background: Template-based automatic item generation (AIG) is more efficient than traditional item writing but it still heavily relies on expert effort in model development. While nontemplate-based AIG, leveraging artificial intelligence (AI), offers efficiency, it faces accuracy challenges. Medical education, a field that relies heavily on both formative and summative assessments with multiple choice questions, is in dire need of AI-based support for the efficient automatic generation of items. Objective: We aimed to propose a hybrid AIG to demonstrate whether it is possible to generate item templates using AI in the field of medical education. Methods: This is a mixed-methods methodological study with proof-of-concept elements. We propose the hybrid AIG method as a structured series of interactions between a human subject matter expert and AI, designed as a collaborative authoring effort. The method leverages AI to generate item models (templates) and cognitive models to combine the advantages of the two AIG approaches. To demonstrate how to create item models using hybrid AIG, we used 2 medical multiple-choice questions: one on respiratory infections in adults and another on acute allergic reactions in the pediatric population. Results: The hybrid AIG method we propose consists of 7 steps. The first 5 steps are performed by an expert in a customized AI environment. These involve providing a parent item, identifying elements for manipulation, selecting options and assigning values to elements, and generating the cognitive model. After a final expert review (Step 6), the content in the template can be used for item generation through a traditional (non-AI) software (Step 7). We showed that AI is capable of generating item templates for AIG under the control of a human expert in only 10 minutes. Leveraging AI in template development made it less challenging. Conclusions: The hybrid AIG method transcends the traditional template-based approach by marrying the “art” t

关键词： AI algorithm artificial intelligence automatic item generation ChatGPT expert human-AI human-AI collaboration hybrid hybrid AIG large language models medical education medical education mixed-method multiple-choice multiple-choice question template-based method

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Medical devices, smart drug delivery, wearables and technology for the treatment of Diabetes Mellitus

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ADVANCED DRUG DELIVERY REVIEWS 2022年 185卷 114280页

作者： Domingo-Lopez, Daniel A. Lattanzi, Giulia Schreiber, Lucien H. J. Wallace, Eimear J. Wylie, Robert O'Sullivan, Janice Dolan, Eimear B. Duffy, Garry P. Natl Univ Ireland Galway Coll Med Nursing & Hlth Sci Sch Med Anat & Regenerat Med Inst REMEDI Galway Ireland Natl Univ Ireland Galway Coll Sci & Engn Sch Engn Biomed Engn Galway Ireland Trinity Coll Dublin Adv Mat & Bioengn Res Ctr AMBER Dublin Ireland Natl Univ Ireland Galway Galway Ireland Natl Univ Ireland Galway Ctr Res Med Devices CURAM Galway Ireland

Diabetes mellitus refers to a group of metabolic disorders which affect how the body uses glucose impacting approximately 9% of the population worldwide. This review covers the most recent technological advances envisioned to control and/or reverse Type 1 diabetes mellitus (T1DM), many of which will also prove effective in treating the other forms of diabetes mellitus. Current standard therapy for T1DM involves multiple daily glucose measurements and insulin injections. Advances in glucose monitors, hormone delivery systems, and control algorithms generate more autonomous and personalised treatments through hybrid and fully automated closed-loop systems, which significantly reduce hypo-and hyperglycaemic episodes and their subsequent complications. Bi-hormonal systems that co-deliver glucagon or amylin with insulin aim to reduce hypoglycaemic events or increase time spent in target glycaemic range, respectively. Stimuli responsive materials for the controlled delivery of insulin or glucagon are a promising alternative to glucose monitors and insulin pumps. By their self-regulated mechanism, these "smart" drugs modulate their potency, pharmacokinetics and dosing depending on patients' glucose levels. Islet transplantation is a potential cure for T1DM as it restores endogenous insulin and glucagon production, but its use is not yet widespread due to limited islet sources and risks of chronic immunosuppression. New encapsulation strategies that promote angiogenesis and oxygen delivery while protecting islets from recipients' immune response may overcome current limiting factors. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Diabetes mellitus Medical devices Insulin Glucagon Closed-loop hybrid algorithm Glucose responsive Islet encapsulation

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A New Teaching-Learning-based Chicken Swarm Optimization algorithm

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SOFT COMPUTING 2020年第7期24卷 5313-5331页

作者： Deb, Sanchari Gao, Xiao-Zhi Tammi, Kari Kalita, Karuna Mahanta, Pinakeswar Indian Inst Technol Ctr Energy Gauhati India Univ Eastern Finland Sch Comp Joensuu Finland Aalto Univ Dept Mech Engn Espoo Finland Indian Inst Technol Dept Mech Engn Gauhati India Natl Inst Technol Dept Mech Engn Yupia Arunachal Prade India

Chicken Swarm Optimization (CSO) is a novel swarm intelligence-based algorithm known for its good performance on many benchmark functions as well as real-world optimization problems. However, it is observed that CSO sometimes gets trapped in local optima. This work proposes an improved version of the CSO algorithm with modified update equation of the roosters and a novel constraint-handling mechanism. Further, the work also proposes synergy of the improved version of CSO with Teaching-Learning-based Optimization (TLBO) algorithm. The proposed ICSOTLBO algorithm possesses the strengths of both CSO and TLBO. The efficacy of the proposed algorithm is tested on eight basic benchmark functions, fifteen computationally expensive benchmark functions as well as two real-world problems. Further, the performance of ICSOTLBO is also compared with a number of state-of-the-art algorithms. It is observed that the proposed algorithm performs better than or as good as many of the existing algorithms.

关键词： algorithm Benchmark Chicken Swarm Optimization Function hybrid Teaching-Learning-based Optimization

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A hybrid optimization approach for the heterogeneous vehicle routing problem with multiple depots cooperative operation

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High Technology Letters 2020年第1期26卷 108-117页

作者： Liu Jiansheng Tan Wenyue Jiang Hai Yu Gong School of Mechanical and Electronic Engineering Nanchang UniversityNanchang 330031P.R.China School of Mechanical Engineering Shanghai Jiaotong UniversityShanghai 200240P.R.China Southampton Business School University of SouthamptonSouthamptonSO171 BJUK

With the challenge of great growing of transport diversity for the automobile enterprises, the heterogeneous vehicle routing problem with multiple depots, multiple types of finished vehicles and multiple types of transport vehicles in finished vehicle logistics(HVRPMD) is modelled and solved. A multi-objective optimization model for HVRPMD is presented considering loading constraints to minimize the total cost and minimize the number of transport vehicles. Then a hybrid heuristic algorithm based on genetic algorithm and particle swarm optimization(GA-PSO) is developed. Moreover, a case study is used to evaluate the effectiveness of this algorithm. By comparing the GA-PSO algorithm with the traditional GA algorithm, the simulation results demonstrate the proposed GA-PSO algorithm is able to better support the HVRPMD problem in practice. Contributions of the paper are the modelling and solving of a complex HVRPMD in logistics industry.

关键词： finished vehicle logistics(FVL) vehicle routing problem(VRP) hybrid heuristic algorithm multiple factory depot

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