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检索条件"主题词=METAHEURISTIC algorithms"

共 1140 条记录，以下是101-110 订阅

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Application of metaheuristic algorithms for Determining the Structure of a Convolutional Neural Network with a Small Dataset 19

Application of Metaheuristic Algorithms for Determining the ...

引用

3rd International Conference on Computer Science and Application Engineering (CSAE)

作者： Shih, Po-Chou Li, Hui Lin, Xin Zhang, Yang Univ Shanghai Sci & Technol Sch Business Shanghai 200093 Peoples R China

ISBN: (纸本)9781450362948

Inadequately labeled data can limit the accuracy of classification in image recognition tasks. Several methods have been proposed in the past to alleviate this limitation, such as transfer learning and data augmentation. However, the classification accuracy of the convolutional neural network (CNN) largely depends on its structural parameters, which are known as hyper-parameters. Therefore, in this paper, we introduce another method for minimizing the misclassification rate in a given small dataset by determining the hyper-parameters. The harmony search (HS) algorithm, improved harmony search (IHS) algorithm, self-adaptive global best harmony search (SGHS) algorithm, and novel global harmony search (NGHS) algorithm are applied for determining the optimal hyper-parameters. Additionally, we also compared the estimation performances of these four HS algorithms. It was finally observed that the HS and the IHS algorithms greatly outperform the other two algorithms.

关键词： Convolutional neural network metaheuristic algorithms Harmony search algorithms Structure

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Low Delay Digital IIR Filter Design Using metaheuristic algorithms 2

Low Delay Digital IIR Filter Design Using Metaheuristic Algo...

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2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Maghsoudi, Yaser Kamandar, Mehdi Kerman Grad Univ Adv Technol Fac Elect & Comp Engn Kerman Iran

ISBN: (纸本)9781509043309

Digital IIR filter design by optimizing a fitness function with respect to coefficients of a filter with rational transfer function by meta-heuristic algorithms has been considered recently, Most researchers use a fitness function consisted of difference between magnitude response of desired filter and designed filter and the constraints such as linear phase, minimum phase and stability of designed filter. In this paper, a comprehensive fitness function for IIR digital filter design with 6 terms is proposed. A new term is added to fitness function to get a filter with low delay. Low delay filters are desirable for real time signal processing. This term is weighted partial energy of the impulse response of designed causal filter. Maximizing this term leads to concentration of energy of impulse response at its beginning, consequently a low delay filter. Low delay property leads to fast decaying of transient response and low delay between input and output of designed filter. Proposed fitness function also includes some terms to meet linear phase, minimum phase and stability constraints. Meta-heuristic optimization algorithms GA, GSA and PSO are used to optimize proposed fitness function. To evaluate efficiency of the proposed method, it will be used to design a low delay low pass filter and a low delay differentiator. Reported results show lower delay of designed filters by proposed method than designed ones by traditional methods.

关键词： Digital signal processing IIR digital filter design Weighted partial energy metaheuristic algorithms

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Comparative Study Of metaheuristic algorithms Using Knapsack Problem 7

Comparative Study Of Metaheuristic Algorithms Using Knapsack...

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7th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Sapra, Dikscha Sharma, Rashi Agarwal, Arun Prakash AUUP ASET Dept CSE Noida India AUUP Amity Sch Engn & Technol Noida India

ISBN: (纸本)9781509035199

This paper aims to discuss and compare various metaheuristic algorithms applied to the "Knapsack Problem". The Knapsack Problem is a combinatorial optimization maximization problem which requires to find the number of each weighted item to be included in a hypothetical knapsack, so the total weight is less than or equal to the required weight. To come to an optimized solution for such a problem, a variety of algorithms can possibly be used. In this paper, Tabu Search, Scatter Search and Local Search algorithms are compared taking execution time, solution quality and relative difference to best known quality, as metrics to compute the results of this NP-hard problem.

关键词： metaheuristic algorithms Knapsack Problem Local Search Tabu Search Scatter Search

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Performance Measures of metaheuristic algorithms 2nd

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2nd International Conference on Harmony Search Algorithm (ICHSA)

作者： Kim, Joong Hoon Lee, Ho Min Jung, Donghwi Sadollah, Ali Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea Korea Univ Res Ctr Disaster Prevent Sci & Technol Seoul 136713 South Korea

ISBN: (纸本)9783662479261;9783662479254

Generally speaking, it is not fully understood why and how metaheuristic algorithms work very well under what conditions. It is the intention of this paper to clarify the performance characteristics of some of popular algorithms depending on the fitness landscape of specific problems. This study shows the performance of each considered algorithm on the fitness landscapes with different problem characteristics. The conclusions made in this study can be served as guidance on selecting algorithms to the problem of interest.

关键词： Fitness landscape metaheuristic algorithms Nature-Inspired algorithms Optimization Performance measures

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LQR for Generalized Systems Using metaheuristic algorithms Based on Disturbance Observer 28

LQR for Generalized Systems Using Metaheuristic Algorithms B...

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28th Iranian Conference on Electrical Engineering (ICEE)

作者： Dehnavi, Vahid Safari Shafiee, Masoud Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran 158754413 Iran

ISBN: (纸本)9781728172965

In this paper, the linear quadratic regulator (LQR) optimal control of constrained dynamic systems for generalized systems with external disturbances is designed. At first, a generalized system is given, this model includes some ordinary differential equations (ODEs) and some algebraic equations as a constraint on dynamics. In this paper, we address the optimal LQR control for continuous linear generalized systems in which the system has only finite dynamic modes. In the LQR problem, we derive a Riccati equation in each case. Then a linear disturbance observer is proposed to estimate the disturbance. Then, we propose four types of metaheuristic algorithms such as grasshopper optimization algorithm (GOA), grey wolf optimizer (GWO), genetic algorithm (GA), and particle swarm optimization (PSO) with a suitable structure for solving Riccati equation. In each case, the solution obtained by GOA, GWO, GA, and PSO. Simulation results demonstrate the good performance of the proposed methods.

关键词： Linear quadratic regulator (LQR) metaheuristic algorithms Generalized systems

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BUILDING ENERGY OPTIMISATION USING MACHINE LEARNING AND metaheuristic algorithms

BUILDING ENERGY OPTIMISATION USING MACHINE LEARNING AND META...

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作者： Keivan Bamdad Masouleh Queensland University of Technology

学位级别：博士

Buildings consume approximately 40% of end-use energy worldwide and are responsible for approximately one-third of greenhouse gas (GHG) emissions. Clearly, designing high energy performance buildings and identifying effective energy retrofit measures not only decrease CO 2 emissions, but also reduce the need for non-renewable energy sources. While the traditional rules of thumb and building codes improve the building energy efficiency, they are likely to be far from the optimal design as they do not consider the interactions among design variables. Therefore, new methods should be developed to achieve the maximum energy savings. Building energy optimisation (BEO) is a method that considers interactions among design variables and selects the optimal building design from a set of available alternative designs based on the mathematics. A challenge of currently-available optimisation methods is that they suffer from high computational cost due to high complexities in building optimisation problems including multi-modal and nonlinear behaviour of building thermal performance, discontinuities in the optimisation variables (e. g. window type), uncertainty in building design parameters (e. g. alterations in building operating conditions) and discontinuities in the output of building simulation software (e. g. EnergyPlus). This high computational cost remains a key barrier to the widespread utilisation of optimisation as a design tool. Accordingly, the focus of this research is on developing new efficient solution methods for Building Optimisation Problems (BOPs) and deploying them on realistic case studies to evaluate their performance and utility. Generally, BOPs can be categorised into two main groups: simulation-based optimisation (software-in-the- loop method) and surrogate-based optimisation methods. In this thesis, new methods were developed to improve the performance of both methods. Furthermore, a new methodology was developed to address uncertainty of building simu

关键词： Active learning methods ant colony optimisation artificial neural networks building energy optimisation building energy simulation metaheuristic algorithms particle swarm optimisation robust design sample selection method simulation-based optimisation method surrogate-based optimisation method uncertainty analysis

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Systematic Literature Review on metaheuristic algorithms in SNP Analysis for Diseases 6

Systematic Literature Review on Metaheuristic Algorithms in ...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Sulistyawan, Jason Sebastian Nugroho, Kuncahyo Setyo Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

Single-nucleotide polymorphism (SNP) analysis has become a pivotal strategy for drug discovery within bioinformatics, especially for incurable diseases like cancer. With the increasing number of researchers starting to embrace metaheuristic methods, a review was done by gathering papers from the Google Scholar database from 2018 to 2023, which resulted in 20 papers after title, abstract, and content filtering. The findings show that the Genetic Algorithm and the Harmony Search Algorithm have become popular approaches in SNP analysis, particularly in studies on breast cancer, age-related macular degeneration, and colorectal cancer. However, the review shows that while researchers have proven most methods effective in finding disease-related SNPs, a more measurable study in SNP analysis is needed, due to the lack of elaboration on measurement metrics in the found studies. © 2024 IEEE.

关键词： metaheuristic algorithms single nucleotide polymorphisms

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A Review on metaheuristic algorithms Employed in WSN

A Review on Metaheuristic Algorithms Employed in WSN

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Al-Ani, Khalid W. University of Anbar Department of Studies and Planning Ramadi Iraq

ISBN: (纸本)9798350372229

In recent years, Wireless Sensor Network (WSN) is in demand over the world due to its rapid deployment in a variety of applications. The challenges in the WSN like limited battery power and communication range caught the attention of the researchers to continue improving network efficiency and extend the lifetime. metaheuristic algorithms have proven efficiency in solving WSN challenges. This paper begins with an introduction about WSN, and its structure, then discusses the optimization challenges in WSN. The paper also presents the most prominent metaheuristic algorithms, their operating mechanisms, and limitations. Finally, the paper analysis of the state of art WSN metaheuristic algorithms. The studies show that using metaheuristic algorithms to solve WSN problems gave a promising result. © 2024 IEEE.

关键词： energy efficiency lifetime metaheuristic algorithms WSN

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Comparative Study of Five metaheuristic algorithms for Team Formation Problem

Comparative Study of Five Metaheuristic Algorithms for Team ...

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Human Engineering Symposium, HUMENS 2021

作者： Kader, Md. Abdul Zamli, Kamal Z. Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pekan Pahang26600 Malaysia

ISBN: (纸本)9789811641145

This paper presents a comparative study of five metaheuristic algorithms, namely, salp swarm algorithm (SSA), owl search algorithm (OSA), sooty tern optimization algorithm (STOA), squirrel search algorithm (SqSA), and crow search algorithm (CSA) adopted in the Covid19 team formation (CTF) problem. The performance comparison of these algorithms is conducted by executing each algorithm twenty times to ensure the statistical significance. The study considers the minimum number of experts and the minimum team formation cost in defining the objective function. The CSA was found to be the more effective metaheuristic algorithm for the Covid19 team formation problem from the optimal results in terms of overall solution quality and runtime efficiency. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Covid19 team formation Crow search algorithm metaheuristic algorithms

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Project Matching Application Framework Using metaheuristic algorithms

Project Matching Application Framework Using Metaheuristic A...

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作者： Jonathan Nilsson CHALMERS UNIVERSITY OF TECHNOLOGY

学位级别：硕士

Timetabling is something that a lot of businesses, public sectors and institutions have to deal with, which is probably why automating the process of creating good timetables is a well-known problem that has been well researched during the re- cent decades. This thesis introduces a timetabling problem named project matching problem, which is a problem of assigning workers to projects and schedule these projects into a limited time frame, while respecting certain constraints such as the workers preference towards these projects. In order to efficiently solve instances of this problem, an application framework based on the metaheuristic algorithm Iter- ated local search is developed and implemented using common software techniques and architectural patterns. As the project matching problem has a broad definition, the idea is that the area of use for this application framework comprises timetabling problems outside the domain of project organisation. The results are based on running these problem instances through the application framework, where the results of these runs are presented in terms of both solution quality and algorithmic efficiency. The solution quality is based on analyses of the actual solutions that is produced while algorithmic efficiency is measured on the execution time of the algorithm, which offers a suggestion of how the solution quality improves as the execution time increases. For one of the problem instances this application framework produced an optimal solution, which offers an indication on the applicability of this framework.

关键词： metaheuristic algorithms Combinatorial optimisation Iterated local search Automation framework C#/. NET

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