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Relevant bibliographies by topics / Bee Swarm Optimization (BSO) / Dissertations / Theses

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Dissertations / Theses on the topic 'Bee Swarm Optimization (BSO)'

Author: Grafiati

Published: 3 June 2025

Last updated: 19 July 2025

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1

Tatjana, Jakšić Krüger. "Development, implementation and theoretical analysis of the bee colony optimization meta-heuristic method." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=104550&source=NDLTD&language=en.

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The Ph.D. thesis addresses a comprehensive study of the bee colonyoptimization meta-heuristic method (BCO). Theoretical analysis of themethod is conducted with the tools of probability theory. Necessary andsufficient conditions are presented that establish convergence of the BCOmethod towards an optimal solution. Three parallelization strategies and fivecorresponding implementations are proposed for BCO for distributed-memorysystems. The influence of method’s parameters on the performance of theBCO algorithm for two combinatorial optimization problems is analyzedthrough the experimental

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2

Schimitzek, Aleš. "Plánování cesty robotu pomocí rojové inteligence." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2013. http://www.nusl.cz/ntk/nusl-230877.

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This diploma thesis deals with the path planning by swarm intelligence. In the theoretical part it describes the best known methods of swarm intelligence (Ant Colony Optimization, Bee Swarm Optimization, Firefly Swarm Optimization and Particle Swarm Optimization) and their application for path planning. In the practical part particle swarm optimization is selected for the design and implementation of path planning in the C#.

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Suarez, Sergio. "Parametric Study of the Multi-Objective Particle Swarm Optimization and the Multi-Objective Bee Algorithm Applied to a Simply Supported Flat-Truss Bridge Structure." Thesis, California State University, Long Beach, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10978095.

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<p> Most engineering fields often encounter challenges in material, performance, and time efficiency. Truss design is a subject many structural engineers confront in their careers. Optimization is an effective approach in solving preliminary designs of truss structures. This paper studies two different multi-objective optimization algorithms, the particle swarm optimization (MOPSO) and the bee algorithm (MOBA), to optimize a simply supported flat-truss bridge designed by California State University, Long Beach’s Steel Bridge team for the American Institute of Steel Construction (AISC) Sp

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4

Абдураімов, Таір Заірович. "Алгоритм глибинного аналізу даних для задачі класифікації на основі штучного бджолиного рою". Master's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/38328.

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Актуальність теми. Оскільки розмір цифрової інформації зростає в геометричній прогресії, потрібно витягувати великі обсяги необроблених даних. На сьогоднішній день існує кілька методів налаштування та обробки даних відповідно до наших потреб. Найбільш поширеним методом є використання інтелектуального аналізу даних (Data Mining). Data Mining застосовується для вилучення неявних, дійсних та потенційно корисних знань із великих обсягів необроблених даних. Видобуті знання повинні бути точними, читабельними та легкими для розуміння. Крім того, процес видобутку даних також називають процесом виявлен

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Das, Choton Kanti. "Smart management strategies of utility-scale energy storage systems in power networks." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2019. https://ro.ecu.edu.au/theses/2209.

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Power systems are presently experiencing a period of rapid change driven by various interrelated issues, e.g., integration of renewables, demand management, power congestion, power quality requirements, and frequency regulation. Although the deployment of Energy Storage Systems (ESSs) has been shown to provide effective solutions to many of these issues, misplacement or non-optimal sizing of these systems can adversely affect network performance. This present research has revealed some novel working strategies for optimal allocation and sizing of utility-scale ESSs to address some important is

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6

Lai, Juan-Ming, and 賴阮明. "Integrating Particle Swarm Optimization and Honey-bee Mating Optimization for Flexible Job Shop Scheduling Problem." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/qhgr3b.

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碩士<br>國立臺北科技大學<br>工業工程與管理研究所<br>97<br>Most scheduling problems are very complex combinatorial optimization problems and hard to solve. The job-shop scheduling problem (JSP) is one of the problems. In the literature, more and more researchers used different heuristic algorithms to solve combinatorial optimization problems. Common algorithms are simulated annealing, genetic algorithm, tabu search approach, ant colony optimization and particle swarm optimization and so on. By more and more progress technology, the traditional job-shop scheduling is not enough to solve the diversity and a little a

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7

Huang, Li-ren, and 黃禮仁. "Bee Swarm Optimization Algorithm with Chaotic Sequence and Psychology Model of Emotion." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/03749074594989244915.

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碩士<br>義守大學<br>資訊管理學系碩士班<br>97<br>Swarm intelligence is one of the most popular derivative-free and population-based optimization algorithm. It has been extensively used for both continuous and discrete optimization problems due to its versatile optimization capabilities. Swarm intelligence is a research limb that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. Artificial Bee Colony algorithm

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8

Wu, Hung-chen, and 吳鴻辰. "Optimal Power Dispatch and CCHP Assessment of Microgrid System Using Improved Bee Swarm Optimization." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/56261925753739047305.

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碩士<br>國立中山大學<br>電機工程學系研究所<br>103<br>Under the guidance of international energy event occurred and international agreements, so Energy Saving and Carbon Reduction have already become an important issue in every county. However, the advances in green power not only provided alternative programs, but also reduced environmental pollution when using traditional way to produce energy. As increasing those unstable supply of green power. It must do some impact on traditional power grid. Such as power quality, system reliability, cost of power, etc. Therefore a microgrid which can quick react and dispa

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9

Chen, Kai-Hong, and 陳凱宏. "Integrating Principle Component Analysis with Multi-objective Particle Swarm Optimization and Honey-Bee Mating Optimization for Portfolio Selection." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/ufrph3.

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碩士<br>國立臺北科技大學<br>工業工程與管理研究所<br>98<br>In the recently years, the stock market is very unstable in Taiwan; especially after attacked by the worldwide financial crisis, the market trend becomes quite unclear. Under today’s unpredictable stock market, how to use financial instruments to make asset allocation and gain the best profit return is the most important topic to address. Therefore, through the help of information technology and heuristic algorithms, this research aimed to establish an information system to identify the best portfolio out of stocks in Taiwan 50 Index and Taiwan Med-Cap 100

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Fu, Ann-Chi, and 傅安琪. "Development and application of Hybride Artificial Bee Colony and Particle Swarm Optimization for Architecture layout problems." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/n6jdqr.

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碩士<br>國立臺灣科技大學<br>營建工程系<br>100<br>This study hybridizes particle swarm optimization (PSO) and artificial bee colony (ABC) to develop approaches which is more applicable than ABC and PSO. To hybride of ABC and PSO(HBP) approaches, agents including PSO particles and ABC bees are categorized into two sub-swarms by their species. Sequentially, agents in a sub-swarm are allowed to migrate to the other sub-swarm based on the sub-swarm fitness. And then, the PSO sub-swarm is permitted to learn from the global information, which involves the best position of the ABCsub-swarm. Twenty-three benchmark fu

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11

Wang, Meng-xuan, and 王孟軒. "Application of Improved Bee Swarm Optimization for Day-Ahead Market Optimal Unit Commitment and Economic Dispatch." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/9yjnwc.

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碩士<br>國立中山大學<br>電機工程學系研究所<br>104<br>The advances of renewable energy in power system not only reduced more environmental pollution than using traditional method, but provided alternative programs. As increasing of those unstable supply of green power. It will impact on the system. Such as system reliability, cost of power, power quality, power stability, etc. Therefore, how to stabilize the system while the load keep changing with ancillary service is an important issue currently. This thesis studies two case, 1th, combined thermal power generator, wind power, solar power, battery storage syst

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12

Hsieh, Yi-Hsiu, and 謝易修. "A hybrid optimization algorithm based on Endocrine Particle Swarm and Artificial Bee Algorithm for classification model selection." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/4rjjt3.

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碩士<br>國立中興大學<br>資訊管理學系所<br>103<br>The classification and analysis of data is an important issue in today''s research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endoc

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13

Gau, Quan-Kun, and 高全坤. "Hybrid Quantum-Inspired Bee Swarm Optimization for Optimal Planning of Reactive Power Source in Distorted Distribution Systems." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/72635687603931196241.

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碩士<br>國立雲林科技大學<br>電機工程系碩士班<br>100<br>The main purpose of optimal planning of reactive power source in the distribution system is to find an optimum allocation of capacitors under considering different load levels and harmonic currents injected to the distribution system, such that the costs of feed line power loss and capacitor investment become as little as possible. By considering the supply voltage quality, the bus voltage deviations must be as small as possible and the harmonic distortion must also be as small as possible. From above descriptions, the linguistic expressions like “as li

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14

HSIEH, CHENG-CHIEH, and 謝承杰. "Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/3eb79r.

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碩士<br>國立勤益科技大學<br>電機工程系<br>107<br>In this thesis, the output characteristics of partial modules in a photovoltaic module array when subject to shading were first explored. Then, an improved particle swarm optimization (PSO) algorithm was applied to track the global maximum power point (MPP), with a multi-peak characteristic curve. The improved particle swarm optimization algorithm proposed, combined with the artificial bee colony (ABC) algorithm, was used to adjust the weighting, cognition learning factor, and social learning factor, and change the number of iterations to enhance the tracking

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15

Pampara, Gary. "Angle modulated population based algorithms to solve binary problems." Diss., 2012. http://hdl.handle.net/2263/22801.

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Recently, continuous-valued optimization problems have received a great amount of focus, resulting in optimization algorithms which are very efficient within the continuous-valued space. Many optimization problems are, however, defined within the binary-valued problem space. These continuous-valued optimization algorithms can not operate directly on a binary-valued problem representation, without algorithm adaptations because the mathematics used within these algorithms generally fails within a binary problem space. Unfortunately, such adaptations may alter the behavior of the algorithm, poten

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