Relevant bibliographies by topics / Bee swarm algorithm

Academic literature on the topic 'Bee swarm algorithm'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Bee swarm algorithm.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Bee swarm algorithm"

1

Sharma, Harish, Jagdish Chand Bansal, K. V. Arya, and Kusum Deep. "Dynamic Swarm Artificial Bee Colony Algorithm." International Journal of Applied Evolutionary Computation 3, no. 4 (October 2012): 19–33. http://dx.doi.org/10.4018/jaec.2012100102.

Full text
Abstract:
Artificial Bee Colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over test problems as well as real world optimization problems. This paper presents an attempt to modify ABC to make it less susceptible to stick at local optima and computationally efficient. In the case of local convergence, addition of some external potential solutions may help the swarm to get out of the local valley and if the algorithm is taking too much time to converge then deletion of some swarm members may help to speed up the convergence. Therefore, in this paper a dynamic swarm size strategy in ABC is proposed. The proposed strategy is named as Dynamic Swarm Artificial Bee Colony algorithm (DSABC). To show the performance of DSABC, it is tested over 16 global optimization problems of different complexities and a popular real world optimization problem namely Lennard-Jones potential energy minimization problem. The simulation results show that the proposed strategies outperformed than the basic ABC and three recent variants of ABC, namely, the Gbest-Guided ABC, Best-So-Far ABC and Modified ABC.
APA, Harvard, Vancouver, ISO, and other styles
2

Yong, Wang, Wang Tao, Zhang Cheng-Zhi, and Huang Hua-Juan. "A New Stochastic Optimization Approach — Dolphin Swarm Optimization Algorithm." International Journal of Computational Intelligence and Applications 15, no. 02 (June 2016): 1650011. http://dx.doi.org/10.1142/s1469026816500115.

Full text
Abstract:
A novel nature-inspired swarm intelligence (SI) optimization is proposed called dolphin swarm optimization algorithm (DSOA), which is based on mimicking the mechanism of dolphins in detecting, chasing after, and preying on swarms of sardines to perform optimization. In order to test the performance, the DSOA is evaluated against the corresponding results of three existing well-known SI optimization algorithms, namely, particle swarm optimization (PSO), bat algorithm (BA), and artificial bee colony (ABC), in the terms of the ability to find the global optimum of a range of the popular benchmark functions. The experimental results show that the proposed optimization seems superior to the other three algorithms, and the proposed algorithm has the performance of fast convergence rate, and high local optimal avoidance.
APA, Harvard, Vancouver, ISO, and other styles
3

Verma, Balwant Kumar, and Dharmender Kumar. "A review on Artificial Bee Colony algorithm." International Journal of Engineering & Technology 2, no. 3 (June 21, 2013): 175. http://dx.doi.org/10.14419/ijet.v2i3.1030.

Full text
Abstract:
In recent years large number of algorithms based on the swarm intelligence has been proposed by various researchers. The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behavior of honey bees. In short span of time, ABC algorithm has gain wide popularity among researchers due to its simplicity, easy to implementation and fewer control parameters. Large numbers of problems have been solved using ABC algorithm such as travelling salesman problem, clustering, routing, scheduling etc. the aim of this paper is to provide up to date enlightenment in the field of ABC algorithm and its applications.
APA, Harvard, Vancouver, ISO, and other styles
4

Balasubramani, Kamalam, and Karnan Marcus. "A Comprehensive review of Artificial Bee Colony Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 5, no. 1 (June 23, 2013): 15–28. http://dx.doi.org/10.24297/ijct.v5i1.4382.

Full text
Abstract:
The Artificial Bee Colony (ABC) algorithm is a stochastic, population-based evolutionary method proposed by Karaboga in the year 2005. ABC algorithm is simple and very flexible when compared to other swarm based algorithms. This method has become very popular and is widely used, because of its good convergence properties. The intelligent foraging behavior of honeybee swarm has been reproduced in ABC.Numerous ABC algorithms were developed based on foraging behavior of honey bees for solving optimization, unconstrained and constrained problems. This paper attempts to provide a comprehensive survey of research on ABC. A system of comparisons and descriptions is used to designate the importance of ABC algorithm, its enhancement, hybrid approaches and applications.
APA, Harvard, Vancouver, ISO, and other styles
5

Dahiya, Brahm Prakash, Shaveta Rani, and Paramjeet Singh. "A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space." International Journal of Mathematical, Engineering and Management Sciences 4, no. 2 (April 1, 2019): 471–88. http://dx.doi.org/10.33889/ijmems.2019.4.2-039.

Full text
Abstract:
Meta-heuristic algorithms are used to get optimal solutions in different engineering branches. Here four types of meta-heuristics algorithms are used such as evolutionary algorithms, swarm-based algorithms, physics based algorithms and human based algorithms respectively. Swarm based meta-heuristic algorithms are given more effective result in optimization problem issues and these are generated global optimal solution. Existing swarm intelligence techniques are suffered with poor exploitation and exploration in given search space. Therefore, in this paper Hybrid Artificial Grasshopper Optimization (HAGOA) meta-heuristic algorithm is proposed to improve the exploitation and exploration in given search space. HAGOA is inherited Salp swarm behaviors. HAGOA performs balancing in exploitation and exploration search space. It is capable to make chain system between exploitation and exploration phases. The efficiency of HAGOA meta-heuristic algorithm will analyze using 19 benchmarks functions from F1 to F19. In this paper, HAGOA algorithm is performed efficiency analyze test with Artificial Grasshopper optimization (AGOA), Hybrid Artificial Bee Colony with Salp (HABCS), Modified Artificial Bee Colony (MABC), and Modify Particle Swarm Optimization (MPSO) swarm based meta-heuristic algorithms using uni-modal and multi-modal functions in MATLAB. Comparison results are shown that HAGOA meta-heuristic algorithm is performed better efficiency than other swarm intelligence algorithms on the basics of high exploitation, high exploration, and high convergence rate. It also performed perfect balancing between exploitation and exploration in given search space.
APA, Harvard, Vancouver, ISO, and other styles
6

Devarajan, Jinil Persis, and T. Paul Robert. "Swarm Intelligent Data Aggregation in Wireless Sensor Network." International Journal of Swarm Intelligence Research 11, no. 2 (April 2020): 1–18. http://dx.doi.org/10.4018/ijsir.2020040101.

Full text
Abstract:
Data aggregation in WSNs is an interesting problem wherein data sensed by the sensors is routed to an aggregation node in an efficient way. Since the sensors are battery operated, it is very important for a routing protocol to conserve energy and also ensure load balancing and faster delivery. In this study, a multi-objective linear programming model is developed for this problem and solved using an exact algorithm applying dominance principle. In order to ensure faster convergence, routing algorithms incorporating strategies of swarms in nature such as Ants, Bees and Fireflies are adapted. In the simulation study, it is quite evident from the convergence characteristics, swarm intelligent algorithms could converge earlier than the exact algorithm with convergence time lesser by 90%. Moreover, when exact algorithm could solve smaller networks, the swarm intelligent algorithms could solve even larger network instances. Firefly algorithm is able to yield approximated pareto – optimal routes which outperforms ant colony optimization and bee colony optimization algorithms.
APA, Harvard, Vancouver, ISO, and other styles
7

Sobecki, Janusz. "Comparison of Selected Swarm Intelligence Algorithms in Student Courses Recommendation Application." International Journal of Software Engineering and Knowledge Engineering 24, no. 01 (February 2014): 91–109. http://dx.doi.org/10.1142/s0218194014500041.

Full text
Abstract:
In this paper a comparison of a few swarm intelligence algorithms applied in recommendation of student courses is presented. Swarm intelligence algorithms are nowadays successfully used in many areas, especially in optimization problems. To apply each swarm intelligence algorithm in recommender systems a special representation of the problem space is necessary. Here we present the comparison of efficiency of grade prediction of several evolutionary algorithms, such as: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weed Optimization (IWO), Bee Colony Optimization (BCO) and Bat Algorithm (BA).
APA, Harvard, Vancouver, ISO, and other styles
8

Chun-Feng, Wang, Liu Kui, and Shen Pei-Ping. "Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/832949.

Full text
Abstract:
Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.
APA, Harvard, Vancouver, ISO, and other styles
9

Zou, Wenping, Yunlong Zhu, Hanning Chen, and Xin Sui. "A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm." Discrete Dynamics in Nature and Society 2010 (2010): 1–16. http://dx.doi.org/10.1155/2010/459796.

Full text
Abstract:
Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.
APA, Harvard, Vancouver, ISO, and other styles
10

Xu, Xiao Qiang, and De Ming Lei. "Research on Swarm Intelligence Algorithm with an Artificial Bee Colony Algorithm for Lot Streaming Problem in Job Shop." Advanced Materials Research 951 (May 2014): 239–44. http://dx.doi.org/10.4028/www.scientific.net/amr.951.239.

Full text
Abstract:
The lot streaming (LS) problem in job shop with equal-size sub-lots and intermittent idling is considered. An effective swarm intelligence algorithm with an artificial bee colony (ABC) algorithm is proposed for the minimization of total penalties of tardiness and earliness. In the first period of ABC, the employed bee phase and the onlooker bee phase are both for lot/sub-lot scheduling. In the second period, the LS conditions are determined in the employed bee phase and the lot/sub-lot is scheduled in the onlooker phase. The worst solution of the swarm is replaced with the elite one every few cycles. Computational results show the promising advantage of ABC.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Bee swarm algorithm"

1

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.

Full text
Abstract:
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 виявлення знань, який використовувався в більшості нових міждисциплінарних областей, таких як бази даних, статистика штучного інтелекту, візуалізація, паралельні обчислення та інші галузі. Одним із нових і надзвичайно потужних алгоритмів, що використовуються в Data Mining, є еволюційні алгоритми та підходи, що базуються на рії, такі як мурашиний алгоритм та оптимізація рою частинок. В даній роботі запропоновано використати для інтелектуального аналізу даних досить нову ідею алгоритма бджолиного рою для широко розповсюдженої задачі класифікації. Мета роботи: покращення результатів класифікації даних в сенсі в точності і сталості за допомогою алгоритму інтелектуального аналізу даних на основі алгоритму бджолиного рою. Об’єктом дослідження є процес інтелектуального аналізу даних для задачі класифікації. Предметом дослідження є використання алгоритму бджолиного рою для інтелектуального аналізу даних. Методи дослідження. Використовуються методи параметричного дослідження евристичних алгоритмів, а також методи порівняльного аналізу для алгоритмів інтелектуального аналізу даних. Наукова новизна одержаних результатів роботи полягає в тому, що після проведеного аналізу існуючих рішень, запропоновано використати алгоритм бджолиного рою для задачі класифікації, точність і сталість якого перевищує показники існуючих класифікаторів. Практичне значення одержаних результатів полягає в тому, що розроблений алгоритм показує кращі результати в сенсі точності і сталості в порівнянні з іншими алгоритмами інтелектуального аналізу даних. Тобто адаптація бджолиного алгоритму може розглядатися як корисне та точне рішення для такої важливої проблеми, як задача класифікації даних. Апробація роботи. Основні положення й результати роботи були представлені та обговорювались на науковій конференції магістрантів та аспірантів «Прикладна математика та комп’ютинг» ПМК-2019 (Київ, 2019 р.), а також на науковій конференції магістрантів та аспірантів «Прикладна математика та комп’ютинг» ПМК-2020 (Київ, 2020 р.). Структура та обсяг роботи. Магістерська дисертація складається з вступу, чотирьох розділів, висновків та додатків. У вступі надано загальну характеристику роботи, виконано оцінку сучасного стану проблеми, обґрунтовано актуальність напрямку досліджень, сформульовано мету і задачі досліджень, показано наукову новизну отриманих результатів і практичну цінність роботи, наведено відомості про апробацію результатів і їх впровадження. У першому розділі розглянуто алгоритми інтелектуального аналізу даних, які використовуються для задачі класифікації. Обґрунтовано можливість використання евристичних алгоритмів, а саме алгоритму бджолиного рою для цієї задачі. У другому розділі детально розглянуто алгоритм бджолиного рою та принципи його роботи, також описано запропоновану методику його застосування для інтелектуального аналізу даних, а саме для задачі класифікації. У третьому розділі описано розроблений алгоритм та програмний додаток, в якому він реалізований. У четвертому розділі приведена оцінка ефективності запропонованого алгоритм, на основі тестування алгоритму, а також порівняльного аналізу між розробленим алгоритмом та вже існуючими. У висновках представлені результати магістерської дисертації. Робота виконана на 81 аркуші, містить посилання на список використаних літературних джерел з 18 найменувань. У роботі наведено 38 рисунків та 5 додатків.
Actuality of theme. As the size of digital information grows exponentially, large amounts of raw data need to be extracted. To date, there are several methods to customize and process data according to our needs. The most common method is to use Data Mining. Data Mining is used to extract implicit, valid and potentially useful knowledge from large amounts of raw data. The knowledge gained must be accurate, readable and easy to understand. In addition, the data mining process is also called the knowledge discovery process, which has been used in most new interdisciplinary fields, such as databases, artificial intelligence statistics, visualization, parallel computing, and other fields. One of the new and extremely powerful algorithms used in Data Mining is evolutionary algorithms and swarm-based approaches, such as the ant algorithm and particle swarm optimization. In this paper, it is proposed to use a fairly new idea of the swarm of bee swarm algorithm for data mining for a widespread classification problem. Purpose: to develop an algorithm for data mining for the classification problem based on the swarm of bee swarms, which exceeds other common classifiers in terms of accuracy of results and consistency. The object of research is the process of data mining for the classification problem. The subject of the study is the use of a swarm of bee swarms for data mining. Research methods. Methods of parametric research of heuristic algorithms, and also methods of the comparative analysis for algorithms of data mining are used. The scientific novelty of the work is as follows: 1. As a result of the analysis of existing solutions for the classification problem, it is decided to use such metaheuristics as the swarm of bee swarm. 2. The implementation of the bee algorithm for data mining is proposed. The practical value of the results obtained in this work is that the developed algorithm can be used as a classifier for data mining. In addition, the proposed adaptation of the bee algorithm can be considered as a useful and accurate solution to such an important problem as the problem of data classification. Approbation of work. The main provisions and results of the work were presented and discussed at the scientific conference of undergraduates and graduate students "Applied Mathematics and Computing" PMK-2019 (Kyiv, 2019), as well as at the scientific conference of undergraduates and graduate students "Applied Mathematics and Computing" PMK-2020 (Kyiv, 2020). Structure and scope of work. The master's dissertation consists of an introduction, four chapters, conclusions and appendices. The introduction provides a general description of the work, assesses the current state of the problem, substantiates the relevance of research, formulates the purpose and objectives of research, shows the scientific novelty of the results and the practical value of the work, provides information on testing and implementation. The first section discusses the data mining algorithms used for the classification problem. The possibility of using heuristic algorithms, namely the bee swarm algorithm for this problem, is substantiated. The second section discusses in detail the algorithm of the bee swarm and the principles of its operation, also describes the proposed method of its application for data mining, namely for the classification problem. The third section describes the developed algorithm and the software application in which it is implemented. In the fourth section the estimation of efficiency of the offered algorithm, on the basis of testing of algorithm, and also the comparative analysis between the developed algorithm and already different is resulted. The conclusions present the results of the master's dissertation. The work is performed on 89 sheets, contains a link to the list of used literature sources with 18 titles. The paper presents 38 figures and 2 appendices.
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
Abstract:

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) Spring 2018 competition. The variables, randomly selected from a continuous domain, are the top chord area, bottom chord area, web member area, and the center-to-center distance between the top and bottom chords. The optimized objectives are the weight and deflections of the bridge for the six load combinations stipulated in AISC’s rules. Both algorithms are calibrated using recommended parameter values derived from the parametric studies conducted. To compare their effectiveness, the recommended parameters were selected so that run-times for both optimization codes were similar. Both algorithms generated optimized solutions to the multi-objective truss problem, but MOPSO exhibited more, and better, solutions in a slightly longer run-time than MOBA.

APA, Harvard, Vancouver, ISO, and other styles
3

Hnízdilová, Bohdana. "Registrace ultrazvukových sekvencí s využitím evolučních algoritmů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442502.

Full text
Abstract:
This master´s thesis deals with the registration of ultrasound sequences using evolutionary algorithms. The theoretical part of the thesis describes the process of image registration and its optimalization using genetic and metaheuristic algorithms. The thesis also presents problems that may occur during the registration of ultrasonographic images and various approaches to their registration. In the practical part of the work, several optimization methods for the registration of a number of sequences were implemented and compared.
APA, Harvard, Vancouver, ISO, and other styles
4

Sholedolu, Michael O. "Nature-inspired optimisation : improvements to the Particle Swarm Optimisation Algorithm and the Bees Algorithm." Thesis, Cardiff University, 2009. http://orca.cf.ac.uk/55013/.

Full text
Abstract:
This research focuses on nature-inspired optimisation algorithms, in particular, the Particle Swarm Optimisation (PSO) Algorithm and the Bees Algorithm. The PSO Algorithm is a population-based stochastic optimisation technique first invented in 1995. It was inspired by the social behaviour of birds flocking or a school of fish. The Bees Algorithm is a population-based search algorithm initially proposed in 2005. It mimics the food foraging behaviour of swarms of honey bees. The thesis presents three algorithms. The first algorithm called the PSO-Bees Algorithm is a cross between the PSO Algorithm and the Bees Algorithm. The PSO-Bees Algorithm enhanced the PSO Algorithm with techniques derived from the Bees Algorithm. The second algorithm called the improved Bees Algorithm is a version of the Bees Algorithm that incorporates techniques derived from the PSO Algorithm. The third algorithm called the SNTO-Bees Algorithm enhanced the Bees Algorithm using techniques derived from the Sequential Number-Theoretic Optimisation (SNTO) Algorithm. To demonstrate the capability of the proposed algorithms, they were applied to different optimisation problems. The PSO-Bees Algorithm is used to train neural networks for two problems, Control Chart Pattern Recognition and Wood Defect Classification. The results obtained and those from tests on well known benchmark functions provide an indication of the performance of the algorithm relative to that of other swarm-based stochastic optimisation algorithms. The improved Bees Algorithm was applied to mechanical design optimisation problems (design of welded beams and coil springs) and the mathematical benchmark problems used previously to test the PSO-Bees Algorithm. The algorithm incorporates cooperation and communication between different neighbourhoods. The results obtained show that the proposed cooperation and communication strategies adopted enhanced the performance and convergence of the algorithm. The SNTO-Bees Algorithm was applied to a set of mechanical design optimisation problems (design of welded beams, coil springs and pressure vessel) and mathematical benchmark functions used previously to test the PSO-Bees Algorithm and the improved Bees Algorithm. In addition, the algorithm was tested with a number of deceptive multi modal benchmark functions. The results obtained help to validate the SNTO-Bees Algorithm as an effective global optimiser capable of handling problems that are deceptive in nature with high dimensions.
APA, Harvard, Vancouver, ISO, and other styles
5

Santos, Daniela Scherer dos. "Bee clustering : um algoritmo para agrupamento de dados inspirado em inteligência de enxames." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/18249.

Full text
Abstract:
Agrupamento de dados é o processo que consiste em dividir um conjunto de dados em grupos de forma que dados semelhantes entre si permaneçam no mesmo grupo enquanto que dados dissimilares sejam alocados em grupos diferentes. Técnicas tradicionais de agrupamento de dados têm sido usualmente desenvolvidas de maneira centralizada dependendo assim de estruturas que devem ser acessadas e modificadas a cada passo do processo de agrupamento. Além disso, os resultados gerados por tais métodos são dependentes de informações que devem ser fornecidas a priori como por exemplo número de grupos, tamanho do grupo ou densidade mínima/máxima permitida para o grupo. O presente trabalho visa propor o bee clustering, um algoritmo distribuído inspirado principalmente em técnicas de inteligência de enxames como organização de colônias de abelhas e alocação de tarefas em insetos sociais, desenvolvido com o objetivo de resolver o problema de agrupamento de dados sem a necessidade de pistas sobre o resultado desejado ou inicialização de parâmetros complexos. O bee clustering é capaz de formar grupos de agentes de maneira distribuída, uma necessidade típica em cenários de sistemas multiagente que exijam capacidade de auto-organização sem controle centralizado. Os resultados obtidos mostram que é possível atingir resultados comparáveis as abordagens centralizadas.
Clustering can be defined as a set of techniques that separate a data set into groups of similar objects. Data items within the same group are more similar than objects of different groups. Traditional clustering methods have been usually developed in a centralized fashion. One reason for this is that this form of clustering relies on data structures that must be accessed and modified at each step of the clustering process. Another issue with classical clustering methods is that they need some hints about the target clustering. These hints include for example the number of clusters, the expected cluster size, or the minimum density of clusters. In this work we propose a clustering algorithm that is inspired by swarm intelligence techniques such as the organization of bee colonies and task allocation among social insects. Our proposed algorithm is developed in a decentralized fashion without any initial information about number of classes, number of partitions, and size of partition, and without the need of complex parameters. The bee clustering algorithm is able to form groups of agents in a distributed way, a typical necessity in multiagent scenarios that require self-organization without central control. The performance of our algorithm shows that it is possible to achieve results that are comparable to those from centralized approaches.
APA, Harvard, Vancouver, ISO, and other styles
6

Matakas, Linas. "Dirbtinės bičių kolonijos algoritmai ir jų taikymai skirstymo uždaviniams spręsti." Bachelor's thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130729_150200-28811.

Full text
Abstract:
Šiame darbe yra trumpai apžvelgiami dalelių spiečių sistemų algoritmai, skirstymo uždaviniai ir jų formuluotės, bei praktinės interpretacijos, plačiau apžvelgiami ir analizuojami dirbtinių bičių kolonijų algoritmai. Taip pat šiame darbe galima rasti dirbtinių bičių kolonijų algoritmo pritaikymą skirstymo uždaviniams spręsti, bei sukurtos programos skaičiavimo rezultatų analizę.
This paper consists of short descriptions of swarm systems algorithms, assigment problems and longer overview of artificial bee colony algorithms and it‘s analysis. Moreover, you can find an Artificial Bee Colony Algorithm's Application to one of an Assigment Problems and it's computational results analysis.
APA, Harvard, Vancouver, ISO, and other styles
7

Carraro, Luziana Ferronatto. "Uma abordagem para o problema de carregamento de navios-contêineres através do emprego de metaheurísticas baseadas na codificação por regras." Universidade do Vale do Rio dos Sinos, 2013. http://www.repositorio.jesuita.org.br/handle/UNISINOS/4623.

Full text
Abstract:
Submitted by William Justo Figueiro (williamjf) on 2015-07-27T20:23:06Z No. of bitstreams: 1 09d.pdf: 2136888 bytes, checksum: 8bc73fd7975259c3bc984b913580a5c1 (MD5)
Made available in DSpace on 2015-07-27T20:23:06Z (GMT). No. of bitstreams: 1 09d.pdf: 2136888 bytes, checksum: 8bc73fd7975259c3bc984b913580a5c1 (MD5) Previous issue date: 2013
CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Com a expansão do transporte marítimo, passou a ser adotado o uso de contêineres para o transporte de cargas, sendo evidenciados alguns problemas. Dentre eles, um dos principais, é o problema de carregamento e descarregamento de contêineres em navios. O problema surge devido aos altos custos operacionais gerados a partir da movimentação de contêineres. Este problema é o foco desta pesquisa, que tem como objetivo principal elaborar planos de carga eficientes que gerem um número mínimo de movimentações de contêineres, nas operações de carga e descarga de navios-contêineres, diminuindo assim os custos de operação. Neste trabalho, é proposta a aplicação da metaheurística Algoritmo Genético e da metaheurística Enxame de Abelhas, resolvendo o problema através de uma codificação baseada em regras de carregamento e descarregamento. A codificação por regras é compacta e adequada, assegurando que as soluções do problema sejam factíveis e de simples representação, acelerando o processo de solução. Nos experimentos realizados, as duas metaheurísticas foram empregadas, assumindo diferentes configurações de regras, com o intuito de comparar o seu desempenho. A proposta de novas regras de carregamento e descarregamento, em complemento às existentes na literatura, trouxeram bons resultados. Desta forma, foram obtidas soluções de boa qualidade e melhores que aquelas encontradas na literatura que abordam o mesmo problema.
With the expansion of maritime transportation, the use of containers for goods transportation has increased, being evidenced some problems. Among these problems, the container ship stowage problem arose as one of the main problems due to the high operational costs related to movement of containers. This problem is the focus of this research, where the main objective is the formulation of stowage plans that generate a minimum number of container shiftings in the operations of loading and unloading performed in port calls of container ships. In order to determine a suitable stowage plan, the application of Genetic Algorithm and Bee Swarm Optimization metaheuristics are proposed to solve the problem by using a rule-based encoding for the solution. The solution encoding based on loading and unloading rules is compact and suitable, ensuring the feasibility of solutions and also the simple representation of it, speeding up the solution procedures. In the performed experiments, both metaheuristics were applied assuming different rules settings with the objective to compare each performance. The proposal of new rules of loading and unloading, in addition with those existing in literature, has produced good solutions. Thereby, good quality solutions were achieved and also better than that found in the literature which discuss the same problem
APA, Harvard, Vancouver, ISO, and other styles
8

Sanches, Rafael Francisco Viana. "Algoritmo de enxame de abelhas para resolução do problema da programação da produção Job Shop flexível multiobjetivo." Universidade Federal de São Carlos, 2017. https://repositorio.ufscar.br/handle/ufscar/9055.

Full text
Abstract:
Submitted by Ronildo Prado (ronisp@ufscar.br) on 2017-08-23T12:44:58Z No. of bitstreams: 1 DissRFVS.pdf: 2728458 bytes, checksum: 4d07aa40b8f58f835e1e857098ff74a8 (MD5)
Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-23T12:45:05Z (GMT) No. of bitstreams: 1 DissRFVS.pdf: 2728458 bytes, checksum: 4d07aa40b8f58f835e1e857098ff74a8 (MD5)
Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-23T12:45:10Z (GMT) No. of bitstreams: 1 DissRFVS.pdf: 2728458 bytes, checksum: 4d07aa40b8f58f835e1e857098ff74a8 (MD5)
Made available in DSpace on 2017-08-23T12:45:16Z (GMT). No. of bitstreams: 1 DissRFVS.pdf: 2728458 bytes, checksum: 4d07aa40b8f58f835e1e857098ff74a8 (MD5) Previous issue date: 2017-02-14
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
The production scheduling activity is considered as one of the most complex activities in production management. This activity is part of the class of NP-Hard problems found in the area of computer science, that is, those problems that can not be solved deterministically in polynomial time. In addition, the complexity of this activity may increase according to the constraints imposed on each programming system/problem. In this research, the problem of programming of production the Flexible Job Shop (JSF) is studied. This problem is considered an extension of the Job Shop programming problem. In JSF, a group of jobs (i.e., products, items, part of an item) formed by a set of operations and each operation must be programmed by a resource (i.e., machine) that belongs to a group of resources that have the same functional characteristics (e.g., cut, sanding, painting). This problem is characterized in two sub-problems being routing and sequencing activity. Routing involves determining which resource will process a given operation. Sequencing is the order in which each operation will be processed on a resource. Through established programming, the objective of this research is to optimize performance multicriteria: the makespan (i.e., time spent to produce a set of jobs), processing time spent on the resource that worked by more time and total production time. In order to reach the objectives mentioned above, a hybrid swarm approach is proposed in this research. In this approach, two auxiliary methods are used to treat the abovementioned sub-problems: genetic operator of mutation to perform the routing activity and for the sequencing activity, an adaptive method of neighborhood structures is proposed. In order to deal with the multiobjectivity of the problem, we propose the Pareto dominance method. Experimental results obtained through commonly used benchmarks prove the efficacy and superiority of the proposed approach when compared to other approaches also applied to the problem studied.
A atividade de programação da produção é considerada como uma das atividades mais complexas no gerenciamento da produção. Essa atividade faz parte da classe de problemas NP-Difícil encontrados na área da ciência da computação, ou seja, aqueles problemas que não podem ser solucionados deterministicamente em tempo polinomial. Além disso, a complexidade dessa atividade pode aumentar de acordo com as restrições impostas a cada sistema/problema de programação. Nesta pesquisa, estuda-se o problema de programação da produção Job Shop Flexível (JSF). Esse problema é considerado como uma extensão do problema de programação Job Shop. No JSF, deve-se programar um grupo de jobs (i.e., produtos, itens, parte de um item) formados por um conjunto de operações e cada operação é processada por um recurso (i.e., máquina) que pertence a um grupo de recursos que possuam mesmas caraterísticas funcionais (e.g., cortar, lixar, pintar). Esse problema é caracterizado em dois sub-problemas, sendo eles, a atividade de roteamento e de sequenciamento. O roteamento implica em definir qual recurso irá processar uma determinada operação. O sequenciamento é a ordem em que cada operação será processada em um recurso. Por meio da programação estabelecida objetiva-se nessa pesquisa, otimizar multicritérios de desempenho, sendo eles: makespan (i.e., tempo gasto para produzir um conjunto de jobs), tempo de processamento gasto no recurso que trabalhou por mais tempo e tempo total de produção. Para alcançar os objetivos supracitados é proposto nessa pesquisa uma abordagem híbrida de enxame de abelhas. Nessa abordagem, utiliza-se dois métodos auxiliares para tratar os sub-problemas supracitados, sendo eles: operador genético de mutação para realizar a atividade de roteamento e para a atividade de sequenciamento é proposto um método adaptativo de estruturas de vizinhança. Para tratar a multiobjetividade do problema, propõe-se o método dominância de Pareto. Resultados experimentais obtidos por meio de benchmarks comumente usados comprovam a eficácia e a superioridade da abordagem proposta quando comparada com outras abordagens também aplicadas ao problema estudado.
APA, Harvard, Vancouver, ISO, and other styles
9

Raudenská, Lenka. "Metriky a kriteria pro diagnostiku sociotechnických systémů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2010. http://www.nusl.cz/ntk/nusl-233879.

Full text
Abstract:
This doctoral thesis is focused on metrics and the criteria for socio-technical system diagnostics, which is a high profile topic for companies wanting to ensure the best in product quality. More and more customers are requiring suppliers to prove reliability in the production and supply quality of products according to given specifications. Consequently the ability to produce quality goods corresponding to customer requirements has become a fundamental condition in order to remain competitive. The thesis firstly lays out the basic strategies and rules which are prerequisite for a successful working company in order to ensure provision of quality goods at competitive costs. Next, methods and tools for planning are discussed. Planning is important in its impact on budget, time schedules, and necessary sourcing quantification. Risk analysis is also included to help define preventative actions, and reduce the probability of error and potential breakdown of the entire company. The next part of the thesis deals with optimisation problems, which are solved by Swarm based optimisation. Algorithms and their utilisation in industry are described, in particular the Vehicle routing problem and Travelling salesman problem, used as tools for solving specialist problems within manufacturing corporations. The final part of the thesis deals with Qualitative modelling, where solutions can be achieved with less exact quantitative information of the surveyed model. The text includes qualitative algebra descriptions, which discern only three possible values – positive, constant and negative, which are sufficient in the demonstration of trends. The results can also be conveniently represented using graph theory tools.
APA, Harvard, Vancouver, ISO, and other styles
10

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.

Full text
Abstract:
碩士
義守大學
資訊管理學系碩士班
97
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 is an optimization algorithm based on the intelligent behavior of honey bee swarm. This thesis presents Bee Swarm Optimization intended to introduce chaotic sequences and psychology factor of emotion into the algorithm. We define two emotions Bees could have, positive and negative, and correspond to two reaction to perception respectively. For avoiding premature convergence it allows the proposed Emotional Chaotic Bee Swarm Optimization to continue search for better even best optimization in classic optimization problems, reaching better solutions than classic Artificial Bee Colony algorithm with a faster convergence speed.
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Bee swarm algorithm"

1

Bonabeau, Eric, Marco Dorigo, and Guy Theraulaz. Swarm Intelligence. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195131581.001.0001.

Full text
Abstract:
Social insects--ants, bees, termites, and wasps--can be viewed as powerful problem-solving systems with sophisticated collective intelligence. Composed of simple interacting agents, this intelligence lies in the networks of interactions among individuals and between individuals and the environment. A fascinating subject, social insects are also a powerful metaphor for artificial intelligence, and the problems they solve--finding food, dividing labor among nestmates, building nests, responding to external challenges--have important counterparts in engineering and computer science. This book provides a detailed look at models of social insect behavior and how to apply these models in the design of complex systems. The book shows how these models replace an emphasis on control, preprogramming, and centralization with designs featuring autonomy, emergence, and distributed functioning. These designs are proving immensely flexible and robust, able to adapt quickly to changing environments and to continue functioning even when individual elements fail. In particular, these designs are an exciting approach to the tremendous growth of complexity in software and information. Swarm Intelligence draws on up-to-date research from biology, neuroscience, artificial intelligence, robotics, operations research, and computer graphics, and each chapter is organized around a particular biological example, which is then used to develop an algorithm, a multiagent system, or a group of robots. The book will be an invaluable resource for a broad range of disciplines.
APA, Harvard, Vancouver, ISO, and other styles
2

Mpedi, Letlhokwa George, ed. Santa Claus: Law, Fourth Industrial Revolution, Decolonisation and Covid-19. African Sun Media, 2020. http://dx.doi.org/10.18820/9781928314837.

Full text
Abstract:
The origins of Santa Claus, or so I am told, is that the young Bishop Nicholas secretly delivered three bags of gold as dowries for three young girls to their indebted father to save them from a life of prostitution. Armed with immortality, a factory of elves and a fleet of reindeer, his has been a lasting legacy, inextricably linked to Christmas. Of course, this Christmas looks a little different. Amidst a global pandemic, shimmying down the chimneys of strangers certainly does not adhere to social distancing guidelines. Some borders remain closed, and in some instances, the quarantine period is far too long. After all, he only has 24 hours to spread cheer across the world. As with the rest of us, Santa Claus is likely to get the remote working treatment. The reindeers this year are likely to be self-driving, reminiscent of an Amazon swarm of technology, and the naughty and nice lists are likely to be based on algorithms derived from social media accounts. In the age of the fourth industrial revolution, it is difficult to imagine that letters suffice anymore. How many posts were verified as real before shared? Enough to get you a drone. Fake news? Here is a lump of coal. Will we see elves in personal protective equipment (PPE) and will Santa Claus, high risk because of age and his likely comorbidities from the copious amount of cookies, have to self-isolate in the North Pole? In fact, will there be any toys at all this year? Surely production has been stalled with the restrictions on imports and exports into the North Pole. Perhaps, there is a view to outsourcing, or perhaps, there is a shift towards local production and supply chains. More importantly, as we have done in many instances in this period, maybe we should pause to reflect on the current structures in place. The sanctification of a figure so clearly dismissive of the Global South and to be critical, quite classist must be called into question. From some of the keenest minds, the contributions in this book make a strong case against this holly jolly man. We traverse important topics such as, is the constitution too lenient with a clear intruder who has conveniently branded himself a Good Samaritan? Allegations of child labour under the guise of elves, blatant animal cruelty, constant surveillance in stark contrast to many democratic ideals and his possible threat to national security come to the fore. Nevertheless, as the song goes, he is aware when you are asleep, and he knows when you are awake. Is feminism a farce to this beloved man – what role does Mrs Claus play and why are there inherent gender norms in his toys? Then is the worry of closed borders and just how accurate his COVID-19 tests are. Of course, this brings his ethics into question. While there is an agreement that transparency, justice and fairness, nonmaleficence, responsibility, and privacy are the core ethical principles, the meaning of these principles differs, particularly across countries and cultures. Why are we subject to Santa Claus’ notions of good and evil when he is so far removed from our context? As Richard Thaler and Cass Sunstein would tell you, this is fundamentally a nudge from Santa Claus for children to fit into his ideals. A nudge, coined by Thaler, is a choice that predictably changes people’s behaviour without forbidding any options or substantially changing their economic incentives. Even with pinched cheeks and an air of holiday cheer, Santa Claus has to come under scrutiny. In the process of decolonising knowledge and looking at various epistemologies, does Santa still make the cut?
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Bee swarm algorithm"

1

Akay, Bahriye, and Dervis Karaboga. "Artificial Bee Colony Algorithm." In Swarm Intelligence Algorithms, 17–30. First edition. | Boca Raton : Taylor and Francis, 2020.: CRC Press, 2020. http://dx.doi.org/10.1201/9780429422614-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Sharma, Nirmala, Harish Sharma, Ajay Sharma, and Jagdish Chand Bansal. "Black Hole Artificial Bee Colony Algorithm." In Swarm, Evolutionary, and Memetic Computing, 214–21. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48959-9_19.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Okwu, Modestus O., and Lagouge K. Tartibu. "Artificial Bee Colony Algorithm." In Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications, 15–31. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61111-8_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Sharma, Tarun Kumar, Millie Pant, and V. P. Singh. "Modified Onlooker Phase in Artificial Bee Colony Algorithm." In Swarm, Evolutionary, and Memetic Computing, 339–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35380-2_40.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Fister, Iztok, Iztok Fister, and Janez Brest. "A Hybrid Artificial Bee Colony Algorithm for Graph 3-Coloring." In Swarm and Evolutionary Computation, 66–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29353-5_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Sharma, Tarun Kumar, Millie Pant, and V. P. Singh. "Artificial Bee Colony Algorithm with Self Adaptive Colony Size." In Swarm, Evolutionary, and Memetic Computing, 593–600. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27172-4_70.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Zhang, Xin, and Zhou Wu. "An Artificial Bee Colony Algorithm with History-Driven Scout Bees Phase." In Advances in Swarm and Computational Intelligence, 239–46. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20466-6_26.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Wu, Di, Rongyi Cui, Changrong Li, and Guangjun Song. "Mechanism and Convergence of Bee-Swarm Genetic Algorithm." In Lecture Notes in Computer Science, 27–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13495-1_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Mini, S., Siba K. Udgata, and Samrat L. Sabat. "Sensor Deployment in 3-D Terrain Using Artificial Bee Colony Algorithm." In Swarm, Evolutionary, and Memetic Computing, 424–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17563-3_51.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Banda, Jayalakshmi, and Alok Singh. "A Hybrid Artificial Bee Colony Algorithm for the Terminal Assignment Problem." In Swarm, Evolutionary, and Memetic Computing, 134–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20294-5_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Bee swarm algorithm"

1

Akbari, Reza, Alireza Mohammadi, and Koorush Ziarati. "A powerful bee swarm optimization algorithm." In 2009 IEEE 13th International Multitopic Conference (INMIC). IEEE, 2009. http://dx.doi.org/10.1109/inmic.2009.5383155.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Nitash, C. G., and Alok Singh. "An artificial bee colony algorithm for minimum weight dominating set." In 2014 IEEE Symposium On Swarm Intelligence (SIS). IEEE, 2014. http://dx.doi.org/10.1109/sis.2014.7011811.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kaya, Ersin, Ismail Babaoglu, and Halife Kodaz. "Galactic swarm optimization using artificial bee colony algorithm." In 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE). IEEE, 2017. http://dx.doi.org/10.1109/ictke.2017.8259616.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Dhote, C. A., Anuradha D. Thakare, and Shruti M. Chaudhari. "Data clustering using particle swarm optimization and bee algorithm." In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). IEEE, 2013. http://dx.doi.org/10.1109/icccnt.2013.6726828.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Sharma, Tarun Kumar, and Millie Pant. "Enhancing the food locations in an Artificial Bee Colony algorithm." In 2011 IEEE Symposium On Swarm Intelligence - Part Of 17273 - 2011 Ssci. IEEE, 2011. http://dx.doi.org/10.1109/sis.2011.5952582.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Biswas, Subhodip, Souvik Kundu, Digbalay Bose, Swagatam Das, P. N. Suganthan, and B. K. Panigrahi. "Migrating forager population in a multi-population Artificial Bee Colony algorithm with modified perturbation schemes." In 2013 IEEE Symposium on Swarm Intelligence (SIS). IEEE, 2013. http://dx.doi.org/10.1109/sis.2013.6615186.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Moradi, Razieh, Hossein Nezamabadi-pour, and Mohadese Soleimanpour. "Modified Distributed Bee Algorithm in Task Allocation of Swarm Robotic." In 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI). IEEE, 2019. http://dx.doi.org/10.1109/kbei.2019.8735026.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Jong-Hyun Lee, Chang Wook Ahn, and Jinung An. "A honey bee swarm-inspired cooperation algorithm for foraging swarm robots: An empirical analysis." In 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2013. http://dx.doi.org/10.1109/aim.2013.6584139.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Barani, Fatemeh, and Mina Mirhosseini. "Classification of binary problems with SVM and a mixed artificial bee colony algorithm." In 2018 3rd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC). IEEE, 2018. http://dx.doi.org/10.1109/csiec.2018.8405413.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Sotelo-Figueroa, Marco Aurelio, Maria del Rosario Baltazar-Flores, Juan Martin Carpio, and Victor Zamudio. "A Comparation between Bee Swarm Optimization and Greedy Algorithm for the Knapsack Problem with Bee Reallocation." In 2010 Ninth Mexican International Conference on Artificial Intelligence (MICAI). IEEE, 2010. http://dx.doi.org/10.1109/micai.2010.32.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Bee swarm algorithm' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography