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Статті в журналах з теми "Bee swarm algorithm"
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.
Повний текст джерелаYong, 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.
Повний текст джерелаVerma, 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.
Повний текст джерелаBalasubramani, 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.
Повний текст джерелаDahiya, 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.
Повний текст джерелаDevarajan, 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.
Повний текст джерелаSobecki, 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.
Повний текст джерелаChun-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.
Повний текст джерелаZou, 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.
Повний текст джерелаXu, 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.
Повний текст джерелаДисертації з теми "Bee swarm algorithm"
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.
Повний текст джерелаActuality 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.
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.
Повний текст джерелаMost 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.
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.
Повний текст джерелаSholedolu, 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/.
Повний текст джерелаSantos, 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.
Повний текст джерелаClustering 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.
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.
Повний текст джерелаThis 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.
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.
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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
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.
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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.
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.
Повний текст джерелаHuang, 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.
Повний текст джерела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.
Книги з теми "Bee swarm algorithm"
Bonabeau, Eric, Marco Dorigo, and Guy Theraulaz. Swarm Intelligence. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195131581.001.0001.
Повний текст джерелаMpedi, Letlhokwa George, ed. Santa Claus: Law, Fourth Industrial Revolution, Decolonisation and Covid-19. African Sun Media, 2020. http://dx.doi.org/10.18820/9781928314837.
Повний текст джерелаЧастини книг з теми "Bee swarm algorithm"
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.
Повний текст джерелаSharma, 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.
Повний текст джерелаOkwu, 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.
Повний текст джерелаSharma, 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.
Повний текст джерелаFister, 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.
Повний текст джерелаSharma, 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.
Повний текст джерелаZhang, 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.
Повний текст джерелаWu, 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.
Повний текст джерелаMini, 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.
Повний текст джерелаBanda, 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.
Повний текст джерелаТези доповідей конференцій з теми "Bee swarm algorithm"
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.
Повний текст джерелаNitash, 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.
Повний текст джерелаKaya, 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.
Повний текст джерелаDhote, 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.
Повний текст джерелаSharma, 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.
Повний текст джерелаBiswas, 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.
Повний текст джерелаMoradi, 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.
Повний текст джерелаJong-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.
Повний текст джерелаBarani, 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.
Повний текст джерелаSotelo-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.
Повний текст джерела