Relevant bibliographies by topics / Large scale swarm intelligence

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Large scale swarm intelligence'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Large scale swarm intelligence"

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Mashwani, Wali Khan, Abdelouahed Hamdi, Muhammad Asif Jan, Atila Göktaş, and Fouzia Khan. "Large-scale global optimization based on hybrid swarm intelligence algorithm." Journal of Intelligent & Fuzzy Systems 39, no. 1 (2020): 1257–75. http://dx.doi.org/10.3233/jifs-192162.

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Xu, Ze Sheng, Zhi Feng Ma, Xin Wen Di, Tao Luo, Hong Yun Guo, and Bao Chen Niu. "The Study on Electric Power System Based on Swarm Intelligence." Advanced Materials Research 442 (January 2012): 424–29. http://dx.doi.org/10.4028/www.scientific.net/amr.442.424.

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In this paper, we introduce the swarm intelligence computation and its applications in power system. Because swarm intelligence does not need any precondition of centralized control and global model, it is very suitable to solve large scale power system nonlinear optimization problems which are hard to establish effective formalized models and difficult to be solved by traditional methods. In order to apply swarm intelligence better in power system, we propose two central research directions in the future: (1) The mathematical basis of swarm intelligence is unsubstantial and it lacks profound
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Mortazavi, Ali. "Large-scale structural optimization using a fuzzy reinforced swarm intelligence algorithm." Advances in Engineering Software 142 (April 2020): 102790. http://dx.doi.org/10.1016/j.advengsoft.2020.102790.

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Yan, Danping, and Yongzhong Lu. "Recent Advances in Particle Swarm Optimization for Large Scale Problems." Journal of Autonomous Intelligence 1, no. 1 (2018): 22. http://dx.doi.org/10.32629/jai.v1i1.15.

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Accompanied by the advent of current big data ages, the scales of real world optimization problems with many decisive design variables are becoming much larger. Up to date, how to develop new optimization algorithms for these large scale problems and how to expand the scalability of existing optimization algorithms have posed further challenges in the domain of bio-inspired computation. So addressing these complex large scale problems to produce truly useful results is one of the presently hottest topics. As a branch of the swarm intelligence based algorithms, particle swarm optimization (PSO)
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Cheng, Shi, Qingyu Zhang, and Quande Qin. "Big data analytics with swarm intelligence." Industrial Management & Data Systems 116, no. 4 (2016): 646–66. http://dx.doi.org/10.1108/imds-06-2015-0222.

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Purpose – The quality and quantity of data are vital for the effectiveness of problem solving. Nowadays, big data analytics, which require managing an immense amount of data rapidly, has attracted more and more attention. It is a new research area in the field of information processing techniques. It faces the big challenges and difficulties of a large amount of data, high dimensionality, and dynamical change of data. However, such issues might be addressed with the help from other research fields, e.g., swarm intelligence (SI), which is a collection of nature-inspired searching techniques. Th
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Cao, Ming, and Weiguo Fang. "Swarm Intelligence Algorithms for Weapon-Target Assignment in a Multilayer Defense Scenario: A Comparative Study." Symmetry 12, no. 5 (2020): 824. http://dx.doi.org/10.3390/sym12050824.

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Weapon-target assignment (WTA) is a kind of NP-complete problem in military operations research. To solve the multilayer defense WTA problems when the information about enemy’s attacking plan is symmetric to the defender, we propose four heuristic algorithms based on swarm intelligence with customizations and improvements, including ant colony optimization (ACO), binary particle swarm optimization (BPSO), integer particle swarm optimization (IPSO) and sine cosine algorithm (SCA). Our objective is to assess and compare the performance of different algorithms to determine the best algorithm for
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Deng, Hanbo, Lizhi Peng, Haibo Zhang, Bo Yang, and Zhenxiang Chen. "Ranking-based biased learning swarm optimizer for large-scale optimization." Information Sciences 493 (August 2019): 120–37. http://dx.doi.org/10.1016/j.ins.2019.04.037.

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Kumar, Gaurav, and Virender Ranga. "Swarm Intelligence-based Partitioned Recovery in Wireless Sensor Networks." Journal of Telecommunications and Information Technology 3 (September 28, 2018): 70–81. http://dx.doi.org/10.26636/jtit.2018.121817.

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The failure rate of sensor nodes in Heterogeneous Wireless Sensor Networks is high due to the use of low battery-powered sensor nodes in a hostile environment. Networks of this kind become non-operational and turn into disjoint segmented networks due to large-scale failures of sensor nodes. This may require the placement of additional highpower relay nodes. In this paper, we propose a network partition recovery solution called Grey Wolf, which is an optimizer algorithm for repairing segmented heterogeneous wireless sensor networks. The proposed solution provides not only strong bi-connectivity
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Xu, Peilan, Wenjian Luo, Xin Lin, Jiajia Zhang, Yingying Qiao, and Xuan Wang. "Constraint-Objective Cooperative Coevolution for Large-scale Constrained Optimization." ACM Transactions on Evolutionary Learning and Optimization 1, no. 3 (2021): 1–26. http://dx.doi.org/10.1145/3469036.

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Large-scale optimization problems and constrained optimization problems have attracted considerable attention in the swarm and evolutionary intelligence communities and exemplify two common features of real problems, i.e., a large scale and constraint limitations. However, only a little work on solving large-scale continuous constrained optimization problems exists. Moreover, the types of benchmarks proposed for large-scale continuous constrained optimization algorithms are not comprehensive at present. In this article, first, a constraint-objective cooperative coevolution (COCC) framework is
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Li, Zhuo, and Xue Luo Qu. "The Novel Parameter Selection of Particle Swarm Optimization." Advanced Materials Research 479-481 (February 2012): 344–47. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.344.

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Particle Swarm Optimization (PSO) is a novel artificial intelligent technique proposed by Eberhart and Kennedy which is a type of Swarm Intelligence. PSO is simulated as population-based stochastic optimization influenced by the social behavior of bird flocks. In past decades, more and more researcher has been targeting to improve the original PSO for solving various problems and it has great potential to be done further. This paper reviews the progress of PSO research so far, and the recent achievements for application to large-scale optimization problems.
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Dissertations / Theses on the topic "Large scale swarm intelligence"

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Wittner, Otto. "Emergent behavior based implements for distributed network management." Doctoral thesis, Norwegian University of Science and Technology, Department of Telematics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-1787.

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<p>Network and system management has always been of concern for telecommunication and computer system operators. The need for standardization was recognised already 20 years ago, hence several standards for network management exist today. However, the ever-increasing number of units connected to networks and the ever-increasing number of services being provided results in significant increased complexity of average network environments. This challenges current management systems. In addition to the general increase in complexity the trend among network owners and operators of merging several s
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Geuther, Brian Q. "Towards Bacteria Inspired Stochastic Control Strategies for Microrobotic Swarm Intelligence." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23751.

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Collective robotic behavior poses significant advantages over classical control methods such as system response and robustness. Biological cooperative communities have provided great insights for development of many control algorithms. Localized chemical signaling within bacterial communities is used for directed movement and dynamic density measurements. Both individual and population scale models have been created to adequately model community dynamics. These dynamics, including directed motion due to chemotaxis and density controlled functionality from quorum sensing, are modeled through an
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Nairouz, Bassem R. "Conceptual design methodology of distributed intelligence large scale systems." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49077.

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Distributed intelligence systems are starting to gain dominance in the field of large-scale complex systems. These systems are characterized by nonlinear behavior patterns that are only predicted through simulation-based engineering. In addition, the autonomy, intelligence, and reconfiguration capabilities required by certain systems introduce obstacles adding another layer of complexity. However, there exists no standard process for the design of such systems. This research presents a design methodology focusing on distributed control architectures while concurrently considering the systems d
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Ealey, Douglas. "Natural language acquisition in large scale neural semantic networks." Thesis, University of Southampton, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310843.

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Zhao, Haixiang. "Artificial Intelligence Models for Large Scale Buildings Energy Consumption Analysis." Phd thesis, Ecole Centrale Paris, 2011. http://tel.archives-ouvertes.fr/tel-00658767.

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The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, occupancy and their behaviors, the operation of sub-level components like Heating, Ventilation and Air-Conditioning (HVAC) system. This complex property makes the prediction, analysis, or fault detection/diagnosis of building energy consumption very difficult to accurately and quickly perform. This thesis mainly focuses on up-to-date artificial intelligence models with the applications to solve these problems. First, we review recently developed models
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Takane, Marina. "Inference of gene regulatory networks from large scale gene expression data." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80883.

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With the advent of the age of genomics, an increasing number of genes have been identified and their functions documented. However, not as much is known of specific regulatory relations among genes (e.g. gene A up-regulates gene B). At the same time, there is an increasing number of large-scale gene expression datasets, in which the mRNA transcript levels of tens of thousands of genes are measured at a number of time points, or under a number of different conditions. A number of studies have proposed to find gene regulatory networks from such datasets. Our method is a modification of th
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Oldewage, Elre Talea. "The perils of particle swarm optimization in high dimensional problem spaces." Diss., University of Pretoria, 2005. http://hdl.handle.net/2263/66233.

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Particle swarm optimisation (PSO) is a stochastic, population-based optimisation algorithm. PSO has been applied successfully to a variety of domains. This thesis examines the behaviour of PSO when applied to high dimensional optimisation problems. Empirical experiments are used to illustrate the problems exhibited by the swarm, namely that the particles are prone to leaving the search space and never returning. This thesis does not intend to develop a new version of PSO speci cally for high dimensional problems. Instead, the thesis investigates why PSO fails in high dimensional search spaces.
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Kaiten, Juan Carlos, Kara Stonehouse, and Sonja Niederhumer. "Large Scale Collaboration towards Strategic Sustainable Development." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2851.

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As humanity transitions towards a global society, opportunities and challenges have grown in equal magnitude. Severe, global depletion of ecosystems and social turmoil have initiated a widespread public response. If the many small actions were to connect and collaborate strategically, then a greater impact could be made on the system to move towards sustainability. Along with continuous technological and scientific development, society is discovering new ways of organizing itself for a more just, sustainable and harmonious world. This is giving birth to a large social body that is becoming awa
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Moon, Yoon Keon 1959. "High performance simulation-based optimization environment for large scale systems." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/282263.

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Modelling large scale systems with natural and artificial components requires storage of voluminous amounts of knowledge/information as well as computing speed for simulations to provide reliable answers in reasonable time. Computing technology is becoming powerful enough to support such high performance modelling and simulation. This dissertation proposes a high performance simulation based optimization environment to support the design and modeling of large scale systems with high levels of resolution. The proposed environment consists of three layers--modeling, simulation and searcher layer
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Schlick, Sandra. "Dynamic approach to competitive intelligence : case studies of large-scale Swiss telecom firms." Thesis, De Montfort University, 2016. http://hdl.handle.net/2086/12226.

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The research aim is to understand how the competitive intelligence (CI) process in large-scale Swiss telecom companies contributes to management decision-making. Studying CI activities of the Swiss large-scale telecom firms (Swisscom, Sunrise, Orange/Salt, Cablecom) in a dynamic European context offers useful insight into the critical challenges that service firms now face when developing intelligence in disruptive market contexts where aggressive competitive behaviour is evident. In considering CI theory, this study has reviewed perspectives drawn from research on the CI process, studies on k
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Books on the topic "Large scale swarm intelligence"

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Bouvry, Pascal. Intelligent Decision Systems in Large-Scale Distributed Environments. Springer Berlin Heidelberg, 2011.

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Elibol, Armagan. Efficient Topology Estimation for Large Scale Optical Mapping. Springer Berlin Heidelberg, 2013.

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Kołodziej, Joanna. Evolutionary Hierarchical Multi-Criteria Metaheuristics for Scheduling in Large-Scale Grid Systems. Springer Berlin Heidelberg, 2012.

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Sakawa, Masatoshi. Large Scale Interactive Fuzzy Multiobjective Programming: Decomposition Approaches. Physica-Verlag HD, 2000.

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Martin, Richard Kipp. Large Scale Linear and Integer Optimization: A Unified Approach. Springer US, 1999.

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Fernández, Juan A. Multi-Hierarchical Representation of Large-Scale Space: Applications to Mobile Robots. Springer Netherlands, 2001.

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An artificial intelligence approach to VLSI routing. Kluwer Academic Publishers, 1986.

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Hameurlain, Abdelkader. Transactions on Large-Scale Data- and Knowledge-Centered Systems VII. Springer Berlin Heidelberg, 2012.

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International Workshop on VLSI for Artificial Intelligence and Neural Networks (1990 Oxford, England). VLSI for artificial intelligence and neural networks. Plenum Press, 1991.

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Kowalski, Thaddeus J. An artificial intelligence approach to VLSI design. Kluwer Academic Publishers, 1985.

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Book chapters on the topic "Large scale swarm intelligence"

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Cheng, Shi, T. O. Ting, and Xin-She Yang. "Large-Scale Global Optimization via Swarm Intelligence." In Solving Computationally Expensive Engineering Problems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08985-0_10.

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Deng, Changshou, Xiaogang Dong, Yanlin Yang, Yucheng Tan, and Xujie Tan. "Differential Evolution with Novel Local Search Operation for Large Scale Optimization Problems." In Advances in Swarm and Computational Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20466-6_34.

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Belousov, Alexander A., Peter O. Skobelev, and Maksim E. Stepanov. "Network-Centric Approach to Real-Time Train Scheduling in Large-Scale Railway Systems." In Advances in Swarm and Computational Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20472-7_31.

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Kalempa, Vivian Cremer, Marco Antonio Simões Teixeira, André Schneider de Oliveira, and João Alberto Fabro. "Agile Experimentation of Robot Swarms in Large Scale." In Studies in Computational Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45956-7_4.

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Mohapatra, Prabhujit, Kedar Nath Das, and Santanu Roy. "An Improvised Competitive Swarm Optimizer for Large-Scale Optimization." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1595-4_47.

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Zhang, Li, Yu Zhu, Si Zhong, Rushi Lan, and Xiaonan Luo. "Multi-level Competitive Swarm Optimizer for Large Scale Optimization." In Security with Intelligent Computing and Big-data Services. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16946-6_15.

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Altshuler, Yaniv, Alex Pentland, and Alfred M. Bruckstein. "Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs." In Swarms and Network Intelligence in Search. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63604-7_8.

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Aote, Shailendra S., M. M. Raghuwanshi, and L. G. Malik. "A New Particle Swarm Optimizer with Cooperative Coevolution for Large Scale Optimization." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11933-5_88.

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Li, Yongfeng, Lingjie Li, Qiuzhen Lin, and Zhong Ming. "An Efficient Competitive Swarm Optimizer for Solving Large-Scale Multi-objective Optimization Problems." In Intelligent Computing Theories and Application. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84522-3_6.

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Xue, Zhibin, and Jianchao Zeng. "Circle Formation Control of Large-Scale Intelligent Swarm Systems in a Distributed Fashion." In Advances in Neural Networks – ISNN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01510-6_125.

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Conference papers on the topic "Large scale swarm intelligence"

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El-Abd, Mohammed. "Hybrid cooperative co-evolution for large scale optimization." In 2014 IEEE Symposium On Swarm Intelligence (SIS). IEEE, 2014. http://dx.doi.org/10.1109/sis.2014.7011815.

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Liang, J. J., and B. Y. Qu. "Large-scale portfolio optimization using multiobjective dynamic mutli-swarm particle swarm optimizer." In 2013 IEEE Symposium on Swarm Intelligence (SIS). IEEE, 2013. http://dx.doi.org/10.1109/sis.2013.6615152.

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Mohammad, R. Raeesi N., and Ziad Kobti. "A new strategy to detect variable interactions in large scale global optimization." In 2014 IEEE Symposium On Swarm Intelligence (SIS). IEEE, 2014. http://dx.doi.org/10.1109/sis.2014.7011812.

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Sherar, Matthew, and Farhana Zulkernine. "Particle swarm optimization for large-scale clustering on apache spark." In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017. http://dx.doi.org/10.1109/ssci.2017.8285208.

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Modiri, Arezoo, Xuejun Gu, Aaron Hagan, and Amit Sawant. "Improved swarm intelligence solution in large scale radiation therapy inverse planning." In 2015 IEEE Great Lakes Biomedical Conference (GLBC). IEEE, 2015. http://dx.doi.org/10.1109/glbc.2015.7158300.

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Affijulla, Shaik, and Sushil Chauhan. "Swarm intelligence solution to large scale thermal power plant Load Dispatch." In 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT 2011). IEEE, 2011. http://dx.doi.org/10.1109/icetect.2011.5760115.

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Guan, Shanwen, Rushi Lan, Yijie Zhu, Ruomei Wang, Yijie Zhu, and Ruomei Wang. "A Novel Group-based Swarm Optimizer for Large-Scale Optimization." In 2020 12th International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2020. http://dx.doi.org/10.1109/icaci49185.2020.9177743.

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Shahrouzi, Mohsen, and Alireza Salehi. "Design of Large-Scale Structures by an enhanced Metaheuristic utilizing Opposition-based Learning." In 2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC). IEEE, 2020. http://dx.doi.org/10.1109/csiec49655.2020.9237319.

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Han-Yu Xie, Qiang Yang, Xiao-Min Hu, and Wei-Neng Chen. "Cross-generation Elites Guided Particle Swarm Optimization for large scale optimization." In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2016. http://dx.doi.org/10.1109/ssci.2016.7850278.

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Zaeifi, Mehrnoosh, Malihe M. Farsangi, Ehsan Bijami, and Farzaneh Karami. "Hierarchical gradient based control optimized by shuffled frog leaping algorithm for large-scale systems." In 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC). IEEE, 2016. http://dx.doi.org/10.1109/csiec.2016.7482115.

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Reports on the topic "Large scale swarm intelligence"

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Knoblock, Craig, Steven Minton, and Ching-Chien Chen. Entity Bases: Large-Scale Knowledgebases for Intelligence Data. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada494978.

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Doo, Johnny. Unsettled Issues Concerning eVTOL for Rapid-response, On-demand Firefighting. SAE International, 2021. http://dx.doi.org/10.4271/epr2021017.

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Recent advancements of electric vertical take-off and landing (eVTOL) aircraft have generated significant interest within and beyond the traditional aviation industry, and many novel applications have been identified and are in development. One promising application for these innovative systems is in firefighting, with eVTOL aircraft complementing current firefighting capabilities to help save lives and reduce fire-induced damages. With increased global occurrences and scales of wildfires—not to mention the issues firefighters face during urban and rural firefighting operations daily—eVTOL tec
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