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Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

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ISBN: 9783658205393 9783658205409 Year: Pages: 201 DOI: https://doi.org/10.1007/978-3-658-20540-9 Language: English
Publisher: Springer Nature Grant: Philipps-Universität Marburg
Subject: Mathematics
Added to DOAB on : 2018-06-29 15:12:23
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This book covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining.

Swarm Robotics

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ISBN: 9783038979227 9783038979234 Year: Pages: 310 DOI: 10.3390/books978-3-03897-923-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2019-06-26 08:44:06
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Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties:

Keywords

3D model identification --- shape normalization --- weighted implicit shape representation --- panoramic view --- scale-invariant feature transform --- optimization --- meta-heuristic --- parallel technique --- Swarm intelligence algorithm --- artificial flora (AF) algorithm --- bionic intelligent algorithm --- particle swarm optimization --- artificial bee colony algorithm --- swarm robotics --- search --- surveillance --- behaviors --- patterns --- comparison --- swarm behavior --- Swarm Chemistry --- self-organization --- asymmetrical interaction --- genetic algorithm --- cooperative target hunting --- multi-AUV --- improved potential field --- surface-water environment --- signal source localization --- multi-robot system --- event-triggered communication --- consensus control --- time-difference-of-arrival (TDOA) --- Cramer–Rao low bound (CRLB) --- optimal configuration --- UAV swarms --- path optimization --- multiple robots --- formation --- sliding mode controller --- nonlinear disturbance observer --- system stability --- formation control --- virtual structure --- formation reconfiguration --- multi-agents --- robotics --- unmanned aerial vehicle --- swarm intelligence --- particle swarm optimization --- search algorithm --- underwater environment --- sensor deployment --- event-driven coverage --- fish swarm optimization --- congestion control --- modular robots --- self-assembly robots --- environmental perception --- target recognition --- autonomous docking --- formation control --- virtual linkage --- virtual structure --- formation reconfiguration --- mobile robots --- robotics --- swarm robotics --- formation control --- coordinate motion --- obstacle avoidance --- n/a

Evolutionary Computation

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ISBN: 9783039219285 9783039219292 Year: Pages: 424 DOI: 10.3390/books978-3-03921-929-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:16
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Computational intelligence is a general term for a class of algorithms designed by nature's wisdom and human intelligence. Computer scientists have proposed many computational intelligence algorithms with heuristic features. These algorithms either mimic the evolutionary processes of the biological world, mimic the physiological structure and bodily functions of the organism,

Keywords

artificial bee colony algorithm (ABC) --- cloud model --- normal cloud model --- Y conditional cloud generator --- global optimum --- evolution --- computation --- urban design --- biology --- shape grammar --- architecture --- SPEA 2 --- energy-efficient job shop scheduling --- dispatching rule --- nonlinear convergence factor --- mutation operation --- whale optimization algorithm --- particle swarm optimization --- confidence term --- random weight --- benchmark functions --- t-test --- success rates --- average iteration times --- set-union knapsack problem --- moth search algorithm --- transfer function --- discrete algorithm --- evolutionary multi-objective optimization --- convergence point --- acceleration search --- evolutionary computation --- optimization --- bat algorithm (BA) --- bat algorithm with multiple strategy coupling (mixBA) --- CEC2013 benchmarks --- Wilcoxon test --- Friedman test --- facility layout design --- single loop --- monarch butterfly optimization --- slicing tree structure --- material handling path --- integrated design --- wireless sensor networks (WSNs) --- DV-Hop algorithm --- multi-objective DV-Hop localization algorithm --- NSGA-II-DV-Hop --- first-arrival picking --- fuzzy c-means --- particle swarm optimization --- range detection --- minimum total dominating set --- evolutionary algorithm --- genetic algorithm --- local search --- constrained optimization problems (COPs) --- evolutionary algorithms (EAs) --- firefly algorithm (FA) --- stochastic ranking (SR) --- Artificial bee colony --- swarm intelligence --- elite strategy --- dimension learning --- global optimization --- DE algorithm --- ?-Hilbert space --- topology structure --- quantum uncertainty property --- numerical simulation --- whale optimization algorithm --- flexible job shop scheduling problem --- nonlinear convergence factor --- adaptive weight --- variable neighborhood search --- elephant herding optimization --- EHO --- swarm intelligence --- individual updating strategy --- large-scale --- benchmark --- diversity maintenance --- particle swarm optimizer --- entropy --- large scale optimization --- minimum load coloring --- memetic algorithm --- evolutionary --- local search --- particle swarm optimization --- large-scale optimization --- adaptive multi-swarm --- diversity maintenance --- deep learning --- convolutional neural network --- rock types --- automatic identification --- monarch butterfly optimization --- greedy optimization algorithm --- global position updating operator --- 0-1 knapsack problems

Distributed Energy Resources Management 2018

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ISBN: 9783039281701 9783039281718 Year: Pages: 286 DOI: 10.3390/books978-3-03928-171-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: General and Civil Engineering --- Technology (General)
Added to DOAB on : 2020-01-30 16:39:46
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The Special Issue Distributed Energy Resources Management 2018 includes 13 papers, and is a continuation of the Special Issue Distributed Energy Resources Management. The success of the previous edition shows the unquestionable relevance of distributed energy resources in the operation of power and energy systems at both the distribution level and at the wider power system level. Improving the management of distributed energy resources makes it possible to accommodate the higher penetration of intermittent distributed generation and electric vehicle charging. Demand response programs, namely the ones with a distributed nature, allow the consumers to contribute to the increased system efficiency while receiving benefits. This book addresses the management of distributed energy resources, with a focus on methods and techniques to achieve an optimized operation, in order to aggregate the resources namely in the scope of virtual power players and other types of aggregators, and to remunerate them. The integration of distributed resources in electricity markets is also addressed as an enabler for their increased and efficient use.

Keywords

clustering --- demand Response --- distributed generation --- smart grids --- demand-side management --- multi-agent system --- distributed coordination --- distributed energy resources --- swarm intelligence --- virtual power plant --- distributed energy resources --- multi-agent technology --- bidding strategy --- stackelberg dynamic game --- aggregator --- distribution system operator --- distributed energy resources --- local flexibility market --- flexibility service --- distributed energy --- comprehensive benefits --- multi-agent synergetic estimation --- synergistic optimization strategy --- control system --- fault-tolerant control --- algorithm design and analysis --- IoT (Internet of Things) --- nonlinear control --- optimization --- DSM --- microgrid --- solar --- wind --- teaching-learning --- microgrid --- energy storage system --- distributed generator --- frequency control --- active power control --- autonomous control --- droop control --- frequency bus-signaling --- batteries --- energy storage --- microgrids --- optimal scheduling --- particle swarm optimization --- power system management --- smart grid --- supply and demand --- trade agreements --- low voltage networks --- multi-period optimal power flow --- multi-temporal optimal power flow --- active distribution networks --- unbalanced networks --- indoor environment quality --- occupant comfort --- building climate control --- healthy building --- energy efficiency --- adaptability --- decentralized energy management system --- local energy trading --- multi-agent system --- optimization --- smart grid --- demand response --- distributed generation --- particle swarm optimization --- prosumer --- n/a

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