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A Tuning Free Approach to Multi-guide Particle Swarm Optimization

A Tuning Free Approach to Multi-guide Particle Swarm Optimiz...

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ieee symposium Series on computational intelligence (SSCI)

作者： Kyle Erwin Andries Engelbrecht Computer Science Division Stellenbosh University South Africa

ISBN: (纸本)9781728190495

Multi-guide particle swarm optimization (MGPSO) is a highly competitive algorithm for multi-objective optimization problems. MGPSO has been shown to perform better than or similar to several state-of-the-art multi-objective algorithms for a variety of multi-objective optimization problems (MOOPs). When comparing algorithmic performance it is recommended that the control parameters of each algorithm be tuned to the problem. However, control parameter tuning is often an expensive and time-consuming process. Recent work has derived the theoretical stability conditions on the MGPSO control parameters to guarantee order-1 and order-2 stability. This paper investigates an approach to randomly sample control parameter values for MGPSO that satisfy these stability conditions. It was shown that the proposed approach yields similar performance to that of MGPSO using tuned parameters, and therefore is a viable alternative to parameter tuning.

关键词： Stability criteria Process control Benchmark testing Particle swarm optimization Tuning Optimization computational intelligence

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BIS: A New Swarm-Based Optimisation Algorithm

BIS: A New Swarm-Based Optimisation Algorithm

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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Varna, Fevzi Tugrul Husbands, Phil Univ Sussex Dept Informat Brighton E Sussex England

ISBN: (纸本)9781728125473

This paper presents a novel swarm-based search algorithm: the bio-breeding intelligent swarm (BIS) algorithm. BIS agents imitate the offspring and maturity phases of the typical lifecycle of an animal. As in nature, the BIS algorithm makes gender distinction among agents and the main search strategy exploits competition between male agents in an attempt to provide a better location for females. BIS agents embark on various nature-inspired mating strategies and the inspiration for the reproduction model is derived from temperature-dependent sex determination (TSD), a reptilian reproduction system. The BIS algorithm's TSD inspired reproduction model enables female agents to control the gender of offsprings based on guidance provided by their male mates, subsequently resulting in regulation of the male-female ratio in the swarm which in turn autocontrols the balance of exploration and exploitation within the population of agents. The efficiency of the BIS algorithm was tested over a wide range of benchmarks including unconstrained high dimensional and real-world problems. The BIS algorithm performed very well in comparison with a number of leading population-based stochastic search methods, finding the highest number of global optimums.

关键词： swarm intelligence optimisation metaheuristics

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An Ultrafast DC-DC Converter Design Methodology Combining Piecewise Linear Analytical Modelling and Genetic Algorithms

An Ultrafast DC-DC Converter Design Methodology Combining Pi...

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ieee Workshop on Computers in Power Electronics

作者： Niklas Fritz David Heidenberger Carsten Fronczek David Bü ndgen Rik W. de Doncker Institute for Power Electronics and Electrical Drives RWTH Aachen University Germany

Design automation is an important trend in power electronics that often relies on multi-objective optimization and analytical modelling. For the optimization, meta-heuristic techniques using genetic algorithms or artificial intelligence become increasingly important. This paper proposes an imple-mentation technique for analytical converter models utilizing multi-dimensional matrices to evaluate designs throughout their entire operating area. Moreover, the time domain is included in the matrices, assuming piecewise linear waveforms, and calculation procedures are introduced to obtain RMS values, Fourier spectra, and many other parameters. The converter losses are calculated as well. This makes the proposed implementation technique applicable to the modelling of a variety of converter topologies. The proposed method is combined with genetic opti-mization, and an exemplary optimization of a 100 kW three-phase dual-active-bridge converter is presented.

关键词： Analytical models computational modeling Genetics Power electronics Topology computational efficiency Matrix converters

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An Artificial intelligence Driven Method for Power System control Based on the Cloud-Edge Collaboration Architecture

An Artificial Intelligence Driven Method for Power System Co...

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Digital Twins and Parallel intelligence (DTPI), ieee International Conference on

作者： Wenchen Li Yanhao Huang Chunjiang He Chenglong Xu Peidong Xu Tianlu Gao Jun Zhang China Electric Power Research Institute Beijing China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

The new-type power system with the high penetration of renewable energy accessed is of strong uncertainty and complexity, which can be challenging for the traditional methods to control. It's significant to introduce artificial intelligence to meet the challenge. This paper proposes a cloud-edge collaborative framework based on multi-agent deep reinforcement learning for power system regulation. Using an unsupervised clustering algorithm, the power grid is decomposed into several sub-networks according to the geographical relationship. Then, edge computing platforms are set up on each sub-network, where agents are deployed. The distributed control problem of each subnetwork can be modeled as a Markov decision model. The global observation information of the system is delivered to each edge platform through the cloud computing center, and all agents are trained to learn the best regulation strategy according to the global information. The proposed method can effectively decompose the centralized tasks and transfer them to the edge side, alleviating the pressure on the cloud center and enhancing the robustness of system operation.

关键词： Training Deep learning Cloud computing computational modeling Collaboration Clustering algorithms Reinforcement learning

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Fünfiiber-Drone: A Modular Open-Platform 18-grams Autonomous Nano-Drone

Fünfiiber-Drone: A Modular Open-Platform 18-grams Autonomou...

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2021 Design, automation and Test in Europe Conference and Exhibition, DATE 2021

作者： Miiller, Hanna Palossi, Daniele Mach, Stefan Conti, Francesco Benini, Luca Integrated Systems Laboratory Eth Zürich Switzerland Dalle Molle Institute for Artificial Intelligence University of Lugano and Supsi Switzerland University of Bologna Department of Electrical Electronic and Information Engineering Italy

ISBN: (纸本)9783981926354

Miniaturizing an autonomous robot is a challenging task - not only the mechanical but also the electrical components have to operate within limited space, payload, and power. Furthermore, the algorithms for autonomous navigation, such as state-of-the-art (SoA) visual navigation deep neural networks (DNNs), are becoming increasingly complex, striving for more flexibility and agility. In this work, we present a sensor-rich, modular, nano-sized Unmanned Aerial Vehicle (UAV), almost as small as a five Swiss Franc coin - called Fünfliber - with a total weight of 18g and 7.2cm in diameter. We conceived our UAV as an open-source hardware robotic platform, controlled by a parallel ultra-low power (PULP) system-on-chip (SoC) with a wide set of onboard sensors, including three cameras (i.e., infrared, optical flow, and standard QVGA), multiple Time-of-Flight (ToF) sensors, a barometer, and an inertial measurement unit. Our system runs the tasks necessary for a flight controller (sensor acquisition, state estimation, and low-level control), requiring only 10% of the computational resources available aboard, consuming only 9mW - 13x less than an equivalent Cortex M4-based system. Pushing our system at its limit, we can use the remaining onboard computational power for sophisticated autonomous navigation workloads, as we showcase with an SoA DNN running at up to 18Hz, with a total electronics' power consumption of 271mW. © 2021 EDAA.

关键词： Deep neural networks

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Elastic and Energy-Efficient Virtual Machine Allocation on Edge Servers for Internet of Vehicles

Elastic and Energy-Efficient Virtual Machine Allocation on E...

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International symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Xianger Li Aiwen Huang Yiqi Fan Zhihai Tang Le Chang Tian Wang School of Automation Guangdong University of Technology Guangzhou China Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Edge computing and virtualization are two important technologies to support future Internet of Vehicles (IoV). Edge servers deployed at Road-Side Units (RSUs) employ Virtual Machines (VMs) to provision realtime, close-distance and low-latency task offloading services to the vehicles on road. Nevertheless, the spatiotemporal traffic dynamics introduce a great challenge in managing the VMs. The local traffic surge can overwhelm the VM capacity of a server, and the VMs have to be frequently powered on and off to catch up with the traffic change, which consumes a great amount of energy. To address these issues, we propose an elastic and energy-efficient VM allocation strategy on edge servers for IoV systems. First, we conduct a trace study on a real traffic trajectory dataset, which reveals the pervasive complementary traffic patterns across neighboring regions. Such insight inspires us to propose an elastic VM allocation strategy by expanding the service range of an edge server to its neighbors. We build a mathematical model of the VM allocation problem, and design an online heuristic algorithm to solve it based on the temporal difference between the current number of VMs and the traffic load in the following round in each region. Extensive trace-driven simulations verify that our algorithm can achieve an up to 35.1%-reduction of the energy consumption compared with other popular approaches, while ensuring that all the vehicles are served by the VMs.

关键词： Telecommunication traffic Traffic control Virtual machines Mathematical models Energy efficiency Trajectory Servers Resource management Vehicle dynamics Internet of Vehicles

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Centroidal Aerodynamic Modeling and control of Flying Multibody Robots

Centroidal Aerodynamic Modeling and Control of Flying Multib...

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ieee International Conference on Robotics and automation (ICRA)

作者： Tong Hui Antonello Paolino Gabriele Nava Giuseppe L' Erario Fabio Di Natale Fabio Bergonti Francesco Braghin Daniele Pucci Artificial and Mechanical Intelligence Istituto Italiano di Tecnologia Genova Italy Department of Mechanical Engineering Politecnico di Milano Milan Italy Department of Industrial Engineering Universit&#x00E0 degli Studi di Napoli Federico II Naples Italy School of Computer Science Univ. of Manchester Manchester U.K.

This paper presents a modeling and control frame-work for multibody flying robots subject to non-negligible aero-dynamic forces acting on the centroidal dynamics. First, aero-dynamic forces are calculated during robot flight in different operating conditions by means of computational Fluid Dynamics (CFD) analysis. Then, analytical models of the aerodynamics coefficients are generated from the dataset collected with CFD analysis. The obtained simplified aerodynamic model is also used to improve the flying robot control design. We present two control strategies: compensating for the aerodynamic effects via feedback linearization and enforcing the controller robustness with gain-scheduling. Simulation results on the jet-powered humanoid robot iRonCub validate the proposed approach.

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Research on synergy based on correlation analysis of weighted samples of grasping action

Research on synergy based on correlation analysis of weighte...

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2021 China automation Congress, CAC 2021

作者： Yu, Jianjun Zhang, Zihao Zuo, Guoyu Liu, Gengyuan Li, Xuchen Beijing Key Laboratory of Computational Intelligence and Intelligent System College of Electronic and Control Engineering Beijing University of Technology Beijing China

ISBN: (纸本)9781665426473

The main research goal of underactuated manipulator is to reduce the number of controllers and improve the bionic ability. In this kind of research, underactuated hand design synergy is the mainstream research direction in recent years. Human hand synergy space is a parameterized representation of human hand motion based on a large number of human hand motion data In the past synergy meta-analysis, samples were selected only based on grasping classification without considering people's daily habits, which led to the lack of bionic ability. In this paper, based on the most authoritative 33 kinds of grabbing classification, the samples are weighted and reconstructed according to the daily use habits;Principal component analysis is used to analyze the reconstructed samples, and the correlation between joints is calculated to obtain the synergy of human hand The comparative experiments show that the underactuated transmission scheme based on the synergy design of reconstructed samples has higher similarity and stronger bionic ability than the ordinary sample scheme in most grasping scenes. On this basis, this paper studies the following ability between fingers through the calculation of correlation coefficient and clustering method, so as to further reduce the number of controllers. © 2021 ieee

关键词： Principal component analysis

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Soft-Sensing Method of Effluent Ammonia Nitrogen Based on Emotional Neural Network

Soft-Sensing Method of Effluent Ammonia Nitrogen Based on Em...

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2021 China automation Congress, CAC 2021

作者： Zhang, Hongye Yang, Cuili Qiao, Junfei Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China

ISBN: (纸本)9781665426473

In the process of wastewater treatment process (WWTP), the concentration of effluent ammonia nitrogen is one of the important indexes to test whether the water quality reaches the standard. Soft sensing technology is a method which can use some easily measurable auxiliary variables to predict key variables online. In this paper, we designed a soft sensing model of effluent NH4-N based on radial basis emotional neural network (RBENN), and then a feature selection (FS) method of sticky binary particle swarm optimization (SBPSO) algorithm based on mutual information (MI) and symmetric uncertainty (SU) is proposed to obtain the appropriate feature variables while balancing correlation and redundancy, and then the network parameters are optimized by comprehensive learning particle swarm optimization (CLPSO) algorithm. Simulation results show that the proposed method effectively improves the prediction performance of the model. © 2021 ieee

关键词： Effluents

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Hybrid Differential Dynamic Programming for Planar Manipulation Primitives

Hybrid Differential Dynamic Programming for Planar Manipulat...

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ieee International Conference on Robotics and automation (ICRA)

作者： Doshi, Neel Hogan, Francois R. Rodriguez, Alberto MIT Intelligence Community Postdoctoral Res Fellowshi 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Mech Engn Cambridge MA 02139 USA

ISBN: (纸本)9781728173955

We present a hybrid differential dynamic programming (DDP) algorithm for closed-loop execution of manipulation primitives with frictional contact switches. Planning and control of these primitives is challenging as they are hybrid, under-actuated, and stochastic. We address this by developing hybrid DDP both to plan finite horizon trajectories with a few contact switches and to create linear stabilizing controllers. We evaluate the performance and computational cost of our framework in ablations studies for two primitives: planar pushing and planar pivoting. We find that generating pose-to-pose closed-loop trajectories from most configurations requires only a couple (one to two) hybrid switches and can be done in reasonable time (one to five seconds). We further demonstrate that our controller stabilizes these hybrid trajectories on a real pushing system. A video describing our work can be found at https://***/YGSe4cUfq6Q.

关键词： Trajectories

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