Modular robots offer an important benefit in evolutionary robotics, which is to quickly evaluate evolved morphologies and control systems in reality. However, artificial evolution of simulated modular robotics is a di...
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ISBN:
(纸本)9783319558493;9783319558486
Modular robots offer an important benefit in evolutionary robotics, which is to quickly evaluate evolved morphologies and control systems in reality. However, artificial evolution of simulated modular robotics is a difficult and time consuming task requiring significant computational power. While artificial evolution in virtual creatures has made use of powerful generative encodings, here we investigate how a generative encoding and direct encoding compare for the evolution of locomotion in modular robots when the number of robotic modules changes. Simulating less modules would decrease the size of the genome of a direct encoding while the size of the genome of the implemented generative encoding stays the same. We found that the generative encoding is significantly more efficient in creating robot phenotypes in the initial stages of evolution when simulating a maximum of 5, 10, and 20 modules. This not only confirms that generative encodings lead to decent performance more quickly, but also that when simulating just a few modules a generative encoding is more powerful than a direct encoding for creating robotic structures. Over longer evolutionary time, the difference between the encodings no longer becomes statistically significant. This leads us to speculate that a combined approach -starting with a generative encoding and later implementing a direct encoding - can lead to more efficient evolved designs.
Cyber-Physical Systems (CPS) find applications in a number of large-scale, safety-critical domains e.g. transportation, smart cities, etc. As a matter of fact, the increasing interactions amongst different CPS are sta...
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ISBN:
(纸本)9781450344876
Cyber-Physical Systems (CPS) find applications in a number of large-scale, safety-critical domains e.g. transportation, smart cities, etc. As a matter of fact, the increasing interactions amongst different CPS are starting to generate unpredictable behaviors and emerging properties, often leading to unforeseen and/or undesired results. Rather than being an unwanted byproduct, these interactions could, however, become an advantage if they were explicitly managed, and accounted, since the early design stages. The CPSwarm project, presented in this paper, aims at tackling these kinds of challenges by easing development and integration of complex herds of heterogeneous CPS. Thanks to CPSwarm, systems designed through a combination of existing and emerging tools, will collaborate on the basis of local policies and exhibit a collective behavior capable of solving complex, real-world, problems. Three real-world use cases will demonstrate the validity of foundational assumptions of the presented approach as well as the viability of the developed tools and methodologies.
This paper concentrates on the optimization of synthesis parameters of generalized predictive control (GPC) using genetic algorithms (GAs), namely the minimum prediction horizon, the maximum prediction horizon, the co...
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ISBN:
(纸本)9781538615164
This paper concentrates on the optimization of synthesis parameters of generalized predictive control (GPC) using genetic algorithms (GAs), namely the minimum prediction horizon, the maximum prediction horizon, the control horizon and the cost weighting factor. This, aims to improve the closed-loop performances. To validate this technique, our application relates to control the speed of asynchronous motor. The results obtained are discussed and presented.
Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Ope...
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ISBN:
(纸本)9781450349208
Here we propose an evolutionary algorithm that self modifies its operators at the same time that candidate solutions are evolved. This tackles convergence and lack of diversity issues, leading to better solutions. Operators are represented as trees and are evolved using genetic programming (GP) techniques. The proposed approach is tested with real benchmark functions and an analysis of operator evolution is provided.
Dielectric elastomer (DE) is a type of soft actuating material, the shape of which can be changed under electrical voltage stimuli. DE materials have great potential in applications involving energy harvesters, micro ...
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ISBN:
(数字)9781510608122
ISBN:
(纸本)9781510608115;9781510608122
Dielectric elastomer (DE) is a type of soft actuating material, the shape of which can be changed under electrical voltage stimuli. DE materials have great potential in applications involving energy harvesters, micro manipulators, and adaptive optics. In this paper, a stripe DE actuator with integrated sensing and actuation is designed and fabricated, and characterized through several experiments. Considering the actuator's capacitor like structure and its deform mechanism, detecting the actuator's displacement through the actuator's circuit feature is a potential approach. A self-sensing scheme that adds a high frequency probing signal into actuation signal is developed. A fast Fourier transform (FFT) algorithm is used to extract the magnitude change of the probing signal, and a non-linear fitting method and artificial neural network (ANN) approach are utilized to reflect the relationship between the probing signal and the actuator's displacement. Experimental results showed this structure has capability of performing self-sensing and actuation, simultaneously. With an enhanced ANN, the self-sensing scheme can achieve 2.5% accuracy.
In this paper, general FIR filters are designed using multiobjective Artificial Bee Colony algorithm. Spherical pruning (SP) and physical programming (PP) techniques are combined together in the implementation of mult...
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ISBN:
(纸本)9781509055388
In this paper, general FIR filters are designed using multiobjective Artificial Bee Colony algorithm. Spherical pruning (SP) and physical programming (PP) techniques are combined together in the implementation of multiobjective Artificial Bee Colony algorithm. Physical programming converts the design objectives into an intuitive language and spherical pruning maintains diversity in the Pareto front. The design of general FIR filters require simultaneous optimization of magnitude and group delay errors and therefore can be formulated as a Multiobjective Optimization (MOO) problem. All the non-dominated solutions of the general FIR design problem can be approximated into a Pareto front. Numerical results show that, multiobjective Artificial Bee Colony algorithm can achieve lower passband, stopband, group delay errors when compared to those of spherical pruning Multiobjective Differential Evolution (spMODE-II).
HNCO consists of a C++ library, command-line tools, and scripts for the optimization of black box functions defined on fixed-length bit vectors. It aims at being flexible, fast, simple, and robust. The library provide...
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ISBN:
(纸本)9781450349390
HNCO consists of a C++ library, command-line tools, and scripts for the optimization of black box functions defined on fixed-length bit vectors. It aims at being flexible, fast, simple, and robust. The library provides classes for functions, populations, neighborhoods, and algorithms. It currently includes 22 concrete functions and 18 concrete algorithms. The command-line tools expose most of the library to the user without the need for programming. One of the goals of HNCO is to automate experiments and favor reproducible research. HNCO comes with experiments designed to tune or compare algorithms. Scripts run all the simulations in an experiment and generate a report. The source code of HNCO is published under the GNU LGPL 3 license.
Back propagation neural network(BP neural network) is a type of multi-layer feed forward network which spread positively, while the error spread backwardly. Since BP network has advantages in learning and storing the ...
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ISBN:
(数字)9781510609921
ISBN:
(纸本)9781510609914;9781510609921
Back propagation neural network(BP neural network) is a type of multi-layer feed forward network which spread positively, while the error spread backwardly. Since BP network has advantages in learning and storing the mapping between a large number of input and output layers without complex mathematical equations to describe the mapping relationship, it is most widely used. BP can iteratively compute the weight coefficients and thresholds of the network based on the training and back propagation of samples, which can minimize the error sum of squares of the network. Since the boundary of the computed tomography (CT) heart images is usually discontinuous, and it exist large changes in the volume and boundary of heart images, The conventional segmentation such as region growing and watershed algorithm can't achieve satisfactory results. Meanwhile, there are large differences between the diastolic and systolic images. The conventional methods can'tt accurately classify the two cases. In this paper, we introduced BP to handle the segmentation of heart images. We segmented a large amount of CT images artificially to obtain the samples, and the BP network was trained based on these samples. To acquire the appropriate BP network for the segmentation of heart images, we normalized the heart images, and extract the gray-level information of the heart. Then the boundary of the images was input into the network to compare the differences between the theoretical output and the actual output, and we reinput the errors into the BP network to modify the weight coefficients of layers. Through a large amount of training, the BP network tend to be stable, and the weight coefficients of layers can be determined, which means the relationship between the CT images and the boundary of heart.
This paper presents a novel approach to the source seeking problem, where a group of mobile agents tries to locate the maximum of a scalar field defined on the space in which they are moving. The agents know their pos...
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This paper presents a novel approach to the source seeking problem, where a group of mobile agents tries to locate the maximum of a scalar field defined on the space in which they are moving. The agents know their position and the local value of the field, and by communicating with their neighbors estimate the gradient direction of the field. A distributed cooperative control scheme is then designed that drives the group towards the maximum while maintaining a specified formation. Previously proposed control schemes that are based on a combination of H-infinity-optimal formation control and local gradient estimation suffer from premature convergence to local maxima. To overcome this problem, here the use of particle swarm optimization for locating the global maximum is proposed. Agents take the role of particles and an information flow filter approach is employed to separate the consensus dynamics from the local feedback loops governing the agent dynamics. Stability of the overall scheme is established based on the small gain theorem, and by decomposing the synthesis problem for the distributed information flow filter the problem size is reduced to that of a single agent. Simulation results with multiple maxima and quadrocopter models as agents illustrate the practicality of the approach. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
MATLAB (R) builds in a number of derivative-free optimisers (DFOs), conveniently providing tools beyond conventional optimisation means. However, with the increase of available DFOs and being compounded by the fact th...
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ISBN:
(数字)9783319633121
ISBN:
(纸本)9783319633121;9783319633114
MATLAB (R) builds in a number of derivative-free optimisers (DFOs), conveniently providing tools beyond conventional optimisation means. However, with the increase of available DFOs and being compounded by the fact that DFOs are often problem dependent and parameter sensitive, it has become challenging to determine which one would be most suited to the application at hand, but there exist no comparisons on MATLAB DFOs so far. In order to help engineers use MATLAB for their applications without needing to learn DFOs in detail, this paper evaluates the performance of all seven DFOs in MATLAB and sets out an amalgamated benchmark of multiple benchmarks. The DFOs include four heuristic algorithms -simulated annealing, particle swarm optimization (PSO), the genetic algorithm (GA), and the genetic algorithm with elitism (GAe), and three direct-search algorithms -Nelder-Mead's simplex search, pattern search (PS) and Powell's conjugate search. The five benchmarks presented in this paper exceed those that have been reported in the literature. Four benchmark problems widely adopted in assessing evolutionary algorithms are employed. Under MATLAB's default settings, it is found that the numerical optimisers Powell is the aggregative best on the unimodal Quadratic Problem, PSO on the lower dimensional Scaffer Problem, PS on the lower dimensional Composition Problem, while the extra-numerical genotype GAe is the best on the Varying Landscape Problem and on the other two higher dimensional problems. Overall, the GAe offers the highest performance, followed by PSO and Powell. The amalgamated benchmark quantifies the advantage and robustness of heuristic and population-based optimisers (GAe and PSO), especially on multimodal problems.
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