Machine scheduling is a critical problem in industries where products are custom-designed. The wide range of products, the lack of previous experiences in manufacturing, and the several conflicting criteria used to ev...
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Machine scheduling is a critical problem in industries where products are custom-designed. The wide range of products, the lack of previous experiences in manufacturing, and the several conflicting criteria used to evaluate the quality of the schedules define a huge search space. Furthermore, production complexity and human influence in each manufacturing step make time estimations difficult to obtain thus reducing accuracy of schedules. The solution described in this paper combines evolutionary computing and neural networks to reduce the impact of (i) the huge search space that the multi-objective optimization must deal with and (ii) the inherent problem of computing the processing times in a domain like custom manufacturing. Our hybrid approach obtains near optimal schedules through the Non-dominated Sorting Genetic Algorithm II (NSGA-II) combined with time estimations based on multilayer perceptron neural networks. (C) 2010 Elsevier B. V. All rights reserved.
This article addresses the preliminary robust design of a small-scale re-entry unmanned space vehicle by means of a hybrid optimization technique. The approach, developed in this article, closely couples an evolutiona...
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This article addresses the preliminary robust design of a small-scale re-entry unmanned space vehicle by means of a hybrid optimization technique. The approach, developed in this article, closely couples an evolutionarymulti-objective algorithm with a direct transcription method for optimal control problems. The evolutionary part handles the shape parameters of the vehicle and the uncertain objective functions, while the direct transcription method generates an optimal control profile for the re-entry trajectory. Uncertainties on the aerodynamic forces and characteristics of the thermal protection material are incorporated into the vehicle model, and a Monte-Carlo sampling procedure is used to compute relevant statistical characteristics of the maximum heat flux and internal temperature. Then, the hybrid algorithm searches for geometries that minimize the mean value of the maximum heat flux, the mean value of the maximum internal temperature, and the weighted sum of their variance: the evolutionary part handles the shape parameters of the vehicle and the uncertain functions, while the direct transcription method generates the optimal control profile for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are utilized to approximate the aerodynamic forces required by the optimal control solver. The artificial neural networks are trained and updated by means of a multi-fidelity approach: initially a low-fidelity analytical model, fitted on a waverider type of vehicle, is used to train the neural networks, and through the evolution a mix of analytical and computational fluid dynamic, high-fidelity computations are used to update it. The data obtained by the high-fidelity model progressively become the main source of updates for the neural networks till, near the end of the optimization process, the influence of the data obtained by the analytical model is practically nullified. On the basis of preli
The field of algorithmic self-assembly is concerned with the design and analysis of self-assembly systems from a computational perspective, that is, from the perspective of mathematical problems whose study may give i...
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The field of algorithmic self-assembly is concerned with the design and analysis of self-assembly systems from a computational perspective, that is, from the perspective of mathematical problems whose study may give insight into the natural processes through which elementary objects self-assemble into more complex ones. One of the main problems of algorithmic self-assembly is the minimum tile set problem, which in the extended formulation we consider, here referred to as MTSP, asks for a collection of types of elementary objects (called tiles) to be found for the self-assembly of an object having a pre-established shape. Such a collection is to be as concise as possible, thus minimizing supply diversity, while satisfying a set of stringent constraints having to do with important properties of the self-assembly process from its tile types. We present a study of what, to the best of our knowledge, is the first practical approach to MTSP. Our study starts with the introduction of an evolutionary heuristic to tackle MTSP and includes selected results from extensive experimentation with the heuristic on the self-assembly of simple objects in two and three dimensions, including the possibility of tile rotation. The heuristic we introduce combines classic elements from the field of evolutionary computation with a problem-specific variant of Pareto dominance into a multi-objective approach to MTSP.
A practical question in industry in designing or re-designing a production system is: how small can intermediate buffers be to ensure the desired production rate? This topic is usually called optimal buffer allocation...
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ISBN:
(纸本)9789077381632
A practical question in industry in designing or re-designing a production system is: how small can intermediate buffers be to ensure the desired production rate? This topic is usually called optimal buffer allocation as the goal is to allocate the minimum buffer capacities to optimize the performance of the line. This paper presents a case study of using simulation based evolutionarymulti-objective optimization to determine the optimal buffer capacities and positions in the re-configuration of a real-world truck axle assembly line in an automobile manufacturer. The case study has not only revealed the applicability of the methodology in seeking optimal configurations in a truly multi-objective context, it also illustrates how additional important knowledge was gained by analyzing the optimization results in the objective space.
Our SPARTEN (Spatially Produced Airspace Routes from Tactical Evolved Networks) tool generates coordinated mission plans for constellations of unmanned aerial vehicles by allowing the mission planner to specify the im...
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ISBN:
(纸本)9781450305570
Our SPARTEN (Spatially Produced Airspace Routes from Tactical Evolved Networks) tool generates coordinated mission plans for constellations of unmanned aerial vehicles by allowing the mission planner to specify the importance of each objective for each mission. Using an evolutionary algorithm-based, multi-objective optimization technique, we consider factors such as area of analysis coverage, restricted operating zones, maximum ground control station range, adverse weather effects, military terrain value, airspace collision avoidance, path linearity, named area of analysis emphasis, and sensor performance. By employing novel visualizations using geographic information systems to represent their effectiveness, we help the user "look under the hood" of the algorithms and understand the viability and effectiveness of the mission plans to identify coverage gaps and other inefficiencies. In this paper, we apply multi-objective evolutionary algorithms to the air mission planning domain, with a focus on the visualization components.
This paper addresses the preliminary robust design of a small-medium scale re-entry unmanned space vehicle. A hybrid optimisation technique is proposed that couples an evolutionarymulti-objective algorithm with a dir...
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ISBN:
(纸本)9781450305570
This paper addresses the preliminary robust design of a small-medium scale re-entry unmanned space vehicle. A hybrid optimisation technique is proposed that couples an evolutionarymulti-objective algorithm with a direct transcription method for optimal control problems. Uncertainties on the aerodynamic forces and vehicle mass are integrated in the design process and the hybrid algorithm searches for geometries that minimise the mean value of the maximum heat flux, the mean value of the maximum achievable distance, and the variance of the maximum heat flux. The evolutionary part handles the system design parameters of the vehicle and the uncertain functions, while the direct transcription method generates optimal control profiles for the re-entry trajectory of each individual of the population. During the optimisation process, artificial neural networks are used to approximate the aerodynamic forces required by the direct transcription method. The artificial neural networks are trained and updated by means of a multi-fidelity, evolution control approach.
Signal decomposition techniques prove to be useful in the analysis of neural activity, as they allow for identification of supposedly distinct neuronal structures (ie., sources of activity). Applied to measurements of...
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ISBN:
(纸本)142440133X
Signal decomposition techniques prove to be useful in the analysis of neural activity, as they allow for identification of supposedly distinct neuronal structures (ie., sources of activity). Applied to measurements of brain activity in a controlled setting as well as under exposure to an external stimulus, they allow for analysis of the impact of the stimulus on those structures. The link between the stimulus and a given source can he confirmed by a classifier that is able to "predict" if a given signal was registered under one or the other condition, solely based on the components. Very often, however, statistical criteria used in traditional decomposition techniques turn out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel hybrid technique based on multi-objective evolutionary algorithms (MOEA) and rough sets (RS) that will perform decomposition in the light of the classification problem itself.
This paper focuses on a typical problem arising in serial production, where two consecutive departments must sequence their internal work, each taking into account the requirements of the other one. Even if the consid...
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This paper focuses on a typical problem arising in serial production, where two consecutive departments must sequence their internal work, each taking into account the requirements of the other one. Even if the considered problem is inherently multi-objective, to date the only heuristic approaches dealing with this problem use single-objective formulations, and also require specific assumptions on the objective function, leaving the most general case of the problem open for innovative approaches. In this paper, we develop and compare three evolutionaryalgorithms for dealing with such a type of combinatorial problems. Two algorithms are designed to perform directed search by aggregating the objectives of each department in a single fitness, while a third one is designed to search for the Pareto front of non-dominated solutions. We apply the three algorithms to considerably complex case studies derived from industrial production of furniture. Firstly, we validate the effectiveness of the proposed genetic algorithms considering a simple case study for which information about the optimal solution is available. Then, we focus on more complex case studies, for which no a priori indication on the optimal solutions is available, and perform an extensive comparison of the various approaches. All the considered algorithms are able to find satisfactory solutions on large production sequences with nearly 300 jobs in acceptable computation times, but they also exhibit some complementary characteristics that suggest hybrid combinations of the various methods.
Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general t...
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Nodes of wireless sensor networks (WSNs) are typically powered by batteries with a limited capacity. Thus, energy is a primary constraint in the design and deployment of WSNs. Since radio communication is in general the main cause of power consumption, the different techniques proposed in the literature to improve energy efficiency have mainly focused on limiting transmission/reception of data, for instance, by adopting data compression and/or aggregation. The limited resources available in a sensor node demand, however, the development of specifically designed algorithms. To this aim, we propose an approach to perform lossy compression on single node based on a differential pulse code modulation scheme with quantization of the differences between consecutive samples. Since different combinations of the quantization process parameters determine different trade-offs between compression performance and information loss, we exploit a multi-objectiveevolutionary algorithm to generate a set of combinations of these parameters corresponding to different optimal trade-offs. The user can therefore choose the combination with the most suitable trade-off for the specific application. We tested our lossy compression approach on three datasets collected by real WSNs. We show that our approach can achieve significant compression ratios despite negligible reconstruction errors. Further, we discuss how our approach outperforms LTC, a lossy compression algorithm purposely designed to be embedded in sensor nodes, in terms of compression rate and complexity. (C) 2010 Elsevier Inc. All rights reserved.
The JPEG algorithm is one of the most used tools for compressing images. The main factor affecting the performance of the JPEG compression is the quantization process, which exploits the values contained in two tables...
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The JPEG algorithm is one of the most used tools for compressing images. The main factor affecting the performance of the JPEG compression is the quantization process, which exploits the values contained in two tables, called quantization tables. The compression ratio and the quality of the decoded images are determined by these values. Thus, the correct choice of the quantization tables is crucial to the performance of the JPEG algorithm. In this paper, a two-objectiveevolutionary algorithm is applied to generate a family of optimal quantization tables which produce different trade-offs between image compression and quality. Compression is measured in terms of difference in percentage between the sizes of the original and compressed images, whereas quality is computed as mean squared error between the reconstructed and the original images. We discuss the application of the proposed approach to well-known benchmark images and show how the quantization tables determined by our method improve the performance of the JPEG algorithm with respect to the default tables suggested in Annex K of the JPEG standard. (C) 2009 Elsevier B.V. All rights reserved.
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