The application of local fuzzy models to determine the remaining life of a unit in a fleet of vehicles is described. Instead of developing individual models based on the track history of each unit or developing a glob...
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The application of local fuzzy models to determine the remaining life of a unit in a fleet of vehicles is described. Instead of developing individual models based on the track history of each unit or developing a global model based on the collective track history of the fleet, local fuzzy models are used based on clusters of peers-similar units with comparable utilization and performance characteristics. A local fuzzy performance model is created for each cluster of peers. This is combined with an evolutionary framework to maintain the models. A process has been defined to generate a collection of competing models, evaluate their performance in light of the currently available data, refine the best models using evolutionary search, and select the best one after a finite number of iterations. This process is repeated periodically to automatically update and improve the overall model. To illustrate this methodology an asset selection problem has been identified: given a fleet of industrial vehicles (diesel electric locomotives), select the best subset for mission-critical utilization. To this end, the remaining life of each unit in the fleet is predicted. The fleet is then sorted using this prediction and the highest ranked units are selected. A series of experiments using data from locomotive operations was conducted and the results from an initial validation exercise are presented. The approach of constructing local predictive models using fuzzy similarity with neighboring points along appropriate dimensions is not specific to any asset type and may be applied to any problem where the premise of similarity along chosen attribute dimensions implies similarity in predicted future behavior. (C) 2006 Elsevier B.V. All rights reserved.
In this paper, we solve instances of the multiobjective multiconstraint (or multidimensional) knapsack problem (MOMCKP) from the literature. with three objective functions and three constraints. We use exact as well a...
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In this paper, we solve instances of the multiobjective multiconstraint (or multidimensional) knapsack problem (MOMCKP) from the literature. with three objective functions and three constraints. We use exact as well as approximate algorithms. The exact algorithm is a properly modified version of the multicriteria branch and bound (MCBB) algorithm, which is further customized by suitable heuristics. Three branching heuristics and a more general purpose composite branching and construction heuristic are devised. Comparison is made to the published results from another exact algorithm, the adaptive epsilon-constraint method [Laumanns, M., Thiele, L, Zitzler, E., 2006. An efficient, adaptive parameter variation scheme for Metaheuristics based on the epsilon-constraint method. European journal of operational Research 169, 932-942], using the same data sets. Furthermore, the same problems are solved using standard multiobjective evolutionary algorithms (MOEA), namely, the SPEA2 and the NSGAII. The results from the exact case show that the branching heuristics greatly improve the performance of the MCBB algorithm, which becomes faster than the adaptive epsilon-constraint. Regarding the performance of the MOEA algorithms in the specific problems, SPEA2 outperforms NSGAII in the degree of approximation of the Pareto front, as measured by the coverage metric (especially for the largest instance). (C) 2009 Elsevier B.V. All rights reserved.
In this work, evolutionary algorithms (EAs) are used to achieve optimal feedforward control in a recombinant bacterial fed-batch fermentation process, that aims at producing a bio-pharmaceutical product. Three differe...
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We propose a method to improve the performance of evolutionary algorithms (EA). The proposed approach defines operators which can modify the performance of EA, including Levy distribution function as a strategy parame...
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We propose a method to improve the performance of evolutionary algorithms (EA). The proposed approach defines operators which can modify the performance of EA, including Levy distribution function as a strategy parameters adaptation, calculating mean point for finding proper region of breeding offspring, and shifting strategy parameters to change the sequence of these parameters. Thereafter, a set of benchmark cost functions is utilized to compare the results of the proposed method with some other well-known algorithms. It is shown that the speed and accuracy of EA are increased accordingly. Finally, this method is exploited to optimize fuzzy control of truck backer-upper system.
A surface acoustic wave sensor (the zNose (TM)) was utilized to detect fruit defects by measuring and analyzing the volatile compounds emitted by apples. The zNose generates a spectrum with 512 wavelength values. This...
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A surface acoustic wave sensor (the zNose (TM)) was utilized to detect fruit defects by measuring and analyzing the volatile compounds emitted by apples. The zNose generates a spectrum with 512 wavelength values. This large number of variables not only increases the processing time, but reduces the classification accuracy due to irrelevant information and noise. In this study, three evolutionary techniques, genetic algorithms (GA), covariance matrix adaptation evolutionary strategy (CMAES), and differential evolution (DE) algorithms, were investigated to select the most relevant wavelengths and reduce data dimensionality of a surface acoustic wave sensor for apple defect detection. Three algorithms were compared for their search quality, search efficiency, and data dimensionality reduction. The whole spectrum, which spans 512 wavelength values, was divided into a different number of windows: with 16, 3:2 and 64 wavelength values in each window. These three different discretization schemes were tested by the three techniques. Both CMAES and DE yielded the best prediction accuracy with the 64 windows scenario, and GA produced comparable results with 32 windows and 64 windows, which were better than 16 windows. These results suggested that the finer the spectrum was discretized, the better the classification accuracy obtained. The results also showed that CMAES was the most efficient search algorithm with comparable search quality as DE. Three algorithms were further fine-tuned by adjusting their population size which influenced the search space. The parametric study was conducted only for the 64-window case. It was observed that algorithms with larger population size gave better search results. For CMAES, the average cost (classification error rate) for ten random seed runs was 0.0289 with the best search cost of 0.0263 by using twice the default population size (lambda). Differential evolution (DE) produced slightly better search results but at the cost of reducing s
Somatosensory evoked potentials, recorded at the spine or scalp of a patient, are contaminated by noise. It is common practice to use ensemble averaging to remove the noise, which usually requires a large number of re...
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Somatosensory evoked potentials, recorded at the spine or scalp of a patient, are contaminated by noise. It is common practice to use ensemble averaging to remove the noise, which usually requires a large number of responses to produce one averaged signal. In this paper a post-processing technique is shown which uses a combination of wavelets and evolutionary algorithms to produce a representative waveform with fewer responses. The most suitable wavelets and a set of weights are selected by an evolutionary algorithm to form a filter bank, which enhances the extraction of evoked potentials from noisy recordings. (C) 2003 IPEM. Published by Elsevier Science Ltd. All rights reserved.
evolutionary algorithms (EAs) are useful tools in design optimization. Due to their simplicity, ease of use, and suitability for multi-objective design optimization problems, EAs have been applied to design optimizati...
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evolutionary algorithms (EAs) are useful tools in design optimization. Due to their simplicity, ease of use, and suitability for multi-objective design optimization problems, EAs have been applied to design optimization problems from various areas. In this paper we review the recent progress in design optimization using evolutionary algorithms to solve real-world aerodynamic problems. Examples are given in the design of turbo pump, compressor, and micro-air vehicles. The paper covers the following topics that are deemed important to solve a large optimization problem from a practical viewpoint: (1) hybridized approaches to speed up the convergence rate of EAs;(2) the use of surrogate model to reduce the computational cost stemmed from EAs;(3) reliability based design optimization using EAs;and (4) data mining of Pareto-optimal solutions. Published by Elsevier Ltd.
Blind and visually impaired (BVI) face various problems during their navigation. Being unable to rely on sight greatly restricts their capacity to learn information about their surroundings. Scene recognition is cruci...
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Blind and visually impaired (BVI) face various problems during their navigation. Being unable to rely on sight greatly restricts their capacity to learn information about their surroundings. Scene recognition is crucial in improving life quality for BVI. Scene recognition systems can recognize and characterize the visual world using powerful artificial intelligence algorithms, allowing users to receive a critical overview. Indoor scene recognition systems are crucial for BVI to explore their environment. These systems are essential for increasing their independence, safety, and overall quality of life. Developing technology that allows BVI to perceive their environment and enable them to navigate and interact with the world on their own is extremely important. We propose in this paper a scene recognition system to assist BVI in their daily activities. The proposed work was developed on top of an efficient set of deep learning techniques called "Deep evolutionary algorithms (DEAs)". DEAs are a type of algorithm that solves complicated search problems by combining the principles of evolutionary computing with deep learning. DEAs provide optimization techniques inspired by the process of natural selection. They iteratively develop a population of potential networks to identify optimum or near-optimal networks to solve complicated problems through genetic methods including mutation, crossover, and selection. To ensure the efficiency of the proposed work, extensive experiments have been conducted using two benchmark datasets the MIT 67 dataset and the Scene 15 dataset. New state-of-the-art results have been ensured by the proposed work in terms of recognition accuracy.
This paper introduces the notion of an entropy measurement for populations of candidate solutions in evolutionary algorithms, developing both conditional and joint entropy-based algorithms. We describe the inherent ch...
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This paper introduces the notion of an entropy measurement for populations of candidate solutions in evolutionary algorithms, developing both conditional and joint entropy-based algorithms. We describe the inherent characteristics of the entropy measurement and how these affect the search process. Following these discussions, we develop a recognition mechanism through which promising candidate solutions can be identified without the need of invoking costly evaluation functions. This on-demand evaluation strategy (ODES) is able to perform decision making tasks regardless of whether the actual fitness evaluation is necessary or not, making it an ideal efficiency enhancement technique for accelerating the computational process of evolutionary algorithms. Two different evolutionary algorithms, a traditional genetic algorithm and a multivariate estimation of distribution algorithm, are employed as example targets for the application of our on-demand evaluation strategy. Ultimately, experimental results confirm that our method is able to broadly improve the performance of various population-based global searchers. (C) 2011 Elsevier Inc. All rights reserved.
In order to take full advantage of the enormous design freedom offered by Additive Manufacturing (AM) technologies, the use of Topology Optimization (TO) methods becomes essential. Although TO is well-established in m...
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In order to take full advantage of the enormous design freedom offered by Additive Manufacturing (AM) technologies, the use of Topology Optimization (TO) methods becomes essential. Although TO is well-established in many disciplines, the problems in vehicle crashworthiness pose severe difficulties for standard, gradient-based approaches, due to high noisiness, multi-modality, and discontinuous nature of the nonlinear simulation responses considered typically as objectives and constraints. In this article, we propose to use evolutionary algorithms (EAs) together with a suitable low-dimensional representation in an extended version of the evolutionary Level Set Method (EA-LSM), able to address complex 3D crash TO problems. The method is used to optimize a 3D-printed metal joint in a hybrid S-rail structure under axial crash loading, inspired by novel frame design concepts in industry. The obtained results show that the method is capable of handling optimization problems with multiple constraints, including challenging acceleration responses, and yields significantly better solutions than the state-of-the-art methods. Finally, the robustness of the obtained designs is studied, demonstrating the ability of EA-LSM to find designs of low sensitivity w.r.t. small variations of the loading conditions, which is crucial from the perspective of industrial applications of the proposed method.
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