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Robust multi-fidelity design of a micro re-entry unmanned space vehicle

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2011年第G11期225卷 1195-1209页

作者： Minisci, E. Vasile, M. Liqiang, H. Univ Strathclyde Dept Mech Engn Glasgow G1 1XJ Lanark Scotland Univ Glasgow Sch Engn Glasgow Lanark Scotland Xian Satellite Control Ctr State Key Lab Astronaut Dynam Xian Shaanxi Peoples R China

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 evolutionary multi-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

关键词： multi-objective evolutionary algorithms robust multi-disciplinary design meta-modelling optimal control unmanned space vehicles

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multi-objective Feature Selection in QSAR Using a Machine Learning Approach

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QSAR & COMBINATORIAL SCIENCE 2009年第11-12期28卷 1509-1523页

作者： Soto, Axel J. Cecchini, Rocio L. Vazquez, Gustavo E. Ponzoni, Ignacio Univ Nacl Sur Dept Ciencias & Ingn Computac LIDeCC RA-8000 Bahia Blanca Buenos Aires Argentina Univ Nacl Sur CONICET RA-8000 Bahia Blanca Buenos Aires Argentina

The selection of descriptor subsets for QSAR/QSPR is a hard combinatorial problem that requires the evaluation of complex relationships in order to assess the relevance of the selected subsets. In this paper, we describe the main issues in applying descriptor selection for QSAR methods and propose a novel two-phase methodology for this task. The first phase makes use of a multi-objective evolutionary technique which yields interesting advantages compared to mono-objective methods. The second phase complements the first one and it enables to refine and improve the confidence in the chosen subsets of descriptors. This methodology allows the selection of subsets when a large number of descriptors are involved and it is also Suitable for linear and nonlinear QSAR/QSPR models. The proposed method was tested using three data sets with experimental values for blood-brain barrier penetration, human intestinal absorption and hydrophobicity. Results reveal the capability of the method for achieving subsets of descriptors with a high predictive capacity and a low cardinality. Therefore, our proposal constitutes a new promising technique helpful for the development of QSAR/QSPR models.

关键词： Descriptor selection multi-objective evolutionary algorithms Bayesian regularized neural networks Computational chemistry Medicinal chemistry

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Minimizing total weighted tardiness and flow time for unrelated parallel machines scheduling with matching-improvement evolutionary algorithms

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IFAC Proceedings Volumes 2009年第4期42卷 1043-1048页

作者： Chiuh-Cheng Chyu Wei-Shung Chang Department of Industrial Engineering and Management Yuan-Ze University Taiwan

This paper addresses unrelated parallel machine scheduling problems with two minimization objectives: total weighted flow time and tardiness, and presents two hybrid methods based on (1) non-dominated sorting genetic algorithms (NSGA-II) and (2) strength Pareto evolutionary algorithm (SPEA). These algorithms were implemented in a different manner according to the following two features: (1) using random or fixed weighted sum direction search (RWSD or FWSD); (2) including or not including a bipartite weighted matching problem (BWMP). The performance of the algorithms is evaluated via two benchmark instances generated by a method in the literature. The experimental results indicate that algorithms with RWSD are superior to those with FWSD, and those including BWMP outperforms those not, in terms of proximity and spread metrics. In particular, NSGA-II with RWSD and BWMP performs best for the large size instance, whereas SPEA with RWSD and BWMP excels other algorithms in solving the medium size instance. Nevertheless, algorithms without BWMP spend much less computation time than others under the same termination criterion

关键词： multi-objective evolutionary algorithms unrelated parallel machines scheduling weighted flow time weighted tardiness weighted bipartite matching algorithm

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New hybrid between NSGA-III with multi-objective particle swarm optimization to multi-objective robust optimization design for Powertrain mount system of electric vehicles

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ADVANCES IN MECHANICAL ENGINEERING 2020年第2期12卷

作者： Nguyen Huy Truong Dinh-Nam Dao Inst Mil Mech Engn Hanoi Vietnam Le Quy Don Tech Univ Control Technol Coll Hanoi 100000 Vietnam

In this study, a new methodology, hybrid NSGA-III with multi-objective particle swarm optimization (HNSGA-III&MOPSO), has been developed to design and achieve cost optimization of Powertrain mount system stiffness parameters. This problem is formalized as a multi-objective optimization problem involving six optimization objectives: mean square acceleration and mean square displacement of the Powertrain mount system. A hybrid HNSGA-III&MOPSO is proposed with the integration of multi-objective particle swarm optimization and a genetic algorithm (NSGA-III). Several benchmark functions are tested, and results reveal that the HNSGA-III&MOPSO is more efficient than the typical multi-objective particle swarm optimization, NSGA-III. Powertrain mount system stiffness parameter optimization with HNSGA-III&MOPSO is simulated, respectively. It proved the potential of the HNSGA-III&MOPSO for Powertrain mount system stiffness parameter optimization problem. The amplitude of the acceleration of the vehicle frame decreased by 22.8%, and the amplitude of the displacement of the vehicle frame reduced by 12.4% compared to the normal design case. The calculation time of the algorithm HNSGA-III&MOPSO is less than the algorithm NSGA-III, that is, 5 and 6 h, respectively, compared to the algorithm multi-objective particle swarm optimization.

关键词： NSGA-III algorithm multi-objective particle swarm optimization algorithm optimal solution Powertrain mount system stiffness multi-objective evolutionary algorithms mounting system

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multi-objective evolutionary optimization of unsupervised latent variables of turning process

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APPLIED SOFT COMPUTING 2022年第0期120卷

作者： de Melo, Simone Aparecida Pereira, Robson Bruno Dutra Reis, Allexandre Fortes da Silva Lauro, Carlos Henrique Brandao, Lincoln Cardoso Univ Fed Sao Joao del Rei Sao Joao Del Rei MG Brazil Ctr Innovat Modeling & Optimizat Syst CIMOS Sao Joao Del Rei MG Brazil Ctr Innovat Sustainable Mfg CIMS Sao Joao Del Rei MG Brazil

Manufacturing process modeling and optimization is a challenging task due to the numerous objectives to be considered in the optimization. Generally, the optimization of these processes requires many objective optimization methods to deal with four or more objective functions. However, the correlation structure of the outputs cannot be disregarded. In this work, it is proposed the unsupervised learning of the outputs together with multi-objective evolutionary optimization of the turning process of AISI 4340 steel considering three scenarios varying the tool nose radius. A central composite design varying the process parameters is used to conduct the experimental tests. After tests and measurements of quality and productivity outputs the p correlated observed outputs are firstly transformed in m unobserved latent variables through factor analysis using principal axis extraction method and varimax rotation, with m < p. Subsequently, the relation between the process parameters and the scores of latent variables is modeled through response surface methodology. multi-objective evolutionary optimization methods are applied in the reduced and uncorrelated set of response models of the transformed outputs. The multi-objective algorithms are compared through hypervolume metric and the pseudo-weights approach is used to decision making. The proposed method can also be applied in other multi-response processes with correlated outputs. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Unsupervised learning Factor analysis multi-objective evolutionary algorithms Turning process

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Topology structure optimization of evolutionary hierarchical fuzzy systems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期238卷

作者： Zhao, Tao Zhu, Yue Xie, Xiangpeng Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China Nanjing Univ Posts & Telecommun Inst Adv Technol Nanjing 210003 Peoples R China

In this paper, a new method of hierarchical fuzzy system modeling for high-dimensional regression problems is proposed, which is called multi-objective evolutionary hierarchical fuzzy regression system (MOEHFRS). Different from the existing hierarchical fuzzy systems with fixed topology, the proposed MOEHFRS improves the accuracy of the model, reduces the total number of rules, and eliminates unnecessary features by flexibly constructing topology. In the process of topology evolution, MOEHFRS can exchange and combine different sub-fuzzy systems to achieve the selection and reuse of important features, as well as the elimination of unnecessary features, which improves the diversity of topology and enables the model to be well applied to high-dimensional regression problems. Different combinations of sub-fuzzy systems will result in different performance and number of rules. A new multi-objective evolutionary optimization algorithm is proposed to simultaneously optimize the number of rules and the accuracy of the model, which achieves the balance between complexity and accuracy of MOEHFRS. The proposed method is validated on 13 real world regression datasets and compared with other 5 methods. The results show that MOEHFRS is effective and advanced in terms of accuracy, number of rules, retention of important features and universality in different regression problems.

关键词： Fuzzy rule-based systems Hierarchical fuzzy systems Topology structure optimization multi-objective evolutionary algorithms High-dimensional regression problems

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A Smart-Distributed Pareto Front Using the ev-MO GA evolutionary Algorithm

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2014年第2期23卷 1450002-1450002页

作者： Herrero, J. M. Reynoso-Meza, G. Martinez, M. Blasco, X. Sanchis, J. Univ Politecn Valencia Predict Control & Heurist Optimizat Grp Dept Syst Engn & Control Valencia 46022 Spain

Obtaining multi-objective optimization solutions with a small number of points smartly distributed along the Pareto front is a challenge. Optimization methods, such as the normalized normal constraint (NNC), propose the use of a filter to achieve a smart Pareto front distribution. The NCC optimization method presents several disadvantages related with the procedure itself, initial condition dependency, and computational burden. In this article, the epsilon-variable multi-objective genetic algorithm (ev-MOCA) is presented. This algorithm characterizes the Pareto front in a smart way and removes the disadvantages of the NNC method. Finally, examples of a three-bar truss design and controller tuning optimizations are presented for comparison purposes.

关键词： multi-objective optimization Pareto front engineering design evolutionary algorithms multi-objective evolutionary algorithms

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A COMPOSITIONAL METHOD USING AN evolutionary ALGORITHM FOR FINDING FUZZY RULES IN 3-LAYERED HIERARCHICAL FUZZY STRUCTURE

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2009年第4期8卷 467-467页

作者： Zajaczkowski, J. Verma, B. Queensland Climate Change Ctr Excellence Rockhampton Qld 4702 Australia CQ Univ Sch Comp Sci Rockhampton Qld 4702 Australia

This paper presents a novel compositional method for finding fuzzy rules in a three-layered hierarchical fuzzy structure. The proposed method incorporates a multi-objective evolutionary algorithm and a large set of initial conditions, including dynamical conditions of the system under investigation. The proposed method is focused on handling the large set of initial conditions by a multi-objective evolutionary algorithm and it can be applied to a wide range of dynamical control systems in robotics. The method has been evaluated on a dynamical system such as the inverted pendulum. The experimental results and analysis showed that the proposed method is much better than the existing methods such as amalgamation and single objective evolutionary algorithm based methods.

关键词： multi-objective evolutionary algorithms compositional methods hierarchical fuzzy control inverted pendulum

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Optimization of supply diversity for the self-assembly of simple objects in two and three dimensions

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NATURAL COMPUTING 2011年第1期10卷 551-581页

作者： Vieira, Fabio R. J. Barbosa, Valmir C. Univ Fed Rio de Janeiro Programa Engn Sistemas & Computacao COPPE BR-21941972 Rio De Janeiro Brazil

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.

关键词： Algorithmic self-assembly Minimum tile set problem multi-objective evolutionary algorithms

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On providing quality of service in grid computing through multi-objective swarm-based knowledge acquisition in fuzzy schedulers

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2012年第2期53卷 228-247页

作者： Prado, R. P. Hoffmann, F. Garcia-Galan, S. Munoz Exposito, J. E. Bertram, T. Univ Jaen Telecommun Engn Dept Jaen Spain Univ Dortmund D-44221 Dortmund Germany

Nowadays, grid computing is increasingly showing a service-oriented tendency and as a result, providing quality of service (QoS) has raised as a relevant issue in such highly dynamic and non-dedicated systems. In this sense, the role of scheduling strategies is critical and new proposals able to deal with the inherent uncertainty of the grid state are needed in a way that QoS can be offered. Fuzzy rule-based schedulers are emerging scheduling schemas in grid computing based on the efficient management of grid resources imprecise state and expert knowledge application to achieve an efficient workload distribution. Given the diverse and usually conflicting nature of the scheduling optimization objectives in grids considering both users and administrators requirements, these strategies can benefit from multi-objective strategies in their knowledge acquisition process greatly. This work suggests the QoS provision in the grid scheduling level with fuzzy rule-based schedulers through multi-objective knowledge acquisition considering multiple optimization criteria. With this aim, a novel learning strategy for the evolution of fuzzy rules based on swarm intelligence, Knowledge Acquisition with a Swarm Intelligence Approach (KASIA) is adapted to the multi-objective evolution of an expert grid meta-scheduler founded on Pareto general optimization theory and its performance with respect to a well-known genetic strategy is analyzed. In addition, the fuzzy scheduler with multi-objective learning results are compared to those of classical scheduling strategies in grid computing. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Soft computing Grid scheduling Fuzzy rule-based systems Automatic learning multi-objective evolutionary algorithms

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