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multi-objective multidisciplinary Design Optimization Approach for Partially Reusable Launch Vehicle Design

JOURNAL OF SPACECRAFT AND ROCKETS 2020年第2期57卷 373-390页

作者： Brevault, Loic Balesdent, Mathieu Hebbal, Ali Off Natl Etud & Rech Aerosp F-91123 Palaiseau France Univ Paris Saclay DTIS Paris France Univ Paris Saclay Paris France Univ Lille CNRS Ctr Rech Informat Signal & Automat Lille Lille France

Reusability of the first stage of launch vehicles may offer new perspectives to lower the cost of payload injection into orbit if sufficient reliability and efficient refurbishment can be achieved. One possible option that may be explored is to design the vehicle first stage for both reusable and expendable uses, in order to increase the flexibility and adaptability to different target missions. This paper proposes a multilevel multidisciplinary design optimization (MDO) approach to design aerospace vehicles addressing multimission problems. The proposed approach is focused on the design of a family of launchers for different missions sharing commonalities using multi-objective MDO to account for the computational cost associated with the discipline simulations. The multimission problem addressed considers two missions: 1) a reusable configuration for a sun synchronous orbit with a medium payload range and recovery of the first stage using a gliding-back strategy;2) an expendable configuration for a medium payload injected into a geostationary transfer orbit. A dedicated MDO formulation introducing couplings between the missions is proposed in order to efficiently solve such a coupled problem while limiting the number of calls to the exact multidisciplinary analysis thanks to the use of Gaussian processes and multi-objective efficient global optimization.

关键词： Expendable Launch Vehicle MDO Geostationary Transfer Orbit Sun Synchronous Orbit Probability Distribution Functions Solid Propellants Perigee Lift Coefficient Surrogate Model multi objective evolutionary algorithms

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multi-objective Cooperated Path Planning of multiple Unmanned Aerial Vehicles Based on Revisit Time

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2021年第12期18卷 919-932页

作者： Haghighi, Hassan Asadi, Davood Delahaye, Daniel Univ Toulouse Natl Sch Civil Aviat F-31400 Toulouse France Adana Alparslan Turkes Sci & Technol Univ Dept Aerosp Engn TR-01250 Adana Turkey

This paper investigates multi-objective optimization of coordinated patrolling flight of multiple unmanned aerial vehicles in the vicinity of terrain, while respecting their performance parameters. A new efficient modified A-star (A*) algorithm with a novel defined criterion known as individual revisit time cell value is introduced and extended to the whole area of the three-dimensional mountainous environment. As a contribution to solving tradeoffs in the optimization problem, revisit time is conjugated with other contrary costs effective in flight planning through Pareto analysis. By introducing the revisit time and applying a specific setup to mitigate computational complexity, the proposed algorithm efficiently revisits the desired zones, which are more important to be revisited during the patrolling mission. The results of the introduced modified A* algorithm are compared in various scenarios with two different algorithms: a complete and optimal algorithm known as Dijkstra, and an evolutionary algorithm known as the genetic algorithm. Simulation results demonstrate that the proposed algorithm generates faster and more efficient trajectories in complex multi-agent scenarios due to the introduced cell selection method and dynamic-based simplifications applied in this research.

关键词： UAV Path Planning multi objective evolutionary algorithms multi objective Genetic Algorithm Flight Planning Optimization Algorithm algorithms and Applications Flight Altitude Ailerons Markov Decision Process

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Rare-PEARs: A new multi objective evolutionary algorithm to mine rare and non-redundant quantitative association rules

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KNOWLEDGE-BASED SYSTEMS 2015年 89卷 366-384页

作者： Almasi, Mehrdad Abadeh, Mohammad Saniee Tarbiat Modares Univ Fac Elect & Comp Engn Tehran Iran

Since finding quantitative association rules (QARs) is an NP-hard problem, evolutionary methods are suitable solutions for discovery QARs. Nevertheless, most of the previous evolutionary methods to discover association rules only consider frequent dependency among items in datasets. They do not pay specific attention to interestingness and non-redundancy as two critical objectives. In this paper, the proposed algorithm (Rare-PEARs) gives a chance to each rule with different length and appearance (antecedent and consequent parts of rules) to be created. Therefore, various interesting, rare or interesting and rare rules can be found. Some of these rules might be uninteresting (those that contain frequent item sets). However, we try to avoid them by Rare-PEARs. To accomplish this goal, our method decomposes the process of association rule mining into N - 1 sub-problems (N is the number of attributes, and each sub-problem is handled by an independent sub-process during Rare-PEARs execution). Each sub-process starts individually with a different initial population. It then explores the search space of its corresponding sub-problem to find rules with semi-optimal intervals for each of the attributes. This process is done by a new definition of Non-Dominated concept. Rare-PEARs uses this definition to find semi-optimal intervals for attributes during the execution of each sub-process. Finally, Rare-PEARs collects QARs from sub-processes and determines the ultimate Non-Dominated rules based on the interestingness and reliability measures. Rare-PEARs tries to maximize three objectives: interestingness, accuracy and reliability while providing vast coverage on the input dataset. We compared Rare-PEARs with ten algorithms (multi-objective, mono-objective and classical algorithms of association rule mining) over several real-world datasets. The results demonstrate high efficiency of Rare-PEARs. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Random permutation multi objective evolutionary algorithms Quantitative association rules

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Efficient and accurate evolutionary multi-objective optimization paradigms for satellite constellation design

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JOURNAL OF SPACECRAFT AND ROCKETS 2007年第3期44卷 682-691页

作者： Ferringer, Matthew P. Clifton, Ronald S. Thompson, Timothy G. Aerosp Corp Syst Engn Div Chantilly VA 20151 USA

multi-objective evolutionary algorithms have been shown to be effective optimization tools to search the complex tradeoff spaces of satellite constellation design. Often, the metrics that make up the design tradeoff require lengthy function evaluation time, resulting in a decreased utility of serial multi-objective evolutionary algorithms. In this research, the authors implement two parallel processing multi-objective evolutionary algorithm paradigms, the master-slave and island models, on a heterogeneous system of processors and operating systems. The efficiency and effectiveness of each approach is studied in the context of a regional coverage design problem. The island scheme outperforms the master-slave model with respect to efficiency. A study of the search dynamics for each paradigm demonstrates that both reliably meet the goals of multi-objective optimization (progressing toward the Pareto-optimal front while maintaining a diverse set of solutions). A key conclusion of this research is that both paradigms provide excellent approximations of the true Pareto frontier using a single seed, and when combined across multiple trial runs, they find nearly the entire set of Pareto-optimal solutions.

关键词： Satellite Constellations multi objective evolutionary algorithms Operating Systems Pareto Frontier Constellations Message Passing Interface Local Area Networks Genetic Algorithm Right Ascension Parallel Computing Systems

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multi-objective Optimization of Helicopter Airfoils Using Surrogate-Assisted Memetic algorithms

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JOURNAL OF AIRCRAFT 2012年第2期49卷 375-383页

作者： Massaro, Andrea Benini, Ernesto Univ Padua I-35131 Padua Italy

An effective strategy for airfoil numerical optimizations is presented that deals with multi-objective and multipoint problems and is specifically designed for helicopter applications. This technique is tested on several realistic problems of airfoil optimization with the aim of improving their aerodynamic performance by searching for optimal shape. The procedure is based On a multi-objective surrogate-assisted memetic algorithm coupled to a Navier-Stokes solver. First, the peculiar features of the algorithm are described with particular attention to its advantages when compared with more traditional evolutionary or gradient-based algorithms. Finally, the results of the optimizations carried out using different operating conditions are presented;starting from the optimal Pareto fronts, several solutions are selected and compared in terms of shapes and performance.

关键词： multi objective evolutionary algorithms Helicopter Blade Thin Airfoil Navier Stokes Solver Aerodynamic Performance Numerical Optimization Aerodynamic Simulation Neural Networks Reynolds Averaged Navier Stokes Numerical Simulation

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Constraint Handling and multi-objective Methods for the Evolution of Interplanetary Trajectories

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2015年第4期38卷 792-799页

作者： Izzo, Dario Hennes, Daniel Riccardi, Annalisa ESA European Space Res & Technol Ctr NL-2201 AZ Noordwijk Netherlands

A study is conducted to demonstrate constraint handling and multi-objective methods for the evolution of interplanetary trajectories. The study is based on the methods developed during GTOC6 and few advanced evolutionary techniques able to evolve complex Jupiter capture options are proposed. The evolutionary process is set up to be able to deal with constraints, along with multiple objectives and show the use of advanced evolutionary techniques to effectively search the solution space in both cases. The investigations show how these techniques allow the evolution of solutions of use to trajectory designers for the complex trajectory case considered in this study where other approaches fail to find acceptable solutions in a reasonable time.

关键词： Spacecraft Trajectory Optimization Callisto multi objective evolutionary algorithms Cumulative Distribution Function Deep Space Maneuver Sequential Quadratic Programming Genetic Algorithm Trajectory Optimization Annealing Derivative Free Method

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multi-objective Hidden Genes Genetic Algorithm for multigravity-Assist Trajectory Optimization

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2022年第7期45卷 1269-1285页

作者： Ellithy, Ahmed Abdelkhalik, Ossama Englander, Jacob Iowa State Univ Aerospace Engn Dept 1200 Howe Hall Ames IA 50011 USA Iowa State Univ Dept Aerosp Engn 1200 Howe Hall Ames IA 50011 USA Johns Hopkins Appl Phys Lab Space Explorat Sect 11101 Johns Hopkins Rd Laurel MD 20723 USA

In multigravity-assist trajectory optimization, the size of the design space is a variable itself. The objective functions are usually replete with local minima. This paper presents a multi-objective hidden genes genetic algorithm (MOHGGA) for trajectory optimization. The length of the chromosome is selected large enough to enable modeling a given maximum number of swing-bys and maximum number of deep space maneuvers (DSMs). Binary tags are appended to those genes that control the swing-bys and DSMs. These binary tags are used to remove/add swing-bys and DSMs to a trajectory solution, and hence enable optimization among solutions of different sizes (different topologies). The MOHGGA generates Pareto fronts that have solutions of, in general, different number of swing-bys, swing-by planets, launch and arrival dates, and number of DSMs. Two objectives are considered in this paper: the total mission cost and total time of flight. An elitist nondominated sorting genetic algorithm is used. Local optimization is conducted on one objective function, holding the other objective function constant, to further improve the resulting Pareto front. Numerical results of four benchmark test cases for missions to Mars, Jupiter, Saturn, and Mercury are presented. The results demonstrate the capability of MOHGGA in searching for optimal trajectory topologies while optimizing two objectives.

关键词： Hidden Genes Genetic algorithms Trajectory Optimization Neptune Deep Space Maneuver Pericenter multi objective evolutionary algorithms Interplanetary Trajectories Lambert Algorithm Cassini Mission MATLAB

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Parallel evolutionary multi-objective Optimization on Large, Heterogeneous Clusters: An Applications Perspective

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JOURNAL OF AEROSPACE COMPUTING INFORMATION AND COMMUNICATION 2008年第11期5卷 460-478页

作者： Reed, Patrick M. Kollat, Joshua B. Ferringer, Matthew P. Thompson, Timothy G. Penn State Univ Dept Civil Engn University Pk PA 16802 USA Grumman Aerosp Corp Performance Modeling & Anal Dept Chantilly VA 20151 USA

Real-world operational use of parallel multi-objective evolutionary algorithms requires successful searches in constrained wall-clock periods, limited trial-and-error algorithmic analysis, and scalable use of heterogeneous computing hardware. This study provides a cross-disciplinary collaborative effort to assess and adapt parallel multi-objective evolutionary algorithms for operational use in satellite constellation design using large dedicated clusters with heterogeneous processor speeds/architectures. A statistical, metric-based evaluation framework is used to demonstrate how time-continuation, asynchronous evolution, dynamic population sizing, and epsilon dominance archiving can be used to enhance both simple master-slave parallelization strategies and more complex multiple-population schemes. Results for a benchmark constellation design coverage problem show that simple master-slave schemes that exploit time-continuation are often sufficient and potentially superior to complex multiple-population schemes.

关键词： Heterogeneous Cluster Satellite Constellations multi objective evolutionary algorithms Constellations Computing Hardware High Performance Computing Cumulative Distribution Function Sensitivity Analysis Parallel Computing Genetic Algorithm

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Fast multi-objective Aerodynamic Optimization Using Sequential Domain Patching and multifidelity Models

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JOURNAL OF AIRCRAFT 2020年第3期57卷 388-398页

作者： Amrit, Anand Leifsson, Leifur Koziel, Slawomir Iowa State Univ Dept Aerosp Engn 537 Bissel Rd Ames IA 50011 USA Reykjavik Univ Engn Optimizat & Modeling Ctr Sch Sci & Engn Menntavegur 1 IS-102 Reykjavik Iceland

Exploration of design tradeoffs for aerodynamic surfaces requires solving of multi-objective optimization (MOO) problems. The major bottleneck here is the time-consuming evaluations of the computational fluid dynamics (CFD) model used to capture the nonlinear physics involved in designing aerodynamic surfaces. This, in conjunction with a large number of simulations necessary to yield a set of designs representing the best possible tradeoffs between conflicting objectives (referred to as a Pareto front), makes CFD-driven MOO very challenging. This paper presents a computationally efficient methodology aimed at expediting the MOO process for aerodynamic design problems. The extreme points of the Pareto front are obtained quickly using single-objective optimizations. Starting from these extreme points, identification of an initial set of Pareto-optimal designs is carried out using a sequential domain patching algorithm. Refinement of the Pareto front, originally obtained at the level of the low-fidelity CFD model, is carried out using local response surface approximations and adaptive corrections. The proposed algorithm is validated using a few multi-objective analytical problems and an aerodynamic problem involving MOO of two-dimensional transonic airfoil shapes where the figures of interest are the drag and pitching moment coefficients. A multifidelity model is constructed using CFD model and control points parameterizing the shape of the airfoil. The results demonstrate that an entire or a part of the Pareto front can be obtained at a low cost when considering up to eight design variables.

关键词： Aerodynamic Shape Optimization Morphing Airfoil CFD Simulation Surrogate Model Reynolds Averaged Navier Stokes multi objective evolutionary algorithms High Performance Computing multi objective Genetic Algorithm Sequential Quadratic Programming Lift Coefficient

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Parameter identification in synthetic biological circuits using multi-objective optimization

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IFAC-PapersOnLine 2016年第26期49卷 77-82页

作者： Boada, Y. Vignoni, A. Reynoso-Meza, G. Picó, J. Universitat Politècnica de Valencia Camino de Vera S/N Valencia46022 Spain Center for Systems Biology Dresden and Max Planck Institute of Molecular Cell Biology and Genetics Pfotenhauer str. 108 Dresden01307 Germany Pontifícia Universidade Católica do Paraná Imaculada Conceição 1155 CuritibaPR80215-901 Brazil

Synthetic biology exploits the of mathematical modeling of synthetic circuits both to predict the behavior of the designed synthetic devices, and to help on the selection of their biological components. The increasing complexity of the circuits being designed requires performing approximations and model reductions to get handy models. Parameter estimation in these models remains a challenging problem that has usually been addressed by optimizing the weighted combination of different prediction errors to obtain a single solution. The single-objective approach is inadequate to incorporate different kinds of experiments, and to identify parameters for an ensemble of biological circuit models. We present a methodology based on multi-objective optimization to perform parameter estimation that can fully harness to ensembles of local models for biological circuits. The methodology uses a global multi-objective evolutionary algorithm and a multi-criteria decision making strategy to select the most suitable solutions. Our approach finds an approximation to the Pareto optimal set of model parameters that correspond to each experimental scenario. Then, the Pareto set was clustered according to the experimental scenarios. This, in turn, allows to analyze the sensitivity of model parameters for different scenarios. Finally, we show the methodology applicability through the case study of a genetic incoherent feed-forward circuit, under different concentrations of the inducer input signal. © 2016

关键词： Parameter estimation Decision making evolutionary algorithms Identification (control systems) Kinetic parameters multiobjective optimization Pareto principle Timing circuits Biological components multi criteria decision making multi objective evolutionary algorithms Pareto optimal sets Prediction errors Single objective Suitable solutions Synthetic biology

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