Improved NSGA-II Multi-objective Genetic Algorithm Based on Hybridization-encouraged Mechanism

To improve performances of multi-objective optimization algorithms, such as convergence and diversity, a hybridization- encouraged mechanism is proposed and realized in elitist nondominated sorting genetic algorithm （NSGA-Ⅱ）. This mechanism uses the normalized distance to evaluate the difference among genes in a population. Three possible modes of crossover operators--＂Max Distance＂, ＂Min-Max Distance＂, and ＂Neighboring-Max＂--are suggested and analyzed. The mode of ＂Neighboring-Max＂, which not only takes advantage of hybridization but also improves the distribution of the population near Pareto optimal front, is chosen and used in NSGA-Ⅱ on the basis of hybridization-encouraged mechanism （short for HEM-based NSGA-Ⅱ）. To prove the HEM-based algorithm, several problems are studied by using standard NSGA-Ⅱ and the presented method. Different evaluation criteria are also used to judge these algorithms in terms of distribution of solutions, convergence, diversity, and quality of solutions. The numerical results indicate that the application of hybridization-encouraged mechanism could effectively improve the performances of genetic algorithm. Finally, as an example in engineering practices, the presented method is used to design a longitudinal flight control system, which demonstrates the obtainability of a reasonable and correct Pareto front.     

Adaptive double chain quantum genetic algorithm for constrained optimization problems

Optimization problems are often highly constrained and evolutionary algorithms(EAs)are effective methods to tackle this kind of problems. To further improve search efficiency and convergence rate of EAs, this paper presents an adaptive double chain quantum genetic algorithm(ADCQGA) for solving constrained optimization problems. ADCQGA makes use of doubleindividuals to represent solutions that are classified as feasible and infeasible solutions. Fitness(or evaluation) functions are defined for both types of solutions. Based on the fitness function, three types of step evolution(SE) are defined and utilized for judging evolutionary individuals. An adaptive rotation is proposed and used to facilitate updating individuals in different solutions.To further improve the search capability and convergence rate, ADCQGA utilizes an adaptive evolution process(AEP), adaptive mutation and replacement techniques. ADCQGA was first tested on a widely used benchmark function to illustrate the relationship between initial parameter values and the convergence rate/search capability. Then the proposed ADCQGA is successfully applied to solve other twelve benchmark functions and five well-known constrained engineering design problems. Multi-aircraft cooperative target allocation problem is a typical constrained optimization problem and requires efficient methods to tackle. Finally, ADCQGA is successfully applied to solving the target allocation problem.     

Optimization and design of an aircraft's morphing wing-tip demonstrator for drag reduction at low speed,Part Ⅰ–Aerodynamic optimization using genetic,bee colony and gradient descent algorithms

In this paper, an ‘in-house' genetic algorithm is described and applied to an optimization problem for improving the aerodynamic performances of an aircraft wing tip through upper surface morphing. The algorithm's performances were studied from the convergence point of view, in accordance with design conditions. The algorithm was compared to two other optimization methods,namely the artificial bee colony and a gradient method, for two optimization objectives, and the results of the optimizations with each of the three methods were plotted on response surfaces obtained with the Monte Carlo method, to show that they were situated in the global optimum region. The optimization results for 16 wind tunnel test cases and 2 objective functions were presented. The 16 cases used for the optimizations were included in the experimental test plan for the morphing wing-tip demonstrator, and the results obtained using the displacements given by the optimizations were evaluated.     

Research of low boom and low drag supersonic aircraft design

Sonic boom reduction will be an issue of utmost importance in future supersonic transport, due to strong regulations on acoustic nuisance. The paper describes a new multi-objective optimization method for supersonic aircraft design. The method is developed by coupling Seebass–George–Darden(SGD) inverse design method and multi-objective genetic algorithm.Based on the method, different codes are developed. Using a computational architecture, a conceptual supersonic aircraft design environment(CSADE) is constructed. The architecture of CSADE includes inner optimization level and out optimization level. The low boom configuration is generated in inner optimization level by matching the target equivalent area distribution and actual equivalent area distribution. And low boom/low drag configuration is generated in outer optimization level by using NSGA-II multi-objective genetic algorithm to optimize the control parameters of SGD method and aircraft shape. Two objective functions, low sonic boom and low wave drag, are considered in CSADE. Physically reasonable Pareto solutions are obtained from the present optimization. Some supersonic aircraft configurations are selected from Pareto front and the optimization results indicate that the swept forward wing configuration has benefits in both sonic boom reduction and wave drag reduction. The results are validated by using computational fluid dynamics(CFD) analysis.     

Optimization Design System for Composite Structures Based on Grid Technology

To solve the topology optimization of complicated multi-objective continuous/discrete design variables in aircmit structure design, a Parallel Pareto Genetic Algorithm （PPGA） is presented based on grid platform in this paper. In the algorithm, the commercial finite element analysis （FEA） software is integrated as the calculating tool for analyzing the objective functions and the filter of Pareto solution set based on weight information is introduced to deal with the relationships among all objectives. Grid technology is utilized in PPGA to realize the distributed computations and the user interface is developed to realize the job submission and job management locally/remotely. Taking the aero-elastic tailoring of a composite wing for optimization as an example, a set of Pareto solutions are obtained for the decision-maker. The numerical results show that the aileron reversal problem can be solved by adding the limited skin weight in this system. The algorithm can be used to solve complicated topology optimization for composite structures in engineering and the computation efficiency can be improved greatly by using the grid platform that aggregates numerous idle resources.     

A memetic algorithm for path planning of curvature-constrained UAVs performing surveillance of multiple ground targets

The problem of generating optimal paths for curvature-constrained unmanned aerial vehicles （UAVs） performing surveillance of multiple ground targets is addressed in this paper. UAVs are modeled as Dubins vehicles so that the constraints of UAVs＇ minimal turning radius can be taken into account. In view of the effective surveillance range of the sensors equipped on UAVs, the problem is formulated as a Dubins traveling salesman problem with neighborhood （DTSPN）. Considering its prohibitively high computational complexity, the Dubins paths in the sense of terminal heading relaxation are introduced to simplify the calculation of the Dubins distance, and a boundary-based encoding scheme is proposed to determine the visiting point of every target neighborhood. Then, an evolutionary algorithm is used to derive the optimal Dubins tour. To further enhance the quality of the solutions, a local search strategy based on approximate gradient is employed to improve the visiting points of target neighborhoods. Finally, by a minor modification to the individual encoding, the algorithm is easily extended to deal with other two more sophisticated DTSPN variants （multi-UAV scenario and multiple groups of targets scenario）. The performance of the algorithm is demonstrated through comparative experiments with other two state-of-the-art DTSPN algorithms identified in literature. Numerical simulations exhibit that the algorithm proposed in this paper can find high-quality solutions to the DTSPN with lower computational cost and produce significantly improved performance over the other algorithms.     

Feedback structure based entropy approach for multiple-model estimation

The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.     

Double-stage Metamodel and Its Application in Aerodynamic Design Optimization

Constructing metamodel with global high-fidelity in design space is significant in engineering design. In this paper, a double-stage metamodel (DSM) which integrates advantages of both interpolation metamodel and regression metamodel is constructed. It takes regression model as the first stage to fit overall distribution of the original model, and then interpolation model of regression model approximation error is used as the second stage to improve accuracy. Under the same conditions and with the same samples, DSM expresses higher fidelity and represents physical characteristics of original model better. Besides, in order to validate DSM characteristics, three examples including Ackley function, airfoil aerodynamic analysis and wing aerody-namic analysis are investigated. In the end, airfoil and wing aerodynamic design optimizations using genetic algorithm are presented to verify the engineering applicability of DSM.     

Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval

The continuous growth of air traffic has led to acute airspace congestion and severe delays, which threatens operation safety and cause enormous economic loss. Flight assignment is an economical and effective strategic plan to reduce the flight delay and airspace congestion by reasonably regulating the air traffic flow of China. However, it is a large-scale combinatorial optimization problem which is difficult to solve. In order to improve the quality of solutions, an effective multi-objective parallel evolution algorithm(MPEA) framework with dynamic migration interval strategy is presented in this work. Firstly, multiple evolution populations are constructed to solve the problem simultaneously to enhance the optimization capability. Then a new strategy is proposed to dynamically change the migration interval among different evolution populations to improve the efficiency of the cooperation of populations. Finally, the cooperative co-evolution(CC) algorithm combined with non-dominated sorting genetic algorithm II(NSGA-II) is introduced for each population. Empirical studies using the real air traffic data of the Chinese air route network and daily flight plans show that our method outperforms the existing approaches, multiobjective genetic algorithm(MOGA), multi-objective evolutionary algorithm based on decomposition(MOEA/D), CC-based multi-objective algorithm(CCMA) as well as other two MPEAs with different migration interval strategies.     

Topology optimization of support structure of telescope skin based on bit-matrix representation NSGA-II

Non-dominated sorting genetic algorithm II（NSGA-II）with multiple constraints handling is employed for multi-objective optimization of the topological structure of telescope skin,in which a bit-matrix is used as the representation of a chromosome,and genetic algorithm（GA）operators are introduced based on the matrix.Objectives including mass,in-plane performance,and out-of-plane load-bearing ability of the individuals are obtained by fnite element analysis（FEA）using ANSYS,and the matrix-based optimization algorithm is realized in MATLAB by handling multiple constraints such as structural connectivity and in-plane strain requirements.Feasible confgurations of the support structure are achieved.The results confrm that the matrix-based NSGA-II with multiple constraints handling provides an effective method for two-dimensional multi-objective topology optimization.     

Test sequencing problem arising at the design stage for reducing life cycle cost

Previous test sequencing algorithms only consider the execution cost of a test at the application stage. Due to the fact that the placement cost of some tests at the design stage is considerably high compared with the execution cost, the sequential diagnosis strategy obtained by previous methods is actually not optimal from the view of life cycle. In this paper, the test sequencing problem based on life cycle cost is presented. It is formulated as an optimization problem, which is non-deterministic polynomial-time hard (NP-hard). An algorithm and a strategy to improve its computational efficiency are proposed. The formulation and algorithms are tested on various simulated systems and comparisons are made with the extant test sequencing methods. Application on a pump rotational speed control (PRSC) system of a spacecraft is studied in detail. Both the simulation results and the real-world case application results suggest that the solution proposed in this paper can significantly reduce the life cycle cost of a sequential fault diagnosis strategy.     

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.     

A Hybrid Optimization Approach for SRM FINOCYL Grain Design

This article presents a method to design and optimize 3D FINOCYL grain （FCG） configuration for solid rocket motors （SRMs）. The design process of FCG configuration involves mathematical modeling of the geometry and parametric evaluation of various independent geometric variables that define the complex configuration. Virtually infinite combinations of these variables will satisfy the requirements of mass of propellant, thrust, and burning time in addition to satisfying basic needs for volumetric loading fraction and web fraction. In order to ensure the acquisition of the best possible design to be acquired, a sound approach of design and optimization is essentially demanded. To meet this need, a method is introduced to acquire the finest possible performance. A series of computations are carried out to formulate the grain geometry in terms of various combinations of key shapes inclusive of ellipsoid, cone, cylinder, sphere, torus, and inclined plane. A hybrid optimization （HO） technique is established by associating genetic algorithm （GA） for global solution convergence with sequential quadratic programming （SQP） for further local convergence of the solution, thus achieving the final optimal design. A comparison of the optimal design results derived from SQP, GA, and HO algorithms is presented. By using HO technique, the parameter of propellant mass is optimized to the minimum value with the required level of thrust staying within the constrained burning time, nozzle and propellant parameters, and a fixed length and outer diameter of grain.     

Trajectory optimization of multiple quad-rotor UAVs in collaborative assembling task

A hierarchic optimization strategy based on the offline path planning process and online trajectory planning process is presented to solve the trajectory optimization problem of multiple quad-rotor unmanned aerial vehicles in the collaborative assembling task. Firstly, the path planning process is solved by a novel parallel intelligent optimization algorithm, the central force optimization-genetic algorithm (CFO-GA), which combines the central force optimization (CFO) algorithm with the genetic algorithm (GA). Because of the immaturity of the CFO, the convergence analysis of the CFO is completed by the stability theory of the linear time-variant discrete-time sys-tems. The results show that the parallel CFO-GA algorithm converges faster than the parallel CFO and the central force optimization-sequential quadratic programming (CFO-SQP) algorithm. Then, the trajectory planning problem is established based on the path planning results. In order to limit the range of the attitude angle and guarantee the flight stability, the optimized object is changed from the ordinary six-degree-of-freedom rigid-body dynamic model to the dynamic model with an inner-loop attitude controller. The results show that the trajectory planning process can be solved by the mature SQP algorithm easily. Finally, the discussion and analysis of the real-time per-formance of the hierarchic optimization strategy are presented around the group number of the waypoints and the equal interval time.     

Optimization and design of an aircraft's morphing wing-tip demonstrator for drag reduction at low speeds,Part Ⅱ-Experimental validation using Infra-Red transition measurement from Wind Tunnel tests

In the present paper, an ‘in-house' genetic algorithm was numerically and experimentally validated. The genetic algorithm was applied to an optimization problem for improving the aerodynamic performances of an aircraft wing tip through upper surface morphing. The optimization was performed for 16 flight cases expressed in terms of various combinations of speeds, angles of attack and aileron deflections. The displacements resulted from the optimization were used during the wind tunnel tests of the wing tip demonstrator for the actuators control to change the upper surface shape of the wing. The results of the optimization of the flow behavior for the airfoil morphing upper-surface problem were validated with wind tunnel experimental transition results obtained with infra-red Thermography on the wing-tip demonstrator. The validation proved that the 2D numerical optimization using the ‘in-house' genetic algorithm was an appropriate tool in improving various aspects of a wing's aerodynamic performances.     

Airworthiness Compliance Verification Method Based on Simulation of Complex System

A study is conducted on a new airworthiness compliance verification method based on pilot-aircraft-environment complex system simulation. Verification scenarios are established by "block diagram" method based on airworthiness criteria. A pilotaircraft-environment complex model is set up and a virtual flight testing method based on connection of MATLAB/Simulink and Flightgear is proposed. Special researches are conducted on the modeling of pilot manipulation stochastic parameters and manipulation in critical situation. Unfavorable flight factors of certain scenario are analyzed, and reliability modeling of important system is researched. A distribution function of small probability event and the theory on risk probability measurement are studied. Nonlinear function is used to depict the relationship between the cumulative probability and the extremum of the critical parameter. A synthetic evaluation model is set up, modified genetic algorithm (MGA) is applied to ascertaining the distribution parameter in the model, and a more reasonable result is obtained. A clause about vehicle control functions (VCFs) verification in MIL-HDBK-516B is selected as an example to validate the practicability of the method.     

Robust Control for Static Loading of Electro-hydraulic Load Simulator with Friction Compensation

Load simulator is a key test equipment for aircraft actuation systems in hardware-in-the-loop-simulation. Static loading is an essential function of the load simulator and widely used in the static/dynamic stiffness test of aircraft actuation systems. The tracking performance of the static loading is studied in this paper. Firstly, the nonlinear mathematical models of the hydraulic load simulator are derived, and the feedback linearization method is employed to construct a feed-forward controller to improve the force tracking performance. Considering the effect of the friction, a LuGre model based friction compensation is synthesized, in which the unmeasurable state is estimated by a dual state observer via a controlled learning mechanism to guarantee that the estimation is bounded. The modeling errors are attenuated by a well-designed robust controller with a control accuracy measured by a design parameter. Employing the dual state observer is to capture the different effects of the unmeasured state and hence can improve the friction compensation accuracy. The tracking performance is summarized by a derived theorem. Experimental results are also obtained to verify the high performance nature of the proposed control strategy.     

UMR: A utility-maximizing routing algorithm for delay-sensitive service in LEO satellite networks

This paper develops a routing algorithm for delay-sensitive packet transmission in a low earth orbit multi-hop satellite network consists of micro-satellites. The micro-satellite low earth orbit(MS-LEO) network endures unstable link connection and frequent link congestion due to the uneven user distribution and the link capacity variations. The proposed routing algorithm,referred to as the utility maximizing routing(UMR) algorithm, improve the network utility of the MS-LEO network for carrying flows with strict end-to-end delay bound requirement. In UMR, first, a link state parameter is defined to capture the link reliability on continuing to keep the end-to-end delay into constraint; then, on the basis of this parameter, a routing metric is formulated and a routing scheme is designed for balancing the reliability in delay bound guarantee among paths and building a path maximizing the network utility expectation. While the UMR algorithm has many advantages, it may result in a higher blocking rate of new calls. This phenomenon is discussed and a weight factor is introduced into UMR to provide a flexible performance option for network operator. A set of simulations are conducted to verify the good performance of UMR, in terms of balancing the traffic distribution on inter-satellite links, reducing the flow interruption rate,and improving the network utility.     

Coordinated adaptive beamformer over distributed antenna network

The spatial diversity of distributed network demands the individual filter to accommodate the topology of interference environment. In this paper, a type of distributed adaptive beamformer is proposed to mitigate interference over coordinated antenna arrays network. The proposed approach is formulated as generalized sidelobe canceller (GSC) structure to facilitate the convex combination of neighboring nodes’ weights, and then it is solved by unconstrained least mean square (LMS) algorithm due to simplicity. Numerical results show that the robustness and convergence rate of antenna arrays network can be significantly improved in strong interference scenario. And they also clearly illustrate that mixing vector is optimized adaptively and adjusted according to the spatial diversity of the distributed nodes which are placed in different power of received signals to interference ratio (SIR) environments.     

A METHOD OF ESTIMATING THE INFLUENCE OF EXPERIMENTAL ERRORS ON THE EXPERIMENTAL RESULTS OF MATERIAL PARAMETERS AND THE CRITERIA TO VALUE THIS INFLUENCE

In this paper the main sources causing the scatter of the experimental results of the material parameters are discussed. They can be divided into two parts: one is the experimental errors which are introduced because of the inaccuracy of experimental equipment, the experimental techniques, etc., and the form of the scatter caused by this source is called external distribution. The other is due to the irregularity and inhomogeneity of the material structure and the randomness of deformation process. The scatter caused by this source is inherent and then this form of the scatter is called internal distribution. Obviously the experimental distribution of material parameters combines these two distributions in some way; therefore, it is a sum distribution of the external distribution and the internal distribution. In view of this , a general method used to analyse the influence of the experimental errors on the experimental results is presented, and three criteria used to value this influence are defined. A