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This paper investigates the maximum network throughput for resource-constrained space networks based on the delay and disruption-tolerant networking (DTN) architecture. Specifically, this paper proposes a methodology for calculating the maximum network throughput of multiple transmission tasks under storage and delay constraints over a space network. A mixed-integer linear programming (MILP) is formulated to solve this problem. Simulations results show that the proposed methodology can successfully calculate the optimal throughput of a space network under storage and delay constraints, as well as a clear, monotonic relationship between end-to-end delay and the maximum network throughput under storage constraints. At the same time, the optimization results shine light on the routing and transport protocol design in space communication, which can be used to obtain the optimal network throughput. [ABSTRACT FROM AUTHOR]

This paper presents a path planning approach for rotary unmanned aerial vehicles (R-UAVs) in a known static rough terrain environment. This approach aims to find collision-free and feasible paths with minimum altitude, length and angle variable rate. First, a three-dimensional (3D) modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs. Considering the length, height and tuning angle of a path, the path planning of R-UAVs is described as a tri-objective optimization problem. Then, an improved multi-objective particle swarm optimization algorithm is developed. To render the algorithm more effective in dealing with this problem, a vibration function is introduced into the collided solutions to improve the algorithm efficiency. Meanwhile, the selection of the global best position is taken into account by the reference point method. Finally, the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine. Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization algorithm based on a multi-loop control system with a neural network controller, in which the objective function that is used is the control plant of each sub-control system. To obtain the global optimization solution from a control plant that has many local minimum points, a transformation function is presented. On the one hand, this approach changes a complex objective function into a simple function under the condition of an unchanged globally optimal solution, to find the global optimization solution more easily by using a multi-loop control system. On the other hand, a special neural network (in which the node function can be simply positioned locally) that is composed of multiple transformation functions is used as the controller, which reduces the possibility of falling into local minimum points. At the same time, a filled function is presented as a control law; it can jump out of a local minimum point and move to another local minimum point that has a smaller value of the objective function. Finally, 18 simulation examples are provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Recovering the low-rank structure of data matrix from sparse errors arises in the principal component pursuit (PCP). This paper exploits the higher-order generalization of matrix recovery, named higher-order principal component pursuit (HOPCP), since it is critical in multi-way data analysis. Unlike the convexification (nuclear norm) for matrix rank function, the tensorial nuclear norm is still an open problem. While existing preliminary works on the tensor completion field provide a viable way to indicate the low complexity estimate of tensor, therefore, the paper focuses on the low multi-linear rank tensor and adopt its convex relaxation to formulate the convex optimization model of HOPCP. The paper further propose two algorithms for HOPCP based on alternative minimization scheme: the augmented Lagrangian alternating direction method (ALADM) and its truncated higher-order singular value decomposition (ALADM-THOSVD) version. The former can obtain a high accuracy solution while the latter is more efficient to handle the computationally intractable problems. Experimental results on both synthetic data and real magnetic resonance imaging data show the applicability of our algorithms in high-dimensional tensor data processing. [ABSTRACT FROM AUTHOR]

Structure learning of Bayesian networks is a well-researched but computationally hard task. For learning Bayesian networks, this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization (U-ACO-B) to solve the drawbacks of the ant colony optimization (ACO-B). In this algorithm, firstly, an unconstrained optimization problem is solved to obtain an undirected skeleton, and then the ACO algorithm is used to orientate the edges, thus returning the final structure. In the experimental part of the paper, we compare the performance of the proposed algorithm with ACO-B algorithm. The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm. [ABSTRACT FROM AUTHOR]

The paper proposes a decentralized concurrent transmission strategy in shared channels based on an incomplete information game in Ad Hoc networks. Based on the nodal channel quality, the game can work out a channel gain threshold, which decides the candidates for taking part in the concurrent transmission. The utility formula is made for maximizing the overall throughput based on channel quality variation. For an achievable Bayesian Nash equilibrium (BNE) solution, this paper further price the selfish players in utility functions for attempting to improve the channel gain one-sidedly. Accordingly, this game allows each node to distributed decide whether to transmit concurrently with others depending on the Nash equilibrium (NE). Besides, to make the proposed game practical, this paper next presents an efficient particle swarm optimization (PSO) model to fasten the otherwise very slow convergence procedure due to the large computational complexity. Numerical results show the proposed approach is feasible to increase concurrent transmission opportunities for active nodes and the convergence can be swiftly obtained with a few of iteration times by the proposed PSO algorithm. [ABSTRACT FROM AUTHOR]

Weapon project planning (WPP) plays a critical role in the process of national defense development and establishment of the future national defense force. WPP faces the backgrounds of various uncertainties, intense inter-influence of weapon systems and involves modelling, assessment, and optimization procedures. The contents of this paper are mainly divided into three parts: first, the WPP processes are analyzed, and related elements are formulated to transform the qualitative problem to mathematics form; second, the value evaluation model of WPP solutions is proposed based on two criteria of total capability gap and total capability dispersion; third, two robustness optimization models are constructed based on the absolute robustness criterion and the robustness deviation criterion to support the robustness optimization process under multi-scenario. Finally, a case is studied to examine the feasibility and effectiveness of the proposed models and approaches. [ABSTRACT FROM AUTHOR]

This paper introduces an optimization algorithm, the hummingbirds optimization algorithm (HOA), which is inspired by the foraging process of hummingbirds. The proposed algorithm includes two phases: a self-searching phase and a guide-searching phase. With these two phases, the exploration and exploitation abilities of the algorithm can be balanced. Both the constrained and unconstrained benchmark functions are employed to test the performance of HOA. Ten classic benchmark functions are considered as unconstrained benchmark functions. Meanwhile, two engineering design optimization problems are employed as constrained benchmark functions. The results of these experiments demonstrate HOA is efficient and capable of global optimization. [ABSTRACT FROM AUTHOR]

Most of the maintenance optimization models in condition-based maintenance (CBM) consider the cost-optimal criterion, but few papers have dealt with availability maximization for maintenance applications. A novel optimal Bayesian control approach is presented for maintenance decision making. The system deterioration evolves as a three-state continuous time hidden semi-Markov process. Considering the optimal maintenance policy, the multivariate Bayesian control scheme based on the hidden semi-Markov model (HSMM) is developed, the objective is to maximize the long-run expected average availability per unit time. The proposed approach can optimize the sampling interval and control limit jointly. A case study using Markov chain Monte Carlo (MCMC) simulation is provided and a comparison with the Bayesian control scheme based on hidden Markov model (HMM), the age-based replacement policy, Hotelling's T2, multivariate exponentially weihted moving average (MEWMA) and multivariate cumulative sum (MCUSUM) control charts is given, which illustrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

To improve the inconsistency in the analytic hierarchy process (AHP), a new method based on marginal optimization theory is proposed. During the improving process, this paper regards the reduction of consistency ratio (CR) as benefit and the maximum modification compared to the original pairwise comparison matrix (PCM) as cost, then the improvement of consistency is transformed to a benefit/cost analysis problem. According to the maximal marginal effect principle, the elements of PCM are modified by a fixed increment (or decrement) step by step till the consistency ratio becomes acceptable, which can ensure minimum adjustment to the original PCM so that the decision maker's judgment is preserved as much as possible. The correctness of the proposed method is proved mathematically by theorem. Firstly, the marginal benefit/cost ratio is calculated for each single element of the PCM when it has been modified by a fixed increment (or decrement). Then, modification to the element with the maximum marginal benefit/cost ratio is accepted. Next, the marginal benefit/cost ratio is calculated again upon the revised matrix and followed by choosing the modification to the element with the maximum marginal benefit/cost ratio. The process of calculating marginal effect and choosing the best modified element is repeated for each revised matrix till acceptable consistency is reached, i.e, CR<0.1. Finally, illustrative examples show the proposed method is more effective and better in preserving the original comparison information than existing methods. [ABSTRACT FROM AUTHOR]

This paper presents a combined strategy to solve the trajectory online optimization problem for unmanned combat aerial vehicle (UCAV). Firstly, as trajectory directly optimizing is quite time costing, an online trajectory functional representation method is proposed. Considering the practical requirement of online trajectory, the 4-order polynomial function is used to represent the trajectory, and which can be determined by two independent parameters with the trajectory terminal conditions; thus, the trajectory online optimization problem is converted into the optimization of the two parameters, which largely lowers the complexity of the optimization problem. Furthermore, the scopes of the two parameters have been assessed into small ranges using the golden section ratio method. Secondly, a multi-population rotation strategy differential evolution approach (MPRDE) is designed to optimize the two parameters; in which, "current-to-best/1/bin", "current-torand/ 1/bin" and "rand/2/bin" strategies with fixed parameter settings are designed, these strategies are rotationally used by three subpopulations. Thirdly, the rolling optimization method is applied to model the online trajectory optimization process. Finally, simulation results demonstrate the efficiency and real-time calculation capability of the designed combined strategy for UCAV trajectory online optimizing under dynamic and complicated environments. [ABSTRACT FROM AUTHOR]

In the past few decades, applications of geostationary orbit (GEO) satellites have attracted increasing attention, and with the development of optical technologies, GEO optical satellites have become popular worldwide. This paper proposes a general working pattern for a GEO optical satellite, as well as a target observation mission planning model. After analyzing the requirements of users and satellite control agencies, two objectives are simultaneously considered: maximization of total profit and minimization of satellite attitude maneuver angle. An NSGA-II based multi-objective optimization algorithm is proposed, which contains some heuristic principles in the initialization phase and mutation operator, and is embedded with a traveling salesman problem (TSP) optimization. The validity and performance of the proposed method are verified by extensive numerical simulations that include several types of point target distributions. [ABSTRACT FROM AUTHOR]

How to efficiently measure the distance between two basic probability assignments(BPAs) is an open issue. In this paper, a new method to measure the distance between two BPA sis proposed, based on two existing measures of evidence distance. The new proposed method is comprehensive and generalized. Numerical examples are used to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Many multi-objective evolutionary algorithms (MOEAs) can converge to the Pareto optimal front and work well on two or three objectives, but they deteriorate when faced with many-objective problems. Indicator-based MOEAs, which adopt various indicators to evaluate the fitness values (instead of the Pareto-dominance relation to select candidate solutions), have been regarded as promising schemes that yield more satisfactory results than well-known algorithms, such as non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm (SPEA2). However, they can suffer from having a slow convergence speed. This paper proposes a new indicator-based multi-objective optimization algorithm, namely, the multi-objective shuffled frog leaping algorithm based on the ε indicator (ε-MOSFLA). This algorithm adopts a memetic meta-heuristic, namely, the SFLA, which is characterized by the powerful capability of global search and quick convergence as an evolutionary strategy and a simple and effective ε-indicator as a fitness assignment scheme to conduct the search procedure. Experimental results, in comparison with other representative indicator-based MOEAs and traditional Pareto-based MOEAs on several standard test problems with up to 50 objectives, show that ε-MOSFLA is the best algorithm for solving many-objective optimization problems in terms of the solution quality as well as the speed of convergence. [ABSTRACT FROM AUTHOR]

Optimization of architecture design has recently drawn research interest. System deployment optimization (SDO) refers to the process of optimizing systems that are being deployed to activities. This paper first formulates a mathematical model to theorize and operationalize the SDO problem and then identifies optimal solutions to solve the SDO problem. In the solutions, the success rate of the combat task is maximized, whereas the execution time of the task and the cost of changes in the system structure are minimized. The presented optimized algorithm generates an optimal solution without the need to check the entire search space. A novel method is finally proposed based on the combination of heuristic method and genetic algorithm (HGA), as well as the combination of heuristic method and particle swarm optimization (HPSO). Experiment results show that the HPSO method generates solutions faster than particle swarm optimization (PSO) and genetic algorithm (GA) in terms of execution time and performs more efficiently than the heuristic method in terms of determining the best solution. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of optimal fault detection filter (FDF) design for a class of discrete-time switched linear systems under arbitrary switching. By using an observer-based FDF as a residual generator, the design of the FDF is formulated into an optimization problem through maximizing the H_/H∞ or H∞/H∞ performance index. With the aid of an operator optimization method, it is shown that a mode-dependent unified optimal solution can be derived by solving a coupled Riccati equation. A numerical example is given to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is concerned with controller design of networked control systems (NCSs) with both network-induced delay and arbitrary packet dropout. By using a packet-loss-dependent Lyapunov function, sufficient conditions for state/output feedback stabilization and corresponding control laws are derived via a switched system approach. Different from the existing results, the proposed stabilizing controllers design is dependent on the packet loss occurring in the last two transmission intervals due to the network-induced delay. The cone complementary linearation (CCL) methodology is used to solve the non-convex feasibility problem by formulating it into an optimization problem subject to linear matrix inequality (LMI) constraints. Numerical examples and simulations are worked out to demonstrate the effectiveness and validity of the proposed techniques. [ABSTRACT FROM AUTHOR]