Biblio

We consider a class of robust optimization problems that we call “robust-to-dynamics optimization” (RDO). The input to an RDO problem is twofold: (i) a mathematical program (e.g., an LP, SDP, IP, etc.), and (ii) a dynamical system (e.g., a linear, nonlinear, discrete, or continuous dynamics). The objective is to maximize over the set of initial conditions that forever remain feasible under the dynamics. The focus of this paper is on the case where the optimization problem is a linear program and the dynamics are linear. We establish some structural properties of the feasible set and prove that if the linear system is asymptotically stable, then the RDO problem can be solved in polynomial time. We also outline a semidefinite programming based algorithm for providing upper bounds on robust-to-dynamics linear programs.

In recent years, binary coding techniques are becoming increasingly popular because of their high efficiency in handling large-scale computer vision applications. It has been demonstrated that supervised binary coding techniques that leverage supervised information can significantly enhance the coding quality, and hence greatly benefit visual search tasks. Typically, a modern binary coding method seeks to learn a group of coding functions which compress data samples into binary codes. However, few methods pursued the coding functions such that the precision at the top of a ranking list according to Hamming distances of the generated binary codes is optimized. In this paper, we propose a novel supervised binary coding approach, namely Top Rank Supervised Binary Coding (Top-RSBC), which explicitly focuses on optimizing the precision of top positions in a Hamming-distance ranking list towards preserving the supervision information. The core idea is to train the disciplined coding functions, by which the mistakes at the top of a Hamming-distance ranking list are penalized more than those at the bottom. To solve such coding functions, we relax the original discrete optimization objective with a continuous surrogate, and derive a stochastic gradient descent to optimize the surrogate objective. To further reduce the training time cost, we also design an online learning algorithm to optimize the surrogate objective more efficiently. Empirical studies based upon three benchmark image datasets demonstrate that the proposed binary coding approach achieves superior image search accuracy over the state-of-the-arts.

In this paper, we focus on the principal-agent problems in continuous time when the participants have ambiguity on the output process in the framework of g-expectation. The first best (or, risk-sharing) type is studied. The necessary condition of the optimal contract is derived by means of the optimal control theory. Finally, we present some examples to clarify our results.

The cuttlefish optimization algorithm is a new combinatorial optimization algorithm in the family of metaheuristics, applied in the continuous domain, and which provides mechanisms for local and global research. This paper presents a new adaptation of this algorithm in the discrete case, solving the famous travelling salesman problem, which is one of the discrete combinatorial optimization problems. This new adaptation proposes a reformulation of the equations to generate solutions depending a different algorithm cases. The experimental results of the proposed algorithm on instances of TSPLib library are compared with the other methods, show the efficiency and quality of this adaptation.

As a clean energy, wind power is massively utilized in net recent years, which significantly reduced the pollution emission created from unit. This article referred to the concept of energy-saving and emission reducing; built a multiple objective function with represent of the emission of CO2& SO2, the coal-fired from units and the lowest unit fees of commitment; Proposed a algorithm to improving NSGA-D (Non-dominated Sorting Genetic Algorithm-II) for the dynamic characteristics, consider of some constraint conditions such as the shortest operation and fault time and climbing etc.; Optimized and commitment discrete magnitude and Load distribution continuous quantity with the double-optimization strategy; Introduced the fuzzy satisfaction-maximizing method to reaching a decision for Pareto solution and also nested into each dynamic solution; Through simulation for 10 units of wind power, the result show that this method is an effective way to optimize the Multi-objective unit commitment modeling in wind power integrated system with Mixed-integer variable.

We propose a methodology for architecture exploration for Cyber-Physical Systems (CPS) based on an iterative, optimization-based approach, where a discrete architecture selection engine is placed in a loop with a continuous sizing engine. The discrete optimization routine proposes a candidate architecture to the sizing engine. The sizing routine optimizes over the continuous parameters using simulation to evaluate the physical models and to monitor the requirements. To decrease the number of simulations, we show how balance equations and conservation laws can be leveraged to prune the discrete space, thus achieving significant reduction in the overall runtime. We demonstrate the effectiveness of our methodology on an industrial case study, namely an aircraft environmental control system, showing more than one order of magnitude reduction in optimization time.

Risk-control optimization has great significance for security of power system. Usually the probabilistic uncertainties of parameters are considered in the research of risk optimization of power system. However, the method of probabilistic uncertainty description will be insufficient in the case of lack of sample data. Thus non-probabilistic uncertainties of parameters should be considered, and will impose a significant influence on the results of optimization. To solve this problem, a robust optimization operation method of power system risk-control is presented in this paper, considering the non-probabilistic uncertainty of parameters based on information gap decision theory (IGDT). In the method, loads are modeled as the non-probabilistic uncertainty parameters, and the model of robust optimization operation of risk-control is presented. By solving the model, the maximum fluctuation of the pre-specified target can be obtained, and the strategy of this situation can be obtained at the same time. The proposed model is applied to the IEEE-30 system of risk-control by simulation. The results can provide the valuable information for operating department to risk management.

In practical reflector antenna structures, components of the back-up structure (BUS) are selected form a standard steel library which is normally manufactured. In this case, the design problem of the antenna structure is a discrete optimization problem. In most cases, discrete design is solved by heuristic-based algorithm which will be computing-expensive when the number of deign variable increases. In this paper, a continuous method is used to transfer the discrete optimization problem to a continuous one and gradient-based technique is utilized to solve this problem. The method proposed can achieve a well antenna surface accuracy with all components selected from a standard cross-section list, which is shown by a 9m diameter antenna optimization problem.

Next generation cellular networks will provide users better experiences by densely deploying smaller cells, which results in more complicated interferences environment. In order to coordinate interference, power control for uplink is particularly challenging due to random locations of uplink transmitter and dense deployment. In this paper, we address the uplink fractional power control (FPC) optimization problem from network optimization perspective. The relations between FPC parameters and network KPIs (Key Performance Indicators) are investigated. Rather than considering any single KPI in conventional approaches, multi-KPI optimization problem is formulated and solved. By relaxing the discrete optimization problem to a continuous one, the gradients of multiple KPIs with respect to FPC parameters are derived. The gradient enables efficiently searching for optimized FPC parameters which is particularly desirable for dense deployment of large number of cells. Simulation results show that the proposed scheme greatly outperforms the traditional one, in terms of network mean load, call drop & block ratio, and convergence speed.

Selective encryption designates a technique that aims at scrambling a message content while preserving its syntax. Such an approach allows encryption to be transparent towards middle-box and/or end user devices, and to easily fit within existing pipelines. In this paper, we propose to apply this property to a real-time diffusion scenario - or broadcast - over a RTP session. The main challenge of such problematic is the preservation of the synchronization between encryption and decryption. Our solution is based on the Advanced Encryption Standard in counter mode which has been modified to fit our auto-synchronization requirement. Setting up the proposed synchronization scheme does not induce any latency, and requires no additional bandwidth in the RTP session (no additional information is sent). Moreover, its parallel structure allows to start decryption on any given frame of the video while leaving a lot of room for further optimization purposes.

To reduce human efforts in browsing long surveillance videos, synopsis videos are proposed. Traditional synopsis video generation applying optimization on video tubes is very time consuming and infeasible for real-time online generation. This dilemma significantly reduces the feasibility of synopsis video generation in practical situations. To solve this problem, the synopsis video generation problem is formulated as a maximum a posteriori probability (MAP) estimation problem in this paper, where the positions and appearing frames of video objects are chronologically rearranged in real time without the need to know their complete trajectories. Moreover, a synopsis table is employed with MAP estimation to decide the temporal locations of the incoming foreground objects in the synopsis video without needing an optimization procedure. As a result, the computational complexity of the proposed video synopsis generation method can be significantly reduced. Furthermore, as it does not require prescreening the entire video, this approach can be applied on online streaming videos.

The main focus of this work is the estimation of a complex valued signal assumed to have a sparse representation in an uncountable dictionary of signals. The dictionary elements are parameterized by a real-valued vector and the available observations are corrupted with an additive noise. By applying a linearization technique, the original model is recast as a constrained sparse perturbed model. The problem of the computation of the involved multiple parameters is addressed from a nonconvex optimization viewpoint. A cost function is defined including an arbitrary Lipschitz differentiable data fidelity term accounting for the noise statistics, and an ℓ0-like penalty. A proximal algorithm is then employed to solve the resulting nonconvex and nonsmooth minimization problem. Experimental results illustrate the good practical performance of the proposed approach when applied to 2D spectrum analysis.

This paper proposes a cooperative continuous ant colony optimization (CCACO) algorithm and applies it to address the accuracy-oriented fuzzy systems (FSs) design problems. All of the free parameters in a zero- or first-order Takagi-Sugeno-Kang (TSK) FS are optimized through CCACO. The CCACO algorithm performs optimization through multiple ant colonies, where each ant colony is only responsible for optimizing the free parameters in a single fuzzy rule. The ant colonies cooperate to design a complete FS, with a complete parameter solution vector (encoding a complete FS) that is formed by selecting a subsolution component (encoding a single fuzzy rule) from each colony. Subsolutions in each ant colony are evolved independently using a new continuous ant colony optimization algorithm. In the CCACO, solutions are updated via the techniques of pheromone-based tournament ant path selection, ant wandering operation, and best-ant-attraction refinement. The performance of the CCACO is verified through applications to fuzzy controller and predictor design problems. Comparisons with other population-based optimization algorithms verify the superiority of the CCACO.

Existing methods for multi-objective optimization usually provide only an approximation of a Pareto front, and there is little theoretical guarantee of finding the real Pareto front. This paper is concerned with the possibility of fully determining the true Pareto front for those continuous multi-objective optimization problems for which there are a finite number of local optima in terms of each single objective function and there is an effective method to find all such local optima. To this end, some generalized theoretical conditions are firstly given to guarantee a complete cover of the actual Pareto front for both discrete and continuous problems. Then based on such conditions, an effective search procedure inspired by the rising sea level phenomenon is proposed particularly for continuous problems of the concerned class. Even for general continuous problems to which not all local optima are available, the new method may still work well to approximate the true Pareto front. The good practicability of the proposed method is especially underpinned by multi-optima evolutionary algorithms. The advantages of the proposed method in terms of both solution quality and computational efficiency are illustrated by the simulation results.

In this paper, we consider distributed algorithm based on a continuous-time multi-agent system to solve constrained optimization problem. The global optimization objective function is taken as the sum of agents' individual objective functions under a group of convex inequality function constraints. Because the local objective functions cannot be explicitly known by all the agents, the problem has to be solved in a distributed manner with the cooperation between agents. Here we propose a continuous-time distributed gradient dynamics based on the KKT condition and Lagrangian multiplier methods to solve the optimization problem. We show that all the agents asymptotically converge to the same optimal solution with the help of a constructed Lyapunov function and a LaSalle invariance principle of hybrid systems.

Distributed optimization is an emerging research topic. Agents in the network solve the problem by exchanging information which depicts people's consideration on a optimization problem in real lives. In this paper, we introduce two algorithms in continuous-time to solve distributed optimization problems with equality constraints where the cost function is expressed as a sum of functions and where each function is associated to an agent. We firstly construct a continuous dynamic system by utilizing the Lagrangian function and then show that the algorithm is locally convergent and globally stable under certain conditions. Then, we modify the Lagrangian function and re-construct the dynamic system to prove that the new algorithm will be convergent under more relaxed conditions. At last, we present some simulations to prove our theoretical results.

In this paper, we propose a decomposition based multiobjective evolutionary algorithm that extracts information from an external archive to guide the evolutionary search for continuous optimization problem. The proposed algorithm used a mechanism to identify the promising regions(subproblems) through learning information from the external archive to guide evolutionary search process. In order to demonstrate the performance of the algorithm, we conduct experiments to compare it with other decomposition based approaches. The results validate that our proposed algorithm is very competitive.

This paper presents one-layer projection neural networks based on projection operators for solving constrained variational inequalities and related optimization problems. Sufficient conditions for global convergence of the proposed neural networks are provided based on Lyapunov stability. Compared with the existing neural networks for variational inequalities and optimization, the proposed neural networks have lower model complexities. In addition, some improved criteria for global convergence are given. Compared with our previous work, a design parameter has been added in the projection neural network models, and it results in some improved performance. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural networks.

Sybil attack poses a serious threat to geographic routing. In this attack, a malicious node attempts to broadcast incorrect location information, identity and secret key information. A Sybil node can tamper its neighboring nodes for the purpose of converting them as malicious. As the amount of Sybil nodes increase in the network, the network traffic will seriously affect and the data packets will never reach to their destinations. To address this problem, researchers have proposed several schemes to detect Sybil attacks. However, most of these schemes assume costly setup such as the use of relay nodes or use of expensive devices and expensive encryption methods to verify the location information. In this paper, the authors present a method to detect Sybil attacks using Sequential Hypothesis Testing. The proposed method has been examined using a Greedy Perimeter Stateless Routing (GPSR) protocol with analysis and simulation. The simulation results demonstrate that the proposed method is robust against detecting Sybil attacks.

Sybil attack poses a serious threat to geographic routing. In this attack, a malicious node attempts to broadcast incorrect location information, identity and secret key information. A Sybil node can tamper its neighboring nodes for the purpose of converting them as malicious. As the amount of Sybil nodes increase in the network, the network traffic will seriously affect and the data packets will never reach to their destinations. To address this problem, researchers have proposed several schemes to detect Sybil attacks. However, most of these schemes assume costly setup such as the use of relay nodes or use of expensive devices and expensive encryption methods to verify the location information. In this paper, the authors present a method to detect Sybil attacks using Sequential Hypothesis Testing. The proposed method has been examined using a Greedy Perimeter Stateless Routing (GPSR) protocol with analysis and simulation. The simulation results demonstrate that the proposed method is robust against detecting Sybil attacks.

To allow fine-grained access control of sensitive data, researchers have proposed various types of functional encryption schemes, such as identity-based encryption, searchable encryption and attribute-based encryption. We observe that it is difficult to define some complex access policies in certain application scenarios by using these schemes individually. In this paper, we attempt to address this problem by proposing a functional encryption approach named Key-Policy Attribute-Based Encryption with Attribute Extension (KP-ABE-AE). In this approach, we utilize extended attributes to integrate various encryption schemes that support different access policies under a common top-level KP-ABE scheme, thus expanding the scope of access policies that can be defined. Theoretical analysis and experimental studies are conducted to demonstrate the applicability of the proposed KP-ABE-AE. We also present an optimization for a special application of KP-ABE-AE where IPE schemes are integrated with a KP-ABE scheme. The optimization results in an integrated scheme with better efficiency when compared to the existing encryption schemes that support the same scope of access policies.

Cache coherence protocol bugs can cause multicores to fail. Existing coherence verification approaches incur state explosion at small scales or require considerable human effort. As protocols' complexity and multicores' core counts increase, verification continues to be a challenge. Recently, researchers proposed fractal coherence which achieves scalable verification by enforcing observational equivalence between sub-systems in the coherence protocol. A larger sub-system is verified implicitly if a smaller sub-system has been verified. Unfortunately, fractal protocols suffer from two fundamental limitations: (1) indirect-communication: sub-systems cannot directly communicate and (2) partially-serial-invalidations: cores must be invalidated in a specific, serial order. These limitations disallow common performance optimizations used by conventional directory protocols: reply-forwarding where caches communicate directly and parallel invalidations. Therefore, fractal protocols lack performance scalability while directory protocols lack verification scalability. To enable both performance and verification scalability, we propose Fractal++ which employs a new class of protocol optimizations for verification-constrained architectures: decoupled-replies, contention-hints, and fully-parallel-fractal-invalidations. The first two optimizations allow reply-forwarding-like performance while the third optimization enables parallel invalidations in fractal protocols. Unlike conventional protocols, Fractal++ preserves observational equivalence and hence is scalably verifiable. In 32-core simulations of single- and four-socket systems, Fractal++ performs nearly as well as a directory protocol while providing scalable verifiability whereas the best-performing previous fractal protocol performs 8% on average and up to 26% worse with a single-socket and 12% on average and up to 34% worse with a longer-latency multi-socket system.
 

In Wireless Mesh Networks (WMNs), Network-Wide Broadcasts (NWBs) are a fundamental operation, required by routing and other mechanisms that distribute information to all nodes in the network. However, due to the characteristics of wireless communication, NWBs are generally problematic. Optimizing them thus is a prime target when improving the overall performance and dependability of WMNs. Most existing optimizations neglect the real nature of WMNs and are based on simple graph models, which provide optimistic assumptions of NWB dissemination. On the other hand, models that fully consider the complex propagation characteristics of NWBs quickly become unsolvable due to their complexity. In this paper, we present the Monte Carlo method Probabilistic Breadth-First Search (PBFS) to approximate the reachability of NWB protocols. PBFS simulates individual NWBs on graphs with probabilistic edge weights, which reflect link qualities of individual wireless links in the WMN, and estimates reachability over a configurable number of simulated runs. This approach is not only more efficient than existing ones, but further provides additional information, such as the distribution of path lengths. Furthermore, it is easily extensible to NWB schemes other than flooding. The applicability of PBFS is validated both theoretically and empirically, in the latter by comparing reachability as calculated by PBFS and measured in a real-world WMN. Validation shows that PBFS quickly converges to the theoretically correct value and approximates the behavior of real-life testbeds very well. The feasibility of PBFS to support research on NWB optimizations or higher level protocols that employ NWBs is demonstrated in two use cases.

This paper presents a novel design of content fingerprints based on maximization of the mutual information across the distortion channel. We use the information bottleneck method to optimize the filters and quantizers that generate these fingerprints. A greedy optimization scheme is used to select filters from a dictionary and allocate fingerprint bits. We test the performance of this method for audio fingerprinting and show substantial improvements over existing learning based fingerprints.

An improved harmony search algorithm is presented for solving continuous optimization problems in this paper. In the proposed algorithm, an elimination principle is developed for choosing from the harmony memory, so that the harmonies with better fitness will have more opportunities to be selected in generating new harmonies. Two key control parameters, pitch adjustment rate (PAR) and bandwidth distance (bw), are dynamically adjusted to favor exploration in the early stages and exploitation during the final stages of the search process with the different search spaces of the optimization problems. Numerical results of 12 benchmark problems show that the proposed algorithm performs more effectively than the existing HS variants in finding better solutions.