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This paper brings forward a method to control the battle situation on the base of ammunition-to-target distribution after targets threaten evaluation. The method aims at the real needs of aerial warfare, owns the characteristics of complicacy and real-time. At first, the paper found a threat evaluation model combining the priority of battlefield situation and aerial warfare situation, and then input...
Given a graph G = (V, E) with real-valued edge weights, the problem of correlation k-clustering with pre-clustered items is to extend a k-clustering of distinguished vertices of G (pre-clustered items) to partition all the vertices into clusters so as to minimize the total absolute weight of cut positive edges and uncut negative edges. This problem for general graphs is APX-complete. A polynomial...
In this article, we shall show how the sigma-point based approximations that have previously been used in optimal filtering can also be used in optimal smoothing. In particular, we shall consider unscented transformation, Gauss-Hermite quadrature and central differences based optimal smoothers. We briefly present the smoother equations and compare performance of different methods in simulated scenarios.
This paper proposes a new B2B electronic commerce model by using bidding information in double auctions. In B2B electronic commerce, buyers try to purchase in multiple items at the same time, since a buyer develops something products by using purchased items. Also suppliers have an incentive of making coalitions, since buyers want to purchase in multiple items. A mechanism designer has to consider...
We propose a new approximation method for Gaussian process (GP) learning for large data sets that combines inline active set selection with hyperparameter optimization. The predictive probability of the label is used for ranking the data points. We use the leave-one-out predictive probability available in GPs to make a common ranking for both active and inactive points, allowing points to be removed...
Discretization of continuous-valued attributes is always one of the key problems in rough sets theory, a multiscale rough set model (MRSM) is developed that describes the discretization at multiple scales and analyzes the relation of classifications and certainty between scales. In view of the model's efficiency and effectiveness. an optimal scale can be acquired with self-organization, self-study...
We consider the problem of optimizing the performance of a network coding router with two stochastic flows. We develop a queueing model which accounts for the fact that coding is not performed when packets are transmitted, but is done by a separate program or hardware which operates independently of the hardware that sends packets out over links. We formulate and solve a constrained optimization problem...
Processing streams of data in an overlay network of operators distributed over a wide-area network is a common idea shared by different applications such as distributed event correlation systems and large-scale sensor networks. In order to utilize network resources efficiently and allow for the parallel deployment of a large number of large-scale operator networks, suitable placement algorithms are...
The classic theory of Rough sets is based on incomplete information systems. In practicing, decision tables are, however, usually incomplete due to the causes of data outputting or processing. That is to say, there are often default values. In order to deal with incomplete systems, Kryszkiewicz put a Rough sets model on the basis of error tolerance relations. According to this model, constructing...
This paper discusses the analytical and computational challenges of various sub-gradient and cutting plane methods applied to updating Lagrangian multipliers associated with the Security-Constrained Unit Commitment problem. Large-scale testing systems are used to demonstrate the effectiveness of the different algorithms.
Quantization plays an important part in lossy vector map compression, for which the existing solutions are based on either a fixed size open-loop codebook, or a simple uniform quantization. In this paper, we proposed an entropy-constrained vector quantization to optimize both the structure and size of the codebook at the same time using a closed-loop approach. In order to lower the distortion to a...
A novel stochastic searching scheme based on the Monte Carlo optimization is presented for polygonal approximation (PA) problem. We propose to combine the split-and-merge based local optimization and the Monte Carlo sampling, to give an efficient stochastic optimization scheme. Our approach, in essence, is a well-designed Basin-Hopping scheme, which performs stochastic hopping among the reduced energy...
The design and application of termination criteria has become an important aspect in evolutionary multi-objective optimization. Online convergence detection (OCD) determines when further generations are no longer promising based on statistical tests on a set of performance indicators. The behavior of OCD mainly depends on two parameters, the number of preceding generations considered in the statistical...
This paper presents a study for using Kriging metamodeling in combination with Covariance Matrix Adaptation Evolution Strategies (CMA-ES) to find robust solutions. A general, archive based, framework is proposed for integrating Kriging within CMA-ES, including a method to utilize the covariance matrix of the CMA-ES in a straightforward way to improve the accuracy of the Kriging predictions without...
This paper develops a novel fuzzy reinforcement learning (RL) based controller for multiagent partially observable Markov decision processes (POMDPs) modeled as a sequence of Bayesian games. Multiagent POMDPs have emerged as a powerful framework for modeling and optimizing multiagent sequential decision making problems under uncertainty, but finding optimal policies is computationally very challenging...
In this paper, a multi-objective heuristic-based method of design of a fuzzy controller for an inverted pendulum-cart system is investigated. A heuristic search optimization called “Imperialist Competitive Algorithm” is proposed to be used in generating and optimizing the fuzzy rules engine. The main purpose of this paper is to design a model-free nonlinear controller for inverted pendulum-cart system...
MOEA/D is a generic multiobjective evolutionary optimization algorithm. MOEA/D needs a approach to decompose a multiobjective optimization problem into a number of single objective optimization problems. The commonly-used weighted sum approach and the Tchebycheff approach may not be able to handle disparately scaled objectives. This paper suggests a new decomposition approach, called NBI-style Tchebycheff...
We present basic ideas related to application of Dominance-based Rough Set Approach (DRSA) in interactive Evolutionary Multiobjective Optimization (EMO). In the proposed methodology, the preference information elicited by the decision maker in successive iterations consists in sorting some solutions in the current population into “relatively good” and “others”, or in comparing some pairs of solutions...
In many applications it can be advantageous for the decision maker to have multiple options available for a possible realization of the project. One way to increase the number of interesting choices is in certain cases to consider in addition to the optimal solution x* also nearly optimal or approximate solutions which differ in the design space from x* by a certain value. In this paper we address...
jMetal is a Java-based framework for multi-objective optimization using metaheuristics. It is a flexible, extensible, and easy-to-use software package that has been used in a wide range of applications. In this paper, we describe the design issues underlying jMetal, focusing mainly on its internal architecture, with the aim of offering a comprehensive view of its main features to interested researchers...
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