A hyper-heuristic based framework for dynamic optimization problems

Most of the real world problems have dynamic characteristics, where one or more elements of the underlying model for a given problem including the objective, constraints or even environmental parameters may change over time. Hyper-heuristics are problem-independent meta-heuristic techniques that are automating the process of selecting and generating multiple low-level heuristics to solve static combinatorial optimization problems. In this paper, we present a novel hybrid strategy for applicability of hyper-heuristic techniques on dynamic environments by integrating them with the memory/search algorithm. The memory/search algorithm is an important evolutionary technique that have applied on various dynamic optimization problems. We validate performance of our method by considering both the dynamic generalized assignment problem and the moving peaks benchmark. The former problem is extended from the generalized assignment problem by changing resource consumptions, capacity constraints and costs of jobs over time; and the latter one is a well-known synthetic problem that generates and updates a multidimensional landscape consisting of several peaks. Experimental evaluation performed on various instances of the given two problems validates that our hyper-heuristic integrated framework significantly outperforms the memory/search algorithm.     

A particle swarm optimization based memetic algorithm for dynamic optimization problems

Recently, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are dynamic. This paper investigates a particle swarm optimization (PSO) based memetic algorithm that hybridizes PSO with a local search technique for dynamic optimization problems. Within the framework of the proposed algorithm, a local version of PSO with a ring-shape topology structure is used as the global search operator and a fuzzy cognition local search method is proposed as the local search technique. In addition, a self-organized random immigrants scheme is extended into our proposed algorithm in order to further enhance its exploration capacity for new peaks in the search space. Experimental study over the moving peaks benchmark problem shows that the proposed PSO-based memetic algorithm is robust and adaptable in dynamic environments.     

Computational approaches to a combinatorial optimization problem arising from text classification

We present a combinatorial optimization problem with a particular cost structure: a constrained set of elements must be chosen from a ground set and the ground set is partitioned into subsets corresponding to types of elements. The constraints concern the elements, whereas the solution cost does not depend on the elements but only on their types. The motivation of this study comes from text categorization but we believe that the same combinatorial structure may emerge in many different contexts. We prove that the problem is NP-hard. We give a 0–1 linear programming formulation and we report on computational experiences on very large instances using branch-and-bound algorithms based on two different Lagrangean relaxations and heuristic algorithms based on Threshold Accepting and Simulated Annealing.     

A high-performance nonlinear dynamic scheme for the solution of equilibrium constrained optimization problems

In this paper, a feedback neural network model is proposed to compute the solution of the mathematical programs with equilibrium constraints (MPEC). The MPEC problem is altered into an identical one-level non-smooth optimization problem, then a sequential dynamic scheme that progressively approximates the non-smooth problem is presented. Besides asymptotic stability, it is proven that the limit equilibrium point of the suggested dynamic model is a solution for the original MPEC problem. Numerical simulation of various types of MPEC problems shows the significance of the results. Moreover, the scheme is applied to compute the Stackelberg–Cournot–Nash equilibria.     

A switchable scheme for ECG beat classification based on independent component analysis

A switchable scheme is proposed to discriminate different types of electrocardiogram (ECG) beats based on independent component analysis (ICA). The RR-interval serves as an indicator for the scheme to select between the longer (1.0 s) and the shorter (0.556 s) data samples for the following processing. Six ECG beat types, including 13900 samples extracted from 25 records in the MIT-BIH database, are employed in this study. Three conventional statistical classifiers are employed to testify the discrimination power of this method. The result shows a promising accuracy of over 99%, with equally well recognition rates throughout all types of ECG beats. Only 27 ICA features are needed to attain this high accuracy, which is substantially smaller in quantity than that in the other methods. The results prove the capability of the proposed scheme in characterizing heart diseases based on ECG signals.     

一种新的EDCA优化策略

为了保证网络负载增加后无线局域网的QoS,在EDCA机制的基础上运用了预约竞争窗口维护技术,当网络处于低负载时,性能与原接入机制一样,随着负荷的增加,在稳定条件下可以大大减少碰撞概率,从而既保证高优先级业务的QoS,又能提高整个网络的吞吐量。     

基于流分类的iTrace技术

通过对ICMP反向追踪技术的研究,提出了一种基于流分类的ICMP反向追踪方案。该方案依据目的位的值将接收流分成目的流和正常流,然后根据网络流量情况,分别对它们使用不同的概率执行ICMP反向追踪。相关分析表明,此方法在路由器的设置被篡改的情况下能获得完整的攻击路径;另外,本方法能更快地重构攻击路径。     

动态家乡代理发现安全保护方案设计*

为保证移动IPv6动态家乡代理发现过程的安全,采用加密生成地址方式将身份与公钥绑定,设计了基于身份的门限群签名方法,满足了动态家乡代理发现过程对认证的要求。在此基础上设计了动态家乡代理发现安全通信协议,保证了家乡代理信息的安全传输。通过对认证方法的安全分析和安全通信协议的形式化证明,表明设计的保护方案是安全有效的。     

A hybrid neural optimization scheme based on parallel updates

A synchronous Hopfield-type neural network model containing units with analog input and binary output, which is suitable for parallel implementation, is examined in the context of solving discrete optimization problems. A hybrid parallel update scheme concerning the stochastic input-output behaviour of each unit is presented. This parallel update scheme maintains the solution quality of the Boltzmann Machine optimizer, which is inherently sequential. Experimental results on the Maximum Independent Set problem demonstrate the benefit of using the proposed optimizer in terms of computation time. Excellent speedup has been obtained through parallel implementation on both shared memory and distributed memory architectures.     

An improved dynamic structure-based neural networks determination approaches to simulation optimization problems

Simulation optimization studies the problem of optimizing simulation-based objectives. This field has a strong history in engineering but often suffers from several difficulties including being time-consuming and NP-hardness. Simulation optimization is a new and hot topic in the field of system simulation and operational research. This paper presents a hybrid approach that combines Evolutionary Algorithms with neural networks (NNs) for solving simulation optimization problems. In this hybrid approach, we use NNs to replace the known simulation model for evaluating subsequent iterative solutions. Further, we apply the dynamic structure-based neural networks to learn and replace the known simulation model. The determination of dynamic structure-based neural networks is the kernel of this paper. The final experimental results demonstrated that the proposed approach can find optimal or close-to-optimal solutions and is superior to other recent algorithms in simulation optimization.     

A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization

Optimization in dynamic environment is considered among prominent optimization problems. There are particular challenges for optimization in dynamic environments, so that the designed algorithms must conquer the challenges in order to perform an efficient optimization. In this paper, a novel optimization algorithm in dynamic environments was proposed based on particle swarm optimization approach, in which several mechanisms were employed to face the challenges in this domain. In this algorithm, an improved multi-swarm approach has been used for finding peaks in the problem space and tracking them after an environment change in an appropriate time. Moreover, a novel method based on change in velocity vector and particle positions was proposed to increase the diversity of swarms. For improving the efficiency of the algorithm, a local search based on adaptive exploiter particle around the best found position as well as a novel awakening–sleeping mechanism were utilized. The experiments were conducted on Moving Peak Benchmark which is the most well-known benchmark in this domain and results have been compared with those of the state-of-the art methods. The results show the superiority of the proposed method.     

A secure dynamic ID based remote user authentication scheme for multi-server environment

Since the number of server providing the facilities for the user is usually more than one, the authentication protocols for multi-server environment are required for practical applications. Most of password authentication schemes for multi-server environment are based on static ID, so the adversary can use this information to trace and identify the user's requests. It is unfavorable to be applied to special applications, such as e-commerce. In this paper, we develop a secure dynamic ID based remote user authentication scheme to achieve user's anonymity. The proposed scheme only uses hashing functions to implement a robust authentication scheme for the multi-server environment. It provides a secure method to update password without the help of third trusted party. The proposed scheme does not only satisfy all requirements for multi-server environment but also achieve efficient computation. Besides, our scheme provides complete functionality to suit with the real applications.     

A deep learning scheme for mental workload classification based on restricted Boltzmann machines

The mental workload (MWL) classification is a critical problem for quantitative assessment and analysis of operator functional state in many safety-critical situations with indispensable human–machine cooperation. The MWL can be measured by psychophysiological signals. In this work, we propose a novel restricted Boltzmann machine (RBM) architecture for MWL classification. In relation to this architecture, we examine two main issues: the optimal structure of RBM and selection of the most important EEG channels (electrodes) for MWL classification. The trial-and-error and entropy-based pruning methods are compared for the RBM structure identification. The degree of importance of EEG channels is calculated from the weights in a well-trained network in order to select the most relevant channels for classification task. Extensive comparative results showed that the selected EEG channels lead to accurate MWL classification across subjects.     

A motion location based video watermarking scheme using ICA to extract dynamic frames

In this paper, we propose a novel video watermarking scheme based on motion location. In the proposed scheme, independent component analysis is used to extract a dynamic frame from two successive frames of original video, and the motion is located by using the variance of 8 × 8 block in the extracted dynamic frame. Then according to the located motion, we choose a corresponding region in the former frame of the two successive frames, where watermark is embedded by using the quantization index modulation algorithm. The procedure above is repeated until each frame of the video (excluding the last one) is watermarked. The simulations show that the proposed scheme has a good performance to resist Gaussian noising, MPEG2 compression, frame dropping, frame cropping, etc. This work was originally presented in the Fifth International Symposium on Neural Networks.     

A DE and PSO based hybrid algorithm for dynamic optimization problems

Many real world optimization problems are dynamic in which the fitness landscape is time dependent and the optima change over time. Such problems challenge traditional optimization algorithms. For such problems, optimization algorithms not only have to find the global optimum but also need to closely track its trajectory. In this paper, a new hybrid algorithm integrating a differential evolution (DE) and a particle swarm optimization (PSO) is proposed for dynamic optimization problems. Multi-population strategy is adopted to enhance the diversity and try to keep each subpopulation on a different peak in the fitness landscape. A hybrid operator combining DE and PSO is designed, in which each individual is sequentially carried out DE and PSO operations. An exclusion scheme is proposed that integrates the distance based exclusion scheme with the hill-valley function to track the adjacent peaks. The algorithm is applied to the set of benchmark functions used in CEC 2009 competition for dynamic environment. Experimental results show that it is more effective in terms of overall performance than other comparative algorithms.     

基于HIBE的移动代理安全方案*

移动代理存在的安全问题限制了它的应用。为此,通过引入HIBE技术,解决了主机和代理两方面的安全问题,为移动代理系统提供一种新的安全解决方案。同时,该方案与基于PKI技术的移动代理安全方案相比,简化了证书管理和交叉认证问题。     

A novel chemistry based metaheuristic optimization method for mining of classification rules

When investigated carefully, chemical reactions possess efficient objects, states, process, and events that can be designed as a computational method en bloc. In this study, a novel computational method, which is robust and have less parameters than that of used in the literature, is intended to be developed inspiring from types and occurring of chemical reactions. The proposed method is named as artificial chemical reaction optimization algorithm, ACROA. In this study, one of the first applications of this method has been performed in classification rule discovery field of data mining and efficiency has been demonstrated.     

A particle swarm optimization based simultaneous learning framework for clustering and classification

A particle swarm optimization based simultaneous learning framework for clustering and classification (PSOSLCC) is proposed in this paper. Firstly, an improved particle swarm optimization (PSO) is used to partition the training samples, the number of clusters must be given in advance, an automatic clustering algorithm rather than the trial and error is adopted to find the proper number of clusters, and a set of clustering centers is obtained to form classification mechanism. Secondly, in order to exploit more useful local information and get a better optimizing result, a global factor is introduced to the update strategy update strategy of particle in PSO. PSOSLCC has been extensively compared with fuzzy relational classifier (FRC), vector quantization and learning vector quantization (VQ+LVQ3), and radial basis function neural network (RBFNN), a simultaneous learning framework for clustering and classification (SCC) over several real-life datasets, the experimental results indicate that the proposed algorithm not only greatly reduces the time complexity, but also obtains better classification accuracy for most datasets used in this paper. Moreover, PSOSLCC is applied to a real world application, namely texture image segmentation with a good performance obtained, which shows that the proposed algorithm has a potential of classifying the problems with large scale.     

A survey on approaches for reliability-based optimization

Reliability-based Optimization is a most appropriate and advantageous methodology for structural design. Its main feature is that it allows determining the best design solution (with respect to prescribed criteria) while explicitly considering the unavoidable effects of uncertainty. In general, the application of this methodology is numerically involved, as it implies the simultaneous solution of an optimization problem and also the use of specialized algorithms for quantifying the effects of uncertainties. In view of this fact, several approaches have been developed in the literature for applying this methodology in problems of practical interest. This contribution provides a survey on approaches for performing Reliability-based Optimization, with emphasis on the theoretical foundations and the main assumptions involved. Early approaches as well as the most recently developed methods are covered. In addition, a qualitative comparison is performed in order to provide some general guidelines on the range of applicability on the different approaches discussed in this contribution.     

A measure of competence based on random classification for dynamic ensemble selection

In this paper, a measure of competence based on random classification (MCR) for classifier ensembles is presented. The measure selects dynamically (i.e. for each test example) a subset of classifiers from the ensemble that perform better than a random classifier. Therefore, weak (incompetent) classifiers that would adversely affect the performance of a classification system are eliminated. When all classifiers in the ensemble are evaluated as incompetent, the classification accuracy of the system can be increased by using the random classifier instead. Theoretical justification for using the measure with the majority voting rule is given. Two MCR based systems were developed and their performance was compared against six multiple classifier systems using data sets taken from the UCI Machine Learning Repository and Ludmila Kuncheva Collection. The systems developed had typically the highest classification accuracies regardless of the ensemble type used (homogeneous or heterogeneous).