A comparative study of different local search application strategies in hybrid metaheuristics

This paper presents results of a comparative study with the objective to identify the most effective and efficient way of applying a local search method embedded in a hybrid algorithm. The hybrid metaheuristic employed in this study is called “DE–HS–HJ” because it is comprised of two cooperative metaheusitic algorithms, i.e., differential evolution (DE) and harmony search (HS), and one local search (LS) method, i.e., Hooke and Jeeves (HJ) direct search. Eighteen different ways of using HJ local search were implemented and all of them were evaluated with 19 problems, in terms of six performance indices, covering both accuracy and efficiency. Statistic analyses were conducted accordingly to determine the significance in performance differences. The test results show that overall the best three LS application strategies are applying local search to every generated solution with a specified probability and also to each newly updated solution (NUS + ESP), applying local search to every generated solution with a specified probability (ESP), and applying local search to every generated solution with probability and also to the updated current global best solution (EUGbest + ESP). ESP is found to be the best local search application strategy in terms of success rate. Integrating it with NUS further improve the overall performance. EUGbest + ESP is the most efficient and it is also able to achieve high level of accuracy (the fourth place in terms of success rate with an average above 0.9).     

A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients

In this study, we propose a set of new algorithms to enhance the effectiveness of classification for 5-year survivability of breast cancer patients from a massive data set with imbalanced property. The proposed classifier algorithms are a combination of synthetic minority oversampling technique (SMOTE) and particle swarm optimization (PSO), while integrating some well known classifiers, such as logistic regression, C5 decision tree (C5) model, and 1-nearest neighbor search. To justify the effectiveness for this new set of classifiers, the g-mean and accuracy indices are used as performance indexes; moreover, the proposed classifiers are compared with previous literatures. Experimental results show that the hybrid algorithm of SMOTE + PSO + C5 is the best one for 5-year survivability of breast cancer patient classification among all algorithm combinations. We conclude that, implementing SMOTE in appropriate searching algorithms such as PSO and classifiers such as C5 can significantly improve the effectiveness of classification for massive imbalanced data sets.     

Credit rating by hybrid machine learning techniques

It is very important for financial institutions to develop credit rating systems to help them to decide whether to grant credit to consumers before issuing loans. In literature, statistical and machine learning techniques for credit rating have been extensively studied. Recent studies focusing on hybrid models by combining different machine learning techniques have shown promising results. However, there are various types of combination methods to develop hybrid models. It is unknown that which hybrid machine learning model can perform the best in credit rating. In this paper, four different types of hybrid models are compared by ‘Classification + Classification’, ‘Classification + Clustering’, ‘Clustering + Classification’, and ‘Clustering + Clustering’ techniques, respectively. A real world dataset from a bank in Taiwan is considered for the experiment. The experimental results show that the ‘Classification + Classification’ hybrid model based on the combination of logistic regression and neural networks can provide the highest prediction accuracy and maximize the profit.     

Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform

The information extraction capability of two widely used signal processing tools, Hilbert Transform (HT) and Wavelet Transform (WT), is investigated to develop a multi-class fault diagnosis scheme for induction motor using radial vibration signals. The vibration signals are associated with unique predominant frequency components and instantaneous amplitudes depending on the motor condition. Using good systematic and analytical approach this fault frequencies can be identified. However, some faults either electrical or mechanical in nature are associated with same or similar vibration frequencies leading to erroneous conclusions. Genetic Algorithm (GA) is proposed and used successfully to find the most relevant fault frequencies in radial (vertical) frame vibration signal which can be used to diagnose the induction motor faults very effectively even in the presence of noise. The information obtained by Continuous Wavelet Transform (CWT) was found to be highly redundant compared to HT and thus by selecting the most relevant features using GA, the fault classification accuracy has considerably improved especially for CWT. Almost similar fault frequencies were found using CWT + GA and HT + GA for radial vibration signal.     

A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition

This paper presents a novel adaptive cuckoo search (ACS) algorithm for optimization. The step size is made adaptive from the knowledge of its fitness function value and its current position in the search space. The other important feature of the ACS algorithm is its speed, which is faster than the CS algorithm. Here, an attempt is made to make the cuckoo search (CS) algorithm parameter free, without a Levy step. The proposed algorithm is validated using twenty three standard benchmark test functions. The second part of the paper proposes an efficient face recognition algorithm using ACS, principal component analysis (PCA) and intrinsic discriminant analysis (IDA). The proposed algorithms are named as PCA + IDA and ACS–IDA. Interestingly, PCA + IDA offers us a perturbation free algorithm for dimension reduction while ACS + IDA is used to find the optimal feature vectors for classification of the face images based on the IDA. For the performance analysis, we use three standard face databases—YALE, ORL, and FERET. A comparison of the proposed method with the state-of-the-art methods reveals the effectiveness of our algorithm.     

Piezoresistive TiB2/silicone rubber composites for circuit breakers

Piezoresistive composites with high hardness and conductivity are required for circuit breakers for multi-cycle operation under large current flow. Based on the simulation results for the mechanical behavior of piezoresistive composites, we developed piezoresistive composites with conductive TiB₂ ceramic materials and silicone rubber. TiB₂ up to 70 vol.% was embedded into the polymer matrix without any mechanical deterioration while the electrical resistance was decreased with increasing TiB₂ content. Piezoresistive composites with 70 vol.% TiB₂ particles exhibited a resistance of 1.7 Ω at a pressure of 1.1 MPa. A circuit breaker with the fabricated piezoresistive composites acted as a switch with a response time of around 2 ms.     

Detailed check of the LDA + U and GGA + U corrected method for defect calculations in wurtzite ZnO

Based on a detailed check of the LDA + U and GGA + U corrected methods, we found that the transition energy levels depend almost linearly on the effective U parameter. GGA + U seems to be better than LDA + U, with effective U parameter of about 5.0 eV. However, though the results between LDA and GGA are very different before correction, the corrected transition energy levels spread less than 0.3 eV. These more or less consistent results indicate the necessity and validity of LDA + U and GGA + U correction.     

Search space reduction in QoS routing

To provide real-time service or engineer constrained-based paths, networks require the underlying routing algorithm to be able to find low-cost paths that satisfy given quality-of-service constraints. However, the problem of constrained shortest (least-cost) path routing is known to be NP-hard, and some heuristics have been proposed to find a near-optimal solution. However, these heuristics either impose relationships among the link metrics to reduce the complexity of the problem which may limit the general applicability of the heuristic, or are too costly in terms of execution time to be applicable to large networks. In this paper, we focus on solving the delay-constrained minimum-cost path problem, and present a fast algorithm to find a near-optimal solution. This algorithm, called delay-cost-constrained routing (DCCR), is a variant of the k-shortest-path algorithm. DCCR uses a new adaptive path weight function together with an additional constraint imposed on the path cost, to restrict the search space. Thus, DCCR can return a near-optimal solution in a very short time. Furthermore, we use a variant of the Lagrangian relaxation method proposed by Handler and Zang [Networks 10 (1980) 293] to further reduce the search space by using a tighter bound on path cost. This makes our algorithm more accurate and even faster. We call this improved algorithm search space reduction + DCCR (SSR + DCCR). Through extensive simulations, we confirm that SSR + DCCR performs very well compared to the optimal but very expensive solution.     

A new discrete circuit for readout of resistive sensor arrays

In this paper, we present a method that simplifies the interconnect complexity of N × M resistive sensor arrays from N × M to N + M. In this method, we propose to use two sets of interconnection lines in row–column fashion with all the sensor elements having one of their ends connected to a row line and other end to a column line. This interconnection overloading results in crosstalk among all the elements. This crosstalk causes the spreading of information over the whole array. The proposed circuit in this method takes care of this effect by minimizing the crosstalk. The circuit makes use of the concept of virtual same potential at the inputs of an operational amplifier in negative feedback to obtain a sufficient isolation among various elements. We theoretically present the suitability of the method for small/moderate sized sensor arrays and experimentally verify the predicted behavior by lock-in-amplifier based measurements on a light dependent resistor (LDR) in a 4 × 4 resistor array. Finally, we present a successful implementation of this method on a 16 × 16 imaging array of LDR.     

Fabrication and characterization of a thermal switch

The development of a thermal switch based on arrays of liquid–metal micro-droplets is presented. Prototype thermal switches are assembled from a silicon substrate on which is deposited an array of 1600 30-μm liquid–metal micro-droplets. The liquid–metal micro-droplet array makes and breaks contact with a second bare silicon substrate. A gap between the two silicon substrates is filled with either air at 760 Torr, air at of 0.5 Torr or xenon at 760 Torr. Heat transfer and thermal resistance across the thermal switches are measured for “on” (make contact) and “off” (break contact) conditions using guard-heated calorimetry. The figure of merit for a thermal switch, the ratio of “off” state thermal resistance over “on” state thermal resistance, R_off/R_on, is 129 ± 43 for a xenon-filled thermal switch that opens 100 μm and 60 ± 17 for an 0.5 Torr air-filled thermal switch that opens 25 μm. These thermal resistance ratios are shown to be markedly higher than values of R_off/R_on for a thermal switch based on contact between polished silicon surfaces. Transient temperature measurements for the liquid–metal micro-droplet switches indicate thermal switching times of less than 100 ms. Switch lifetimes are found to exceed one-million cycles.     

Securing high resolution grayscale facial captures using a blockwise coevolutionary GA

In biometric systems, reference facial images captured during enrollment are commonly secured using watermarking, where invisible watermark bits are embedded into these images. Evolutionary Computation (EC) is widely used to optimize embedding parameters in intelligent watermarking (IW) systems. Traditional IW methods represent all blocks of a cover image as candidate embedding solutions of EC algorithms, and suffer from premature convergence when dealing with high resolution grayscale facial images. For instance, the dimensionality of the optimization problem to process a 2048 × 1536 pixel grayscale facial image that embeds 1 bit per 8 × 8 pixel block involves 49k variables represented with 293k binary bits. Such Large-Scale Global Optimization problems cannot be decomposed into smaller independent ones because watermarking metrics are calculated for the entire image. In this paper, a Blockwise Coevolutionary Genetic Algorithm (BCGA) is proposed for high dimensional IW optimization of embedding parameters of high resolution images. BCGA is based on the cooperative coevolution between different candidate solutions at the block level, using a local Block Watermarking Metric (BWM). It is characterized by a novel elitism mechanism that is driven by local blockwise metrics, where the blocks with higher BWM values are selected to form higher global fitness candidate solutions. The crossover and mutation operators of BCGA are performed on block level. Experimental results on PUT face image database indicate a 17% improvement of fitness produced by BCGA compared to classical GA. Due to improved exploration capabilities, BCGA convergence is reached in fewer generations indicating an optimization speedup.     

Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm

Stock index forecasting is a hot issue in the financial arena. As the movements of stock indices are non-linear and subject to many internal and external factors, they pose a great challenge to researchers who try to predict them. In this paper, we select a radial basis function neural network (RBFNN) to train data and forecast the stock indices of the Shanghai Stock Exchange. We introduce the artificial fish swarm algorithm (AFSA) to optimize RBF. To increase forecasting efficiency, a K-means clustering algorithm is optimized by AFSA in the learning process of RBF. To verify the usefulness of our algorithm, we compared the forecasting results of RBF optimized by AFSA, genetic algorithms (GA) and particle swarm optimization (PSO), as well as forecasting results of ARIMA, BP and support vector machine (SVM). Our experiment indicates that RBF optimized by AFSA is an easy-to-use algorithm with considerable accuracy. Of all the combinations we tried in this paper, BIAS6 + MA5 + ASY4 was the optimum group with the least errors.     

Comparisons of metaheuristic algorithms and fitness functions on software test data generation

Cost of testing activities is a major portion of the total cost of a software. In testing, generating test data is very important because the efficiency of testing is highly dependent on the data used in this phase. In search-based software testing, soft computing algorithms explore test data in order to maximize a coverage metric which can be considered as an optimization problem. In this paper, we employed some meta-heuristics (Artificial Bee Colony, Particle Swarm Optimization, Differential Evolution and Firefly Algorithms) and Random Search algorithm to solve this optimization problem. First, the dependency of the algorithms on the values of the control parameters was analyzed and suitable values for the control parameters were recommended. Algorithms were compared based on various fitness functions (path-based, dissimilarity-based and approximation level + branch distance) because the fitness function affects the behaviour of the algorithms in the search space. Results showed that meta-heuristics can be effectively used for hard problems and when the search space is large. Besides, approximation level + branch distance based fitness function is generally a good fitness function that guides the algorithms accurately.     

Efficient multicast schemes for 3-D Networks-on-Chip

3-D Networks-on-Chip (NoCs) have been proposed as a potent solution to address both the interconnection and design complexity problems facing future System-on-Chip (SoC) designs. In this paper, two topology-aware multicast routing algorithms, Multicasting XYZ (MXYZ) and Alternative XYZ (AL + XYZ) algorithms in supporting of 3-D NoC are proposed. In essence, MXYZ is a simple dimension order multicast routing algorithm that targets 3-D NoC systems built upon regular topologies. To support multicast routing in irregular regions, AL + XYZ can be applied, where an alternative output channel is sought to forward/replicate the packets whenever the output channel determined by MXYZ is not available. To evaluate the performance of MXYZ and AL + XYZ, extensive experiments have been conducted by comparing MXYZ and AL + XYZ against a path-based multicast routing algorithm and an irregular region oriented multiple unicast routing algorithm, respectively. The experimental results confirm that the proposed MXYZ and AL + XYZ schemes, respectively, have lower latency and power consumption than the other two routing algorithms, meriting the two proposed algorithms to be more suitable for supporting multicasting in 3-D NoC systems. In addition, the hardware implementation cost of AL + XYZ is shown to be quite modest.     

Constructing a novel mortality prediction model with Bayes theorem and genetic algorithm

Intensive care is one of the most important components of the modern medical system. Healthcare professionals need to utilize intensive care resources effectively. Mortality prediction models help physicians decide which patients require intensive care the most and which do not. The Simplified Acute Physiology System 2nd version (SAPS II) is one of the most popular mortality scoring systems currently available. This study retrospectively collected data on 496 patients admitted to intensive care units from year 2000 to 2001. The average patient age was 59.96 ± 1.83 years old and 23.8% of patients died before discharge. We used these data as training data and constructed an exponential Bayesian mortality prediction model by combining BSM (Bayesian statistical model) and GA (genetic algorithm). The optimal weights and the parameters were determined with GA. Furthermore, we prospectively collected data on 142 patients for testing the new model. The average patient age for this group was 57.80 ± 3.33 years old and 21.8% patients died before discharge. The mortality prediction power of the new model was better than SAPS II (p < 0.001). The new model combining BSM and GA can manage both binary data and continuous data. The mortality rate is predicted to be high if the patient’s Glasgow coma score is less than 5.     

Passive single chip wireless microwave pressure sensor

A novel micromachined passive wireless pressure sensor is presented. The device consists of a tuned circuit operating at 10 GHz fabricated on to a SiO₂ membrane, supported on a silicon wafer. A pressure difference across the membrane causes it to deflect so that an antenna circuit detunes. The circuit is remotely interrogated to read off the sensor data wirelessly. The chip area is 5 mm × 4 mm and the membrane area is 2 mm² with a thickness of 4 μm. Two on-chip passive resonant circuits were investigated: a meandered dipole and a zigzag antenna. Both have a physical length of 4.25 mm. The sensors show a shift in their resonant frequency in response to changing pressure of 10.28–10.27 GHz for the meandered dipole, and 9.61–9.58 GHz for the zigzag antenna. The sensitivities of the meandered dipole and zigzag sensors are 12.5 kHz/mbar and 16 kHz/mbar respectively.     

Circuit design of Clos-based on-chip interconnection networks

Single-hop non-blocking networks have the advantage of providing uniform latency and throughput, which is important for cache-coherent network-on-chip systems. This paper focuses on high performance circuit designs of multi-stage non-blocking networks as alternatives to crossbars. Existing work shows that Benes networks have much lower transistor count and smaller circuit area but longer delay than crossbars. To reduce the timing delay, we propose to design the Clos network built with larger size switches. Using less than half number of stages than the Benes network, the Clos network with 4 × 4 switches can significantly reduce the timing delay. The circuit designs of both Benes and Clos networks in different sizes are conducted considering two types of implementation of the configurable switch: with N-type metal-oxide-semiconductor logic (NMOS) transistors only and full transmission gates (TGs). The layout and simulation results under 45 nm technology show that the TG-based implementation demonstrates much better signal integrity than its counterpart. Clos networks achieve average 60% lower timing delay than Benes networks with even smaller area and power consumption.     

Speech emotion recognition: Features and classification models

To solve the speaker independent emotion recognition problem, a three-level speech emotion recognition model is proposed to classify six speech emotions, including sadness, anger, surprise, fear, happiness and disgust from coarse to fine. For each level, appropriate features are selected from 288 candidates by using Fisher rate which is also regarded as input parameter for Support Vector Machine (SVM). In order to evaluate the proposed system, principal component analysis (PCA) for dimension reduction and artificial neural network (ANN) for classification are adopted to design four comparative experiments, including Fisher + SVM, PCA + SVM, Fisher + ANN, PCA + ANN. The experimental results proved that Fisher is better than PCA for dimension reduction, and SVM is more expansible than ANN for speaker independent speech emotion recognition. The average recognition rates for each level are 86.5%, 68.5% and 50.2% respectively.     

On design and performance evaluation of an integrated SIP and HCoP-B architecture for nested network mobility

This paper proposes a fully-integrated SIP + HCoP-B architecture to provide efficient mobility management of the nested mobile network. It achieves the following merits, which are rare in the literature. First, it reduces network deployment costs by only equipping an integrated SIP mobile server. Second, it supports both SIP-based and non-SIP-based applications. Third, by adopting the analytical model proposed in Mohanty and Akyildiz (2007) [19], mathematical analyses are provided to investigate six performance metrics of SIP + HCoP-B and the other four well-known SIP's over NEMO schemes over the error-prone wireless link. Finally, it is shown that SIP + HCoP-B outperforms these four traditional schemes through intensive simulations.     

Fabrication and characterization of polyaniline-based gas sensor by ultra-thin film technology

Pure polyaniline (PAN) film, polyaniline and acetic acid (AA) mixed film, as well as PAN and polystyrenesulfonic acid (PSSA) composite film with various number of layers were prepared by Langmuir–Blodgett (LB) and self-assembly (SA) techniques. These ultra-thin films were characterized by ultraviolet–visible (UV–VIS) spectroscopy and ellipsometry. It is found that the thickness of PAN-based ultra-thin films increases linearly with the increase of the number of film layers. The gas-sensitivity of these ultra-thin films with various layers to NO₂ was studied. It is found that pure polyaniline films prepared by LB technique had good sensitivity to NO₂, while SA films exhibited faster recovery property. The response time to NO₂ and the relative change of resistance of ultra-thin films increased with the increase of the number of film layers. The response time of three-layer PAN film prepared by LB technique to 20 ppm NO₂ was about 10 s, two-layer SA film was about 8 s. The mechanism of sensitivity to NO₂ of PAN-based ultra-thin films was also discussed.