Tundong Liu

An improved PSO algorithm based on particle exploration for function optimization and the modeling of chaotic systems

A novel method to improve the global performance of particle swarm optimization (PSO) is proposed, which extends the exploring domain of the optimal position in the current generation and the optimal position thus achieved by every particle. In each generation, the best two positions are modified according to their searching radii and directions. If the new solutions improve the old ones, the optimal positions in the updating equations of the conventional PSO algorithm will be replaced by the new solutions. Using this operator, the swarm diverges from local optimization easily. Moreover, the algorithm is easily implemented, and because the basic structure of PSO is not altered, the algorithm can be easily combined with different PSO methods to improve the performance. Some benchmark functions and chaotic systems are evaluated via simulations, showing that the proposed algorithm exceeds existing methods such as BPSO, LDWPSO, DNLPSO to some extent.

Structural optimization of Pt-Pd-Au trimetallic nanoparticles by discrete particle swarm algorithms

Trimetallic nanoparticles have received enormous attention due to their multifunctional catalytic activities. Their surface structures strongly determine their catalytic performances, therefore an investigation on their stable structures is of great importance for understanding the catalytic activity. In this article, we have employed an improved discrete particle swarm optimization algorithm to systematically explore the structural stability and segregation behavior of tetrahexahedral Pt–Pd–Au trimetallic nanoparticles. The exchange probability was introduced to decrease computational cost and to avoid falling into local optima. The simulation results reveal that Pt atoms tend to occupy the interior, while both Pd and Au atoms preferentially segregate to the surface. Furthermore, Au atoms exhibit stronger surface segregation than Pd ones, and the segregative behavior is less pronounced in larger nanoparticles. Besides, the distribution of surface atoms has been further examined by the analyses of coordination number. This study provides a fundamental perspective on structural features and segregation behavior of trimetallic nanoparticles.

Structural optimization of Pt–Pd alloy nanoparticles using an improved discrete particle swarm optimization algorithm

Abstract Pt–Pd alloy nanoparticles, as potential catalyst candidates for new-energy resources such as fuel cells and lithium ion batteries owing to their excellent reactivity and selectivity, have aroused growing attention in the past years. Since structure determines physical and chemical properties of nanoparticles, the development of a reliable method for searching the stable structures of Pt–Pd alloy nanoparticles has become of increasing importance to exploring the origination of their properties. In this article, we have employed the particle swarm optimization algorithm to investigate the stable structures of alloy nanoparticles with fixed shape and atomic proportion. An improved discrete particle swarm optimization algorithm has been proposed and the corresponding scheme has been presented. Subsequently, the swap operator and swap sequence have been applied to reduce the probability of premature convergence to the local optima. Furthermore, the parameters of the exchange probability and the ‘particle’ size have also been considered in this article. Finally, tetrahexahedral Pt–Pd alloy nanoparticles has been used to test the effectiveness of the proposed method. The calculated results verify that the improved particle swarm optimization algorithm has superior convergence and stability compared with the traditional one.

Wavelength detection in FBG sensor networks using least squares support vector regression

A wavelength detection method for a wavelength division multiplexing (WDM) fiber Bragg grating (FBG) sensor network is proposed based on least squares support vector regression (LS-SVR). As a kind of promising machine learning technique, LS-SVR is employed to approximate the inverse function of the reflection spectrum. The LS-SVR detection model is established from the training samples, and then the Bragg wavelength of each FBG can be directly identified by inputting the measured spectrum into the well-trained model. We also discuss the impact of the sample size and the preprocess of the input spectrum on the performance of the training effectiveness. The results demonstrate that our approach is effective in improving the accuracy for sensor networks with a large number of FBGs.

Wavelength detection in spectrally overlapped fbg sensor network using extreme learning machine

This letter presents a novel learning-based method called extreme learning machine (ELM) to solve the Bragg wavelength detection problem in the fiber Bragg grating (FBG) sensor network. Based on building up a regression model, the proposed approach is divided into two phases: 1) offline training phase and 2) online detection phase. Due to the good generalization capability of ELM, the well-trained detection model can directly and accurately determine the Bragg wavelengths of the sensors even when the spectra of FBGs are completely overlapped. The results demonstrate that the proposed method is efficient and stable. It has shown competitive advantages in terms of the detection accuracy, the offline training speed, as well as the real-time detection efficiency.

Design of an FBG Sensor Network Based on Pareto Multi-Objective Optimization

A novel scheme based on Pareto-based multiobjective optimization technology is proposed to design a wavelength-division-multiplexing (WDM) fiber Bragg grating (FBG) sensor network. Considering multiple objectives, which are minimizing the bandwidth of the optical source and minimizing the overlapping spectra, an elitist non-dominated sorting genetic algorithm is applied for obtaining the Pareto front. Simulation results show that our approach is effective in improving the multiplexing capability of the WDM network.

A multi-populations multi-strategies differential evolution algorithm for structural optimization of metal nanoclusters

Abstract Theoretically, the determination of the structure of a cluster is to search the global minimum on its potential energy surface. The global minimization problem is often nondeterministic-polynomial-time (NP) hard and the number of local minima grows exponentially with the cluster size. In this article, a multi-populations multi-strategies differential evolution algorithm has been proposed to search the globally stable structure of Fe and Cr nanoclusters. The algorithm combines a multi-populations differential evolution with an elite pool scheme to keep the diversity of the solutions and avoid prematurely trapping into local optima. Moreover, multi-strategies such as growing method in initialization and three differential strategies in mutation are introduced to improve the convergence speed and lower the computational cost. The accuracy and effectiveness of our algorithm have been verified by comparing the results of Fe clusters with Cambridge Cluster Database. Meanwhile, the performance of our algorithm has been analyzed by comparing the convergence rate and energy evaluations with the classical DE algorithm. The multi-populations, multi-strategies mutation and growing method in initialization in our algorithm have been considered respectively. Furthermore, the structural growth pattern of Cr clusters has been predicted by this algorithm. The results show that the lowest-energy structure of Cr clusters contains many icosahedra, and the number of the icosahedral rings rises with increasing size.

A Combinational Clustering Based Method for cDNA Microarray Image Segmentation

Microarray technology plays an important role in drawing useful biological conclusions by analyzing thousands of gene expressions simultaneously. Especially, image analysis is a key step in microarray analysis and its accuracy strongly depends on segmentation. The pioneering works of clustering based segmentation have shown that k-means clustering algorithm and moving k-means clustering algorithm are two commonly used methods in microarray image processing. However, they usually face unsatisfactory results because the real microarray image contains noise, artifacts and spots that vary in size, shape and contrast. To improve the segmentation accuracy, in this article we present a combination clustering based segmentation approach that may be more reliable and able to segment spots automatically. First, this new method starts with a very simple but effective contrast enhancement operation to improve the image quality. Then, an automatic gridding based on the maximum between-class variance is applied to separate the spots into independent areas. Next, among each spot region, the moving k-means clustering is first conducted to separate the spot from background and then the k-means clustering algorithms are combined for those spots failing to obtain the entire boundary. Finally, a refinement step is used to replace the false segmentation and the inseparable ones of missing spots. In addition, quantitative comparisons between the improved method and the other four segmentation algorithms--edge detection, thresholding, k-means clustering and moving k-means clustering--are carried out on cDNA microarray images from six different data sets. Experiments on six different data sets, 1) Stanford Microarray Database (SMD), 2) Gene Expression Omnibus (GEO), 3) Baylor College of Medicine (BCM), 4) Swiss Institute of Bioinformatics (SIB), 5) Joe DeRisi’s individual tiff files (DeRisi), and 6) University of California, San Francisco (UCSF), indicate that the improved approach is more robust and sensitive to weak spots. More importantly, it can obtain higher segmentation accuracy in the presence of noise, artifacts and weakly expressed spots compared with the other four methods.

WSPT's Competitive Performance for Minimizing the Total Weighted Flow Time: From Single to Parallel Machines

We consider the classical online scheduling problem over single and parallel machines with the objective of minimizing total weighted flow time. We employ an intuitive and systematic analysis method and show that the weighted shortest processing time (WSPT) is an optimal online algorithm with the competitive ratio of for the case of single machine, and it is ()-competitive for the case of parallel machines , where P is the ratio of the longest to the shortest processing time.

Optimal Design of High-Channel-Count Fiber Bragg Grating Filters With Low Index Modulation Using an Improved Differential Evolution Algorithm

An effective optimization method based on a self-adaptive differential evolution (DE) algorithm is proposed to design high-channel-count fiber Bragg grating (FBG) filters. By combining the optimization algorithm with the tailored group delay technology, we have established a mathematical model aiming at minimizing the maximum index modulation of the grating. Equipped with a parameter self-adaptive strategy, the improved DE algorithm shows its powerful global searching ability in finding the optimal group delay parameter for each channel. Design examples demonstrate that the proposed approach yields better results with a remarkable reduction in the maximum index modulation compared with the previous works. Furthermore, we numerically present a 1037-channel 50-GHz spaced FBG filter enabling to cover the whole bands (O + E + S + C + L + U), which indicates the potential application of this method in the dense wavelength-division multiplexing (DWDM) system.