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Algorithms, Volume 8, Issue 2 (June 2015) , Pages 82-365

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Open AccessArticle
MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm
Algorithms 2015, 8(2), 336-365; https://doi.org/10.3390/a8020336 - 19 Jun 2015
Cited by 8 | Viewed by 3281
Abstract
The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired [...] Read more.
The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired swarm intelligence optimization algorithms, namely, the Monkey Algorithm (MA) and the Krill Herd Algorithm (KHA). The hybridization made use of the efficient steps in each of the two original algorithms and provided a better balance between the exploration/diversification steps and the exploitation/intensification steps. The new hybrid algorithm, MAKHA, was rigorously tested with 27 benchmark problems and its results were compared with the results of the two original algorithms. MAKHA proved to be considerably more reliable and more efficient in tested problems. Full article
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Open AccessArticle
Time Domain Simulation of Sound Waves Using Smoothed Particle Hydrodynamics Algorithm with Artificial Viscosity
Algorithms 2015, 8(2), 321-335; https://doi.org/10.3390/a8020321 - 17 Jun 2015
Cited by 2 | Viewed by 3051
Abstract
Smoothed particle hydrodynamics (SPH), as a Lagrangian, meshfree method, is supposed to be useful in solving acoustic problems, such as combustion noise, bubble acoustics, etc., and has been gradually used in sound wave computation. However, unphysical oscillations in the sound wave simulation [...] Read more.
Smoothed particle hydrodynamics (SPH), as a Lagrangian, meshfree method, is supposed to be useful in solving acoustic problems, such as combustion noise, bubble acoustics, etc., and has been gradually used in sound wave computation. However, unphysical oscillations in the sound wave simulation cannot be ignored. In this paper, an artificial viscosity term is added into the standard SPH algorithm used for solving linearized acoustic wave equations. SPH algorithms with or without artificial viscosity are both built to compute sound propagation and interference in the time domain. Then, the effects of the smoothing kernel function, particle spacing and Courant number on the SPH algorithms of sound waves are discussed. After comparing SPH simulation results with theoretical solutions, it is shown that the result of the SPH algorithm with the artificial viscosity term added attains good agreement with the theoretical solution by effectively reducing unphysical oscillations. In addition, suitable computational parameters of SPH algorithms are proposed through analyzing the sound pressure errors for simulating sound waves. Full article
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Open AccessArticle
An Optimal Eighth-Order Derivative-Free Family of Potra-Pták’s Method
Algorithms 2015, 8(2), 309-320; https://doi.org/10.3390/a8020309 - 15 Jun 2015
Cited by 6 | Viewed by 2050
Abstract
In this paper, we present a new three-step derivative-free family based on Potra-Pták’s method for solving nonlinear equations numerically. In terms of computational cost, each member of the proposed family requires only four functional evaluations per full iteration to achieve optimal eighth-order convergence. [...] Read more.
In this paper, we present a new three-step derivative-free family based on Potra-Pták’s method for solving nonlinear equations numerically. In terms of computational cost, each member of the proposed family requires only four functional evaluations per full iteration to achieve optimal eighth-order convergence. Further, computational results demonstrate that the proposed methods are highly efficient as compared with many well-known methods. Full article
(This article belongs to the Special Issue Numerical Algorithms for Solving Nonlinear Equations and Systems)
Open AccessArticle
Training Artificial Neural Networks by a Hybrid PSO-CS Algorithm
Algorithms 2015, 8(2), 292-308; https://doi.org/10.3390/a8020292 - 11 Jun 2015
Cited by 18 | Viewed by 3455
Abstract
Presenting a satisfactory and efficient training algorithm for artificial neural networks (ANN) has been a challenging task in the supervised learning area. Particle swarm optimization (PSO) is one of the most widely used algorithms due to its simplicity of implementation and fast convergence [...] Read more.
Presenting a satisfactory and efficient training algorithm for artificial neural networks (ANN) has been a challenging task in the supervised learning area. Particle swarm optimization (PSO) is one of the most widely used algorithms due to its simplicity of implementation and fast convergence speed. On the other hand, Cuckoo Search (CS) algorithm has been proven to have a good ability for finding the global optimum; however, it has a slow convergence rate. In this study, a hybrid algorithm based on PSO and CS is proposed to make use of the advantages of both PSO and CS algorithms. The proposed hybrid algorithm is employed as a new training method for feedforward neural networks (FNNs). To investigate the performance of the proposed algorithm, two benchmark problems are used and the results are compared with those obtained from FNNs trained by original PSO and CS algorithms. The experimental results show that the proposed hybrid algorithm outperforms both PSO and CS in training FNNs. Full article
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Open AccessArticle
Model Equivalence-Based Identification Algorithm for Equation-Error Systems with Colored Noise
Algorithms 2015, 8(2), 280-291; https://doi.org/10.3390/a8020280 - 02 Jun 2015
Cited by 9 | Viewed by 2206
Abstract
For equation-error autoregressive (EEAR) systems, this paper proposes an identification algorithm by means of the model equivalence transformation. The basic idea is to eliminate the autoregressive term in the model using the model transformation, to estimate the parameters of the converted system and [...] Read more.
For equation-error autoregressive (EEAR) systems, this paper proposes an identification algorithm by means of the model equivalence transformation. The basic idea is to eliminate the autoregressive term in the model using the model transformation, to estimate the parameters of the converted system and further to compute the parameter estimates of the original system using the comparative coefficient way and the model equivalence principle. For comparison, the recursive generalized least squares algorithm is given simply. The simulation results verify that the proposed algorithm is effective and can produce more accurate parameter estimates. Full article
Open AccessArticle
Dynamics and Fractal Dimension of Steffensen-Type Methods
Algorithms 2015, 8(2), 271-279; https://doi.org/10.3390/a8020271 - 01 Jun 2015
Cited by 5 | Viewed by 1832
Abstract
In this paper, the dynamical behavior of different optimal iterative schemes for solving nonlinear equations with increasing order, is studied. The tendency of the complexity of the Julia set is analyzed and referred to the fractal dimension. In fact, this fractal dimension can [...] Read more.
In this paper, the dynamical behavior of different optimal iterative schemes for solving nonlinear equations with increasing order, is studied. The tendency of the complexity of the Julia set is analyzed and referred to the fractal dimension. In fact, this fractal dimension can be shown to be a powerful tool to compare iterative schemes that estimate the solution of a nonlinear equation. Based on the box-counting algorithm, several iterative derivative-free methods of different convergence orders are compared. Full article
Open AccessArticle
On String Matching with Mismatches
Algorithms 2015, 8(2), 248-270; https://doi.org/10.3390/a8020248 - 26 May 2015
Cited by 5 | Viewed by 2512
Abstract
In this paper, we consider several variants of the pattern matching with mismatches problem. In particular, given a text \(T=t_1 t_2\cdots t_n\) and a pattern \(P=p_1p_2\cdots p_m\), we investigate the following problems: (1) pattern matching with mismatches: for every \(i, 1\leq i \leq [...] Read more.
In this paper, we consider several variants of the pattern matching with mismatches problem. In particular, given a text \(T=t_1 t_2\cdots t_n\) and a pattern \(P=p_1p_2\cdots p_m\), we investigate the following problems: (1) pattern matching with mismatches: for every \(i, 1\leq i \leq n-m+1\) output, the distance between \(P\) and \(t_i t_{i+1}\cdots t_{i+m-1}\); and (2) pattern matching with \(k\) mismatches: output those positions \(i\) where the distance between \(P\) and \(t_i t_{i+1}\cdots t_{i+m-1}\) is less than a given threshold \(k\). The distance metric used is the Hamming distance. We present some novel algorithms and techniques for solving these problems. We offer deterministic, randomized and approximation algorithms. We consider variants of these problems where there could be wild cards in either the text or the pattern or both. We also present an experimental evaluation of these algorithms. The source code is available at http://www.engr.uconn.edu/\(\sim\)man09004/kmis.zip. Full article
(This article belongs to the Special Issue Algorithmic Themes in Bioinformatics)
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Open AccessArticle
An Optimization Clustering Algorithm Based on Texture Feature Fusion for Color Image Segmentation
Algorithms 2015, 8(2), 234-247; https://doi.org/10.3390/a8020234 - 22 May 2015
Cited by 5 | Viewed by 2422
Abstract
We introduce a multi-feature optimization clustering algorithm for color image segmentation. The local binary pattern, the mean of the min-max difference, and the color components are combined as feature vectors to describe the magnitude change of grey value and the contrastive information of [...] Read more.
We introduce a multi-feature optimization clustering algorithm for color image segmentation. The local binary pattern, the mean of the min-max difference, and the color components are combined as feature vectors to describe the magnitude change of grey value and the contrastive information of neighbor pixels. In clustering stage, it gets the initial clustering center and avoids getting into local optimization by adding mutation operator of genetic algorithm to particle swarm optimization. Compared with well-known methods, the proposed method has an overall better segmentation performance and can segment image more accurately by evaluating the ratio of misclassification. Full article
(This article belongs to the Special Issue Clustering Algorithms)
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Open AccessArticle
Numerical Solution of Turbulence Problems by Solving Burgers’ Equation
Algorithms 2015, 8(2), 224-233; https://doi.org/10.3390/a8020224 - 08 May 2015
Cited by 5 | Viewed by 2770
Abstract
In this work we generate the numerical solutions of Burgers’ equation by applying the Crank-Nicholson method and different schemes for solving nonlinear systems, instead of using Hopf-Cole transformation to reduce Burgers’ equation into the linear heat equation. The method is analyzed on two [...] Read more.
In this work we generate the numerical solutions of Burgers’ equation by applying the Crank-Nicholson method and different schemes for solving nonlinear systems, instead of using Hopf-Cole transformation to reduce Burgers’ equation into the linear heat equation. The method is analyzed on two test problems in order to check its efficiency on different kinds of initial conditions. Numerical solutions as well as exact solutions for different values of viscosity are calculated, concluding that the numerical results are very close to the exact solution. Full article
(This article belongs to the Special Issue Numerical Algorithms for Solving Nonlinear Equations and Systems)
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Open AccessArticle
Pulmonary Nodule Detection from X-ray CT Images Based on Region Shape Analysis and Appearance-based Clustering
Algorithms 2015, 8(2), 209-223; https://doi.org/10.3390/a8020209 - 08 May 2015
Cited by 3 | Viewed by 2467
Abstract
In this paper, we propose a detection method of pulmonary nodules in X-ray computed tomography (CT) scans by use of three image filters and appearance-based k-means clustering. First, voxel values are suppressed in radial directions so as to eliminate extra regions in the [...] Read more.
In this paper, we propose a detection method of pulmonary nodules in X-ray computed tomography (CT) scans by use of three image filters and appearance-based k-means clustering. First, voxel values are suppressed in radial directions so as to eliminate extra regions in the volumes of interest (VOIs). Globular regions are enhanced by moment-of-inertia tensors where the voxel values in the VOIs are regarded as mass. Excessively enhanced voxels are reduced based on displacement between the VOI centers and the gravity points of the voxel values in the VOIs. Initial nodule candidates are determined by these filtering processings. False positives are reduced by, first, normalizing the directions of intensity distributions in the VOIs by rotating the VOIs based on the eigenvectors of the moment-of-inertia tensors, and then applying an appearance-based two-step k-means clustering technique to the rotated VOIs. The proposed method is applied to actual CT scans and experimental results are shown. Full article
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Open AccessArticle
From Enumerating to Generating: A Linear Time Algorithm for Generating 2D Lattice Paths with a Given Number of Turns
Algorithms 2015, 8(2), 190-208; https://doi.org/10.3390/a8020190 - 08 May 2015
Viewed by 1925
Abstract
We propose a linear time algorithm, called G2DLP, for generating 2D lattice L(n1, n2) paths, equivalent to two-item multiset permutations, with a given number of turns. The usage of turn has three meanings: in the [...] Read more.
We propose a linear time algorithm, called G2DLP, for generating 2D lattice L(n1, n2) paths, equivalent to two-item multiset permutations, with a given number of turns. The usage of turn has three meanings: in the context of multiset permutations, it means that two consecutive elements of a permutation belong to two different items; in lattice path enumerations, it means that the path changes its direction, either from eastward to northward or from northward to eastward; in open shop scheduling, it means that we transfer a job from one type of machine to another. The strategy of G2DLP is divide-and-combine; the division is based on the enumeration results of a previous study and is achieved by aid of an integer partition algorithm and a multiset permutation algorithm; the combination is accomplished by a concatenation algorithm that constructs the paths we require. The advantage of G2DLP is twofold. First, it is optimal in the sense that it directly generates all feasible paths without visiting an infeasible one. Second, it can generate all paths in any specified order of turns, for example, a decreasing order or an increasing order. In practice, two applications, scheduling and cryptography, are discussed. Full article
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Open AccessArticle
An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors
Algorithms 2015, 8(2), 177-189; https://doi.org/10.3390/a8020177 - 07 May 2015
Cited by 4 | Viewed by 3021
Abstract
The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data [...] Read more.
The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets. Full article
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Open AccessArticle
Multiobjective Cloud Particle Optimization Algorithm Based on Decomposition
Algorithms 2015, 8(2), 157-176; https://doi.org/10.3390/a8020157 - 23 Apr 2015
Cited by 7 | Viewed by 2712
Abstract
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has received attention from researchers in recent years. This paper presents a new multiobjective algorithm based on decomposition and the cloud model called multiobjective decomposition evolutionary algorithm based on Cloud Particle Differential Evolution (MOEA/D-CPDE). In [...] Read more.
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has received attention from researchers in recent years. This paper presents a new multiobjective algorithm based on decomposition and the cloud model called multiobjective decomposition evolutionary algorithm based on Cloud Particle Differential Evolution (MOEA/D-CPDE). In the proposed method, the best solution found so far acts as a seed in each generation and evolves two individuals by cloud generator. A new individual is produced by updating the current individual with the position vector difference of these two individuals. The performance of the proposed algorithm is carried on 16 well-known multi-objective problems. The experimental results indicate that MOEA/D-CPDE is competitive. Full article
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Open AccessArticle
The Auxiliary Problem Principle with Self-Adaptive Penalty Parameter for Multi-Area Economic Dispatch Problem
Algorithms 2015, 8(2), 144-156; https://doi.org/10.3390/a8020144 - 22 Apr 2015
Viewed by 2243
Abstract
The auxiliary problem principle is a powerful tool for solving multi-area economic dispatch problem. One of the main drawbacks of the auxiliary problem principle method is that the convergence performance depends on the selection of penalty parameter. In this paper, we propose a [...] Read more.
The auxiliary problem principle is a powerful tool for solving multi-area economic dispatch problem. One of the main drawbacks of the auxiliary problem principle method is that the convergence performance depends on the selection of penalty parameter. In this paper, we propose a self-adaptive strategy to adjust penalty parameter based on the iterative information, the proposed approach is verified by two given test systems. The corresponding simulation results demonstrate that the proposed self-adaptive auxiliary problem principle iterative scheme is robust in terms of the selection of penalty parameter and has better convergence rate compared with the traditional auxiliary problem principle method. Full article
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Open AccessArticle
A Clustering Algorithm based on Feature Weighting Fuzzy Compactness and Separation
Algorithms 2015, 8(2), 128-143; https://doi.org/10.3390/a8020128 - 13 Apr 2015
Cited by 3 | Viewed by 2391
Abstract
Aiming at improving the well-known fuzzy compactness and separation algorithm (FCS), this paper proposes a new clustering algorithm based on feature weighting fuzzy compactness and separation (WFCS). In view of the contribution of features to clustering, the proposed algorithm introduces the feature weighting [...] Read more.
Aiming at improving the well-known fuzzy compactness and separation algorithm (FCS), this paper proposes a new clustering algorithm based on feature weighting fuzzy compactness and separation (WFCS). In view of the contribution of features to clustering, the proposed algorithm introduces the feature weighting into the objective function. We first formulate the membership and feature weighting, and analyze the membership of data points falling on the crisp boundary, then give the adjustment strategy. The proposed WFCS is validated both on simulated dataset and real dataset. The experimental results demonstrate that the proposed WFCS has the characteristics of hard clustering and fuzzy clustering, and outperforms many existing clustering algorithms with respect to three metrics: Rand Index, Xie-Beni Index and Within-Between(WB) Index. Full article
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Open AccessArticle
A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables
Algorithms 2015, 8(2), 100-127; https://doi.org/10.3390/a8020100 - 08 Apr 2015
Cited by 24 | Viewed by 3688
Abstract
Maximum power point tracking (MPPT) is one of the key functions of the solar power management system in solar energy deployment. This paper investigates the design of fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Six fuzzy MPPT algorithms, based on [...] Read more.
Maximum power point tracking (MPPT) is one of the key functions of the solar power management system in solar energy deployment. This paper investigates the design of fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Six fuzzy MPPT algorithms, based on different input variables, were considered in this study, namely (i) slope (of solar power-versus-solar voltage) and changes of the slope; (ii) slope and variation of the power; (iii) variation of power and variation of voltage; (iv) variation of power and variation of current; (v) sum of conductance and increment of the conductance; and (vi) sum of angles of arctangent of the conductance and arctangent of increment of the conductance. Algorithms (i)–(iv) have two input variables each while algorithms (v) and (vi) use a single input variable. The fuzzy logic MPPT function is deployed using a buck-boost power converter. This paper presents the details of the determinations, considerations of the fuzzy rules, as well as advantages and disadvantages of each MPPT algorithm based upon photovoltaic (PV) cell properties. The range of the input variable of Algorithm (vi) is finite and the maximum power point condition is well defined in steady condition and, therefore, it can be used for multipurpose controller design. Computer simulations are conducted to verify the design. Full article
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Open AccessArticle
Statistical Properties of Protein-Protein Interfaces
Algorithms 2015, 8(2), 92-99; https://doi.org/10.3390/a8020092 - 02 Apr 2015
Cited by 9 | Viewed by 2399
Abstract
The properties of 1172 protein complexes (downloaded from the Protein Data Bank (PDB)) have been studied based on the concept of circular variance as a buriedness indicator and the concept of mutual proximity as a parameter-free definition of contact. The propensities of residues [...] Read more.
The properties of 1172 protein complexes (downloaded from the Protein Data Bank (PDB)) have been studied based on the concept of circular variance as a buriedness indicator and the concept of mutual proximity as a parameter-free definition of contact. The propensities of residues to be in the protein, on the surface or form contact, as well as residue pairs to form contact were calculated. In addition, the concept of circular variance has been used to compare the ruggedness and shape of the contact surface with the overall surface. Full article
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Open AccessArticle
A Stable Gaussian Fitting Procedure for the Parameterization of Remote Sensed Thermal Images
Algorithms 2015, 8(2), 82-91; https://doi.org/10.3390/a8020082 - 27 Mar 2015
Cited by 4 | Viewed by 2266
Abstract
An image analysis procedure based on a two dimensional Gaussian fitting is presented and applied to satellite maps describing the surface urban heat island (SUHI). The application of this fitting technique allows us to parameterize the SUHI pattern in order to better understand [...] Read more.
An image analysis procedure based on a two dimensional Gaussian fitting is presented and applied to satellite maps describing the surface urban heat island (SUHI). The application of this fitting technique allows us to parameterize the SUHI pattern in order to better understand its intensity trend and also to perform quantitative comparisons among different images in time and space. The proposed procedure is computationally rapid and stable, executing an initial guess parameter estimation by a multiple regression before the iterative nonlinear fitting. The Gaussian fit was applied to both low and high resolution images (1 km and 30 m pixel size) and the results of the SUHI parameterization shown. As expected, a reduction of the correlation coefficient between the map values and the Gaussian surface was observed for the image with the higher spatial resolution due to the greater variability of the SUHI values. Since the fitting procedure provides a smoothed Gaussian surface, it has better performance when applied to low resolution images, even if the reliability of the SUHI pattern representation can be preserved also for high resolution images. Full article
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