Algorithms

418 KiB

Open AccessArticle

Noncircular Sources-Based Sparse Representation Algorithm for Direction of Arrival Estimation in MIMO Radar with Mutual Coupling

by Weidong Zhou, Jing Liu, Pengxiang Zhu, Wenhe Gong and Jiaxin Hou

Algorithms 2016, 9(3), 61; https://doi.org/10.3390/a9030061 - 8 Sep 2016

Cited by 3 | Viewed by 6269

Abstract

In this paper, a reweighted sparse representation algorithm based on noncircular sources is proposed, and the problem of the direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with mutual coupling is addressed. Making full use of the special structure of banded [...] Read more.

In this paper, a reweighted sparse representation algorithm based on noncircular sources is proposed, and the problem of the direction of arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar with mutual coupling is addressed. Making full use of the special structure of banded symmetric Toeplitz mutual coupling matrices (MCM), the proposed algorithm firstly eliminates the effect of mutual coupling by linear transformation. Then, a reduced dimensional transformation is exploited to reduce the computational complexity of the proposed algorithm. Furthermore, by utilizing the noncircular feature of signals, the new extended received data matrix is formulated to enlarge the array aperture. Finally, based on the new received data, a reweighted matrix is constructed, and the proposed method further designs the joint reweighted sparse representation scheme to achieve the DOA estimation by solving the

l_{1}

-norm constraint minimization problem. The proposed method enlarges the array aperture due to the application of signal noncircularity, and in the presence of mutual coupling, the proposed algorithm provides higher resolution and better angle estimation performance than ESPRIT-like,

l_{1}

-SVD and

l_{1}

-SRDML (sparse representation deterministic maximum likelihood) algorithms. Numerical experiment results verify the effectiveness and advantages of the proposed method. Full article

► Show Figures

Figure 1

1649 KiB

Open AccessArticle

HMM Adaptation for Improving a Human Activity Recognition System

by Rubén San-Segundo, Juan M. Montero, José Moreno-Pimentel and José M. Pardo

Algorithms 2016, 9(3), 60; https://doi.org/10.3390/a9030060 - 2 Sep 2016

Cited by 13 | Viewed by 7818

Abstract

When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR) system. [...] Read more.

When developing a fully automatic system for evaluating motor activities performed by a person, it is necessary to segment and recognize the different activities in order to focus the analysis. This process must be carried out by a Human Activity Recognition (HAR) system. This paper proposes a user adaptation technique for improving a HAR system based on Hidden Markov Models (HMMs). This system segments and recognizes six different physical activities (walking, walking upstairs, walking downstairs, sitting, standing and lying down) using inertial signals from a smartphone. The system is composed of a feature extractor for obtaining the most relevant characteristics from the inertial signals, a module for training the six HMMs (one per activity), and the last module for segmenting new activity sequences using these models. The user adaptation technique consists of a Maximum A Posteriori (MAP) approach that adapts the activity HMMs to the user, using some activity examples from this specific user. The main results on a public dataset have reported a significant relative error rate reduction of more than 30%. In conclusion, adapting a HAR system to the user who is performing the physical activities provides significant improvement in the system’s performance. Full article

(This article belongs to the Special Issue Algorithms for Psycho-Motor Training and Performance Using Wearable Technologies)

► Show Figures

Figure 1

1043 KiB

Open AccessArticle

Binary Cockroach Swarm Optimization for Combinatorial Optimization Problem

by Ibidun Christiana Obagbuwa and Ademola Philips Abidoye

Algorithms 2016, 9(3), 59; https://doi.org/10.3390/a9030059 - 2 Sep 2016

Cited by 10 | Viewed by 6742

Abstract

The Cockroach Swarm Optimization (CSO) algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and its variants operate mainly in continuous search space [...] Read more.

The Cockroach Swarm Optimization (CSO) algorithm is inspired by cockroach social behavior. It is a simple and efficient meta-heuristic algorithm and has been applied to solve global optimization problems successfully. The original CSO algorithm and its variants operate mainly in continuous search space and cannot solve binary-coded optimization problems directly. Many optimization problems have their decision variables in binary. Binary Cockroach Swarm Optimization (BCSO) is proposed in this paper to tackle such problems and was evaluated on the popular Traveling Salesman Problem (TSP), which is considered to be an NP-hard Combinatorial Optimization Problem (COP). A transfer function was employed to map a continuous search space CSO to binary search space. The performance of the proposed algorithm was tested firstly on benchmark functions through simulation studies and compared with the performance of existing binary particle swarm optimization and continuous space versions of CSO. The proposed BCSO was adapted to TSP and applied to a set of benchmark instances of symmetric TSP from the TSP library. The results of the proposed Binary Cockroach Swarm Optimization (BCSO) algorithm on TSP were compared to other meta-heuristic algorithms. Full article

► Show Figures

Figure 1

452 KiB

Open AccessArticle

LR Parsing for LCFRS

by Laura Kallmeyer and Wolfgang Maier

Algorithms 2016, 9(3), 58; https://doi.org/10.3390/a9030058 - 27 Aug 2016

Cited by 3 | Viewed by 6011

Abstract

LR parsing is a popular parsing strategy for variants of Context-Free Grammar (CFG). It has also been used for mildly context-sensitive formalisms, such as Tree-Adjoining Grammar. In this paper, we present the first LR-style parsing algorithm for Linear Context-Free Rewriting Systems (LCFRS), a [...] Read more.

LR parsing is a popular parsing strategy for variants of Context-Free Grammar (CFG). It has also been used for mildly context-sensitive formalisms, such as Tree-Adjoining Grammar. In this paper, we present the first LR-style parsing algorithm for Linear Context-Free Rewriting Systems (LCFRS), a mildly context-sensitive extension of CFG which has received considerable attention in the last years in the context of natural language processing. Full article

(This article belongs to the Special Issue Selected papers from “Theorietage der Gesellschaft für Informatik” in Speyer; Invited Talks)

► Show Figures

Figure 1

614 KiB

Open AccessArticle

Uniform Page Migration Problem in Euclidean Space

by Amanj Khorramian and Akira Matsubayashi

Algorithms 2016, 9(3), 57; https://doi.org/10.3390/a9030057 - 23 Aug 2016

Cited by 4 | Viewed by 4844

Abstract

The page migration problem in Euclidean space is revisited. In this problem, online requests occur at any location to access a single page located at a server. Every request must be served, and the server has the choice to migrate from its current [...] Read more.

The page migration problem in Euclidean space is revisited. In this problem, online requests occur at any location to access a single page located at a server. Every request must be served, and the server has the choice to migrate from its current location to a new location in space. Each service costs the Euclidean distance between the server and request. A migration costs the distance between the former and the new server location, multiplied by the page size. We study the problem in the uniform model, in which the page has size

D = 1

. All request locations are not known in advance; however, they are sequentially presented in an online fashion. We design a

2.75

-competitive online algorithm that improves the current best upper bound for the problem with the unit page size. We also provide a lower bound of

2.732

for our algorithm. It was already known that 2.5 is a lower bound for this problem. Full article

► Show Figures

Figure 1

2082 KiB

Open AccessArticle

Multiple Artificial Neural Networks with Interaction Noise for Estimation of Spatial Categorical Variables

by Xiang Huang and Zhizhong Wang

Algorithms 2016, 9(3), 56; https://doi.org/10.3390/a9030056 - 20 Aug 2016

Cited by 1 | Viewed by 4871

Abstract

This paper presents a multiple artificial neural networks (MANN) method with interaction noise for estimating the occurrence probabilities of different classes at any site in space. The MANN consists of several independent artificial neural networks, the number of which is determined by the [...] Read more.

This paper presents a multiple artificial neural networks (MANN) method with interaction noise for estimating the occurrence probabilities of different classes at any site in space. The MANN consists of several independent artificial neural networks, the number of which is determined by the neighbors around the target location. In the proposed algorithm, the conditional or pre-posterior (multi-point) probabilities are viewed as output nodes, which can be estimated by weighted combinations of input nodes: two-point transition probabilities. The occurrence probability of a certain class at a certain location can be easily computed by the product of output probabilities using Bayes’ theorem. Spatial interaction or redundancy information can be measured in the form of interaction noises. Prediction results show that the method of MANN with interaction noise has a higher classification accuracy than the traditional Markov chain random fields (MCRF) model and can successfully preserve small-scale features. Full article

► Show Figures

Figure 1

1217 KiB

Open AccessArticle

A Novel AHRS Inertial Sensor-Based Algorithm for Wheelchair Propulsion Performance Analysis

by Jonathan Bruce Shepherd, Tomohito Wada, David Rowlands and Daniel Arthur James

Algorithms 2016, 9(3), 55; https://doi.org/10.3390/a9030055 - 17 Aug 2016

Cited by 15 | Viewed by 6937

Abstract

With the increasing rise of professionalism in sport, athletes, teams, and coaches are looking to technology to monitor performance in both games and training in order to find a competitive advantage. The use of inertial sensors has been proposed as a cost effective [...] Read more.

With the increasing rise of professionalism in sport, athletes, teams, and coaches are looking to technology to monitor performance in both games and training in order to find a competitive advantage. The use of inertial sensors has been proposed as a cost effective and adaptable measurement device for monitoring wheelchair kinematics; however, the outcomes are dependent on the reliability of the processing algorithms. Though there are a variety of algorithms that have been proposed to monitor wheelchair propulsion in court sports, they all have limitations. Through experimental testing, we have shown the Attitude and Heading Reference System (AHRS)-based algorithm to be a suitable and reliable candidate algorithm for estimating velocity, distance, and approximating trajectory. The proposed algorithm is computationally inexpensive, agnostic of wheel camber, not sensitive to sensor placement, and can be embedded for real-time implementations. The research is conducted under Griffith University Ethics (GU Ref No: 2016/294). Full article

► Show Figures

Figure 1

2500 KiB

Open AccessArticle

Sign Function Based Sparse Adaptive Filtering Algorithms for Robust Channel Estimation under Non-Gaussian Noise Environments

by Tingping Zhang and Guan Gui

Algorithms 2016, 9(3), 54; https://doi.org/10.3390/a9030054 - 12 Aug 2016

Cited by 4 | Viewed by 5146

Abstract

Robust channel estimation is required for coherent demodulation in multipath fading wireless communication systems which are often deteriorated by non-Gaussian noises. Our research is motivated by the fact that classical sparse least mean square error (LMS) algorithms are very sensitive to impulsive noise [...] Read more.

Robust channel estimation is required for coherent demodulation in multipath fading wireless communication systems which are often deteriorated by non-Gaussian noises. Our research is motivated by the fact that classical sparse least mean square error (LMS) algorithms are very sensitive to impulsive noise while standard SLMS algorithm does not take into account the inherent sparsity information of wireless channels. This paper proposes a sign function based sparse adaptive filtering algorithm for developing robust channel estimation techniques. Specifically, sign function based least mean square error (SLMS) algorithms to remove the non-Gaussian noise that is described by a symmetric α-stable noise model. By exploiting channel sparsity, sparse SLMS algorithms are proposed by introducing several effective sparse-promoting functions into the standard SLMS algorithm. The convergence analysis of the proposed sparse SLMS algorithms indicates that they outperform the standard SLMS algorithm for robust sparse channel estimation, which can be also verified by simulation results. Full article

► Show Figures

Figure 1

1392 KiB

Open AccessArticle

Control for Ship Course-Keeping Using Optimized Support Vector Machines

by Weilin Luo and Hongchao Cong

Algorithms 2016, 9(3), 52; https://doi.org/10.3390/a9030052 - 10 Aug 2016

Cited by 8 | Viewed by 4258

Abstract

Support vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account [...] Read more.

Support vector machines (SVM) are proposed in order to obtain a robust controller for ship course-keeping. A cascaded system is constructed by combining the dynamics of the rudder actuator with the dynamics of ship motion. Modeling errors and disturbances are taken into account in the plant. A controller with a simple structure is produced by applying an SVM and L2-gain design. The SVM is used to identify the complicated nonlinear functions and the modeling errors in the plant. The Lagrangian factors in the SVM are obtained using on-line tuning algorithms. L2-gain design is applied to suppress the disturbances. To obtain the optimal parameters in the SVM, then particle swarm optimization (PSO) method is incorporated. The stability and robustness of the close-loop system are confirmed by Lyapunov stability analysis. Numerical simulation is performed to demonstrate the validity of the proposed hybrid controller and its superior performance over a conventional PD controller. Full article

(This article belongs to the Special Issue Data Analytics and Optimization for Hybrid Communication Systems)

► Show Figures

Figure 1

14805 KiB

Open AccessArticle

Faster Force-Directed Graph Drawing with the Well-Separated Pair Decomposition

by Fabian Lipp, Alexander Wolff and Johannes Zink

Algorithms 2016, 9(3), 53; https://doi.org/10.3390/a9030053 - 4 Aug 2016

Cited by 4 | Viewed by 8923

Abstract

The force-directed paradigm is one of the few generic approaches to drawing graphs. Since force-directed algorithms can be extended easily, they are used frequently. Most of these algorithms are, however, quite slow on large graphs, as they compute a quadratic number of forces [...] Read more.

The force-directed paradigm is one of the few generic approaches to drawing graphs. Since force-directed algorithms can be extended easily, they are used frequently. Most of these algorithms are, however, quite slow on large graphs, as they compute a quadratic number of forces in each iteration. We give a new algorithm that takes only

O (m + n log n)

time per iteration when laying out a graph with n vertices and m edges. Our algorithm approximates the true forces using the so-called well-separated pair decomposition. We perform experiments on a large number of graphs and show that we can strongly reduce the runtime, even on graphs with less than a hundred vertices, without a significant influence on the quality of the drawings (in terms of the number of crossings and deviation in edge lengths). Full article

(This article belongs to the Special Issue Graph Drawing and Experimental Algorithms)

► Show Figures

Graphical abstract

6648 KiB

Open AccessArticle

A Multi-Objective Harmony Search Algorithm for Sustainable Design of Floating Settlements

by Cemre Cubukcuoglu, Ioannis Chatzikonstantinou, Mehmet Fatih Tasgetiren, I. Sevil Sariyildiz and Quan-Ke Pan

Algorithms 2016, 9(3), 51; https://doi.org/10.3390/a9030051 - 30 Jul 2016

Cited by 18 | Viewed by 5637

Abstract

This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla, which is a rural touristic region located on the [...] Read more.

This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla, which is a rural touristic region located on the west coast of Turkey, near the metropolis of Izmir. The problem at hand includes both engineering and architectural aspects that need to be addressed in a comprehensive manner. We thus adapt the view as a multi-objective constrained real-parameter optimization problem. Specifically, we consider three objectives, which are conflicting. The first one aims at maximizing accessibility of urban functions such as housing and public spaces, as well as special functions, such as a marina for yachts and a yacht club. The second one aims at ensuring the wind protection of the general areas of the settlement, by adequately placing them in between neighboring land masses. The third one aims at maximizing visibility of the settlement from external observation points, so as to maximize the exposure of the settlement. To address this complex multi-objective optimization problem and identify lucrative alternative design solutions, a multi-objective harmony search algorithm (MOHS) is developed and applied in this paper. When compared to the Differential Evolution algorithm developed for the problem in the literature, we demonstrate that MOHS achieves competitive or slightly better performance in terms of hyper volume calculation, and gives promising results when the Pareto front approximation is examined. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)

► Show Figures

Graphical abstract

352 KiB

Open AccessArticle

Utilizing Network Structure to Accelerate Markov Chain Monte Carlo Algorithms

by Ahmad Askarian, Rupei Xu and András Faragó

Algorithms 2016, 9(3), 50; https://doi.org/10.3390/a9030050 - 29 Jul 2016

Cited by 1 | Viewed by 4476

Abstract

We consider the problem of estimating the measure of subsets in very large networks. A prime tool for this purpose is the Markov Chain Monte Carlo (MCMC) algorithm. This algorithm, while extremely useful in many cases, still often suffers from the drawback of [...] Read more.

We consider the problem of estimating the measure of subsets in very large networks. A prime tool for this purpose is the Markov Chain Monte Carlo (MCMC) algorithm. This algorithm, while extremely useful in many cases, still often suffers from the drawback of very slow convergence. We show that in a special, but important case, it is possible to obtain significantly better bounds on the convergence rate. This special case is when the huge state space can be aggregated into a smaller number of clusters, in which the states behave approximately the same way (but their behavior still may not be identical). A Markov chain with this structure is called quasi-lumpable. This property allows the aggregation of states (nodes) into clusters. Our main contribution is a rigorously proved bound on the rate at which the aggregated state distribution approaches its limit in quasi-lumpable Markov chains. We also demonstrate numerically that in certain cases this can indeed lead to a significantly accelerated way of estimating the measure of subsets. The result can be a useful tool in the analysis of complex networks, whenever they have a clustering that aggregates nodes with similar (but not necessarily identical) behavior. Full article

(This article belongs to the Special Issue Algorithms for Complex Network Analysis)

► Show Figures

Figure 1

264 KiB

Open AccessArticle

Data Filtering Based Recursive and Iterative Least Squares Algorithms for Parameter Estimation of Multi-Input Output Systems

by Jiling Ding

Algorithms 2016, 9(3), 49; https://doi.org/10.3390/a9030049 - 26 Jul 2016

Cited by 9 | Viewed by 4657

Abstract

This paper discusses the parameter estimation problems of multi-input output-error autoregressive (OEAR) systems. By combining the auxiliary model identification idea and the data filtering technique, a data filtering based recursive generalized least squares (F-RGLS) identification algorithm and a data filtering based iterative least [...] Read more.

This paper discusses the parameter estimation problems of multi-input output-error autoregressive (OEAR) systems. By combining the auxiliary model identification idea and the data filtering technique, a data filtering based recursive generalized least squares (F-RGLS) identification algorithm and a data filtering based iterative least squares (F-LSI) identification algorithm are derived. Compared with the F-RGLS algorithm, the proposed F-LSI algorithm is more effective and can generate more accurate parameter estimates. The simulation results confirm this conclusion. Full article

(This article belongs to the Special Issue Algorithms for Complex Network Analysis)

► Show Figures

Graphical abstract

841 KiB

Open AccessArticle

Semi-Supervised Classification Based on Low Rank Representation

by Xuan Hou, Guangjun Yao and Jun Wang

Algorithms 2016, 9(3), 48; https://doi.org/10.3390/a9030048 - 22 Jul 2016

Cited by 3 | Viewed by 4598

Abstract

Graph-based semi-supervised classification uses a graph to capture the relationship between samples and exploits label propagation techniques on the graph to predict the labels of unlabeled samples. However, it is difficult to construct a graph that faithfully describes the relationship between high-dimensional samples. [...] Read more.

Graph-based semi-supervised classification uses a graph to capture the relationship between samples and exploits label propagation techniques on the graph to predict the labels of unlabeled samples. However, it is difficult to construct a graph that faithfully describes the relationship between high-dimensional samples. Recently, low-rank representation has been introduced to construct a graph, which can preserve the global structure of high-dimensional samples and help to train accurate transductive classifiers. In this paper, we take advantage of low-rank representation for graph construction and propose an inductive semi-supervised classifier called Semi-Supervised Classification based on Low-Rank Representation (SSC-LRR). SSC-LRR first utilizes a linearized alternating direction method with adaptive penalty to compute the coefficient matrix of low-rank representation of samples. Then, the coefficient matrix is adopted to define a graph. Finally, SSC-LRR incorporates this graph into a graph-based semi-supervised linear classifier to classify unlabeled samples. Experiments are conducted on four widely used facial datasets to validate the effectiveness of the proposed SSC-LRR and the results demonstrate that SSC-LRR achieves higher accuracy than other related methods. Full article

► Show Figures

Graphical abstract

2118 KiB

Open AccessArticle

A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems

by Pei-Chann Chang, Cheng-Hui Lin and Meng-Hui Chen

Algorithms 2016, 9(3), 47; https://doi.org/10.3390/a9030047 - 22 Jul 2016

Cited by 68 | Viewed by 10265

Abstract

This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) [...] Read more.

This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005–2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)

► Show Figures

Graphical abstract

1035 KiB

Open AccessArticle

Affinity Propagation Clustering Using Path Based Similarity

by Yuan Jiang, Yuliang Liao and Guoxian Yu

Algorithms 2016, 9(3), 46; https://doi.org/10.3390/a9030046 - 21 Jul 2016

Cited by 4 | Viewed by 6878

Abstract

Clustering is a fundamental task in data mining. Affinity propagation clustering (APC) is an effective and efficient clustering technique that has been applied in various domains. APC iteratively propagates information between affinity samples, updates the responsibility matrix and availability matrix, and employs these [...] Read more.

Clustering is a fundamental task in data mining. Affinity propagation clustering (APC) is an effective and efficient clustering technique that has been applied in various domains. APC iteratively propagates information between affinity samples, updates the responsibility matrix and availability matrix, and employs these matrices to choose cluster centers (or exemplars) of respective clusters. However, since it mainly uses negative Euclidean distance between exemplars and samples as the similarity between them, it is difficult to identify clusters with complex structure. Therefore, the performance of APC deteriorates on samples distributed with complex structure. To mitigate this problem, we propose an improved APC based on a path-based similarity (APC-PS). APC-PS firstly utilizes negative Euclidean distance to find exemplars of clusters. Then, it employs the path-based similarity to measure the similarity between exemplars and samples, and to explore the underlying structure of clusters. Next, it assigns non-exemplar samples to their respective clusters via that similarity. Our empirical study on synthetic and UCI datasets shows that the proposed APC-PS significantly outperforms original APC and other related approaches. Full article

► Show Figures

Figure 1

4475 KiB

Open AccessArticle

Designing a Framework to Improve Time Series Data of Construction Projects: Application of a Simulation Model and Singular Spectrum Analysis

by Zahra Hojjati Tavassoli, Seyed Hossein Iranmanesh and Ahmad Tavassoli Hojjati

Algorithms 2016, 9(3), 45; https://doi.org/10.3390/a9030045 - 18 Jul 2016

Cited by 1 | Viewed by 5477

Abstract

During a construction project life cycle, project costs and time estimations contribute greatly to baseline scheduling. Besides, schedule risk analysis and project control are also influenced by the above factors. Although many papers have offered estimation techniques, little attempt has been made to [...] Read more.

During a construction project life cycle, project costs and time estimations contribute greatly to baseline scheduling. Besides, schedule risk analysis and project control are also influenced by the above factors. Although many papers have offered estimation techniques, little attempt has been made to generate project time series data as daily progressive estimations in different project environments that could help researchers in generating general and customized formulae in further studies. This paper, however, is an attempt to introduce a new simulation approach to reflect the data regarding time series progress of the project, considering the specifications and the complexity of the project and the environment where the project is performed. Moreover, this simulator can equip project managers with estimated information, which reassures them of the execution stages of the project although they lack historical data. A case study is presented to show the usefulness of the model and its applicability in practice. In this study, singular spectrum analysis has been employed to analyze the simulated outputs, and the results are separated based on their signal and noise trends. The signal trend is used as a point-of-reference to compare the outputs of a simulation employing S-curve technique results and the formulae corresponding to earned value management, as well as the life of a given project. Full article

► Show Figures

Figure 1

582 KiB

Open AccessArticle

A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes

by Till Tantau

Algorithms 2016, 9(3), 44; https://doi.org/10.3390/a9030044 - 9 Jul 2016

Cited by 2 | Viewed by 5205

Abstract

Algorithmic metatheorems state that if a problem can be described in a certain logic and the inputs are structured in a certain way, then the problem can be solved with a certain amount of resources. As an example, by Courcelle’s Theorem, all monadic [...] Read more.

Algorithmic metatheorems state that if a problem can be described in a certain logic and the inputs are structured in a certain way, then the problem can be solved with a certain amount of resources. As an example, by Courcelle’s Theorem, all monadic second-order (“in a certain logic”) properties of graphs of bounded tree width (“structured in a certain way”) can be solved in linear time (“with a certain amount of resources”). Such theorems have become valuable tools in algorithmics: if a problem happens to have the right structure and can be described in the right logic, they immediately yield a (typically tight) upper bound on the time complexity of the problem. Perhaps even more importantly, several complex algorithms rely on algorithmic metatheorems internally to solve subproblems, which considerably broadens the range of applications of these theorems. This paper is intended as a gentle introduction to the ideas behind algorithmic metatheorems, especially behind some recent results concerning space and circuit classes, and tries to give a flavor of the range of their applications. Full article

(This article belongs to the Special Issue Selected papers from “Theorietage der Gesellschaft für Informatik” in Speyer; Invited Talks)

► Show Figures

Graphical abstract

1081 KiB

Open AccessArticle

Opposition-Based Adaptive Fireworks Algorithm

by Chibing Gong

Algorithms 2016, 9(3), 43; https://doi.org/10.3390/a9030043 - 8 Jul 2016

Cited by 15 | Viewed by 4802

Abstract

A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to [...] Read more.

A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based adaptive fireworks algorithm (OAFWA). The final results conclude that OAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally, OAFWA was compared with a bat algorithm (BA), differential evolution (DE), self-adapting control parameters in differential evolution (jDE), a firefly algorithm (FA), and a standard particle swarm optimization 2011 (SPSO2011) algorithm. The research results indicate that OAFWA ranks the highest of the six algorithms for both solution accuracy and runtime cost. Full article

(This article belongs to the Special Issue Metaheuristic Algorithms in Optimization and Applications)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Algorithms, Volume 9, Issue 3 (September 2016) – 19 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI