A Reward Population-Based Differential Genetic Harmony Search Algorithm

To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is divided into four ordinary sub-populations and one reward sub-population, for each of which the evolution strategy of the differential genetic harmony search is used. After the evolution, the population with the optimal average fitness is combined with the reward population to produce a new reward population. During an experiment, tests were conducted first on determining the value of the harmony memory size (HMS) and the harmony memory consideration rate (HMCR), followed by an analysis of the effect of their values on the performance of the proposed algorithm. Then, six benchmark functions were selected for the experiment, and a comparison was made on the calculation results of the standard harmony memory search algorithm, reward population harmony search algorithm, differential genetic harmony algorithm, and reward population-based differential genetic harmony search algorithm. The result suggests that the reward population-based differential genetic harmony search algorithm has the merits of a strong global search ability, high solving accuracy, and satisfactory stability.

Tries-Based Parallel Solutions for Generating Perfect Crosswords Grids

A general crossword grid generation is considered an NP-complete problem and theoretically it could be a good candidate to be used by cryptography algorithms. In this article, we propose a new algorithm for generating perfect crosswords grids (with no black boxes) that relies on using tries data structures, which are very important for reducing the time for finding the solutions, and offers good opportunity for parallelisation, too. The algorithm uses a special tries representation and it is very efficient, but through parallelisation the performance is improved to a level that allows the solution to be obtained extremely fast. The experiments were conducted using a dictionary of almost 700,000 words, and the solutions were obtained using the parallelised version with an execution time in the order of minutes. We demonstrate here that finding a perfect crossword grid could be solved faster than has been estimated before, if we use tries as supporting data structures together with parallelisation. Still, if the size of the dictionary is increased by a lot (e.g., considering a set of dictionaries for different languages—not only for one), or through a generalisation to a 3D space or multidimensional spaces, then the problem still could be investigated for a possible usage in cryptography.

Graph Based Feature Selection for Reduction of Dimensionality in Next-Generation RNA Sequencing Datasets

Analysis of high-dimensional data, with more features () than observations () (), places significant demand in cost and memory computational usage attributes. Feature selection can be used to reduce the dimensionality of the data. We used a graph-based approach, principal component analysis (PCA) and recursive feature elimination to select features for classification from RNAseq datasets from two lung cancer datasets. The selected features were discretized for association rule mining where support and lift were used to generate informative rules. Our results show that the graph-based feature selection improved the performance of sequential minimal optimization (SMO) and multilayer perceptron classifiers (MLP) in both datasets. In association rule mining, features selected using the graph-based approach outperformed the other two feature-selection techniques at a support of 0.5 and lift of 2. The non-redundant rules reflect the inherent relationships between features. Biological features are usually related to functions in living systems, a relationship that cannot be deduced by feature selection and classification alone. Therefore, the graph-based feature-selection approach combined with rule mining is a suitable way of selecting and finding associations between features in high-dimensional RNAseq data.

MINC-NRL: An Information-Based Approach for Community Detection

Complex networks usually consist of dense-connected cliques, which are defined as communities. A community structure is a reflection of the local characteristics existing in the network topology, this makes community detection become an important research field to reveal the internal structural characteristics of networks. In this article, an information-based community detection approach MINC-NRL is proposed, which can be applied to both overlapping and non-overlapping community detection. MINC-NRL introduces network representation learning (NRL) to represent the target network as vectors, then generates a community evolution process based on these vectors to reduce the search space, and finally, finds the best community partition in this process using mutual information between network and communities (MINC). Experiments on real-world and synthetic data sets verifies the effectiveness of the approach in community detection, both on non-overlapping and overlapping tasks.

Parameter Optimization of Active Disturbance Rejection Controller Using Adaptive Differential Ant-Lion Optimizer

Parameter optimization in the field of control engineering has always been a research topic. This paper studies the parameter optimization of an active disturbance rejection controller. The parameter optimization problem in controller design can be summarized as a nonlinear optimization problem with constraints. It is often difficult and complicated to solve the problem directly, and meta-heuristic algorithms are suitable for this problem. As a relatively new method, the ant-lion optimization algorithm has attracted much attention and study. The contribution of this work is proposing an adaptive ant-lion algorithm, namely differential step-scaling ant-lion algorithm, to optimize parameters of the active disturbance rejection controller. Firstly, a differential evolution strategy is introduced to increase the diversity of the population and improve the global search ability of the algorithm. Then the step scaling method is adopted to ensure that the algorithm can obtain higher accuracy in a local search. Comparison with existing optimizers is conducted for different test functions with different qualities, the results show that the proposed algorithm has advantages in both accuracy and convergence speed. Simulations with different algorithms and different indexes are also carried out, the results show that the improved algorithm can search better parameters for the controllers.

Extremality of Disordered Phase of λ-Model on Cayley Trees

In this paper, we consider the λ-model for an arbitrary-order Cayley tree that has a disordered phase. Such a phase corresponds to a splitting Gibbs measure with free boundary conditions. In communication theory, such a measure appears naturally, and its extremality is related to the solvability of the non-reconstruction problem. In general, the disordered phase is not extreme; hence, it is natural to find a condition for their extremality. In the present paper, we present certain conditions for the extremality of the disordered phase of the λ-model.

Analog Circuit Fault Diagnosis Using a Novel Variant of aConvolutional Neural Network

Analog circuits play an important role in modern electronic systems. Aiming to accurately diagnose the faults of analog circuits, this paper proposes a novel variant of a convolutional neural network, namely, a multi-scale convolutional neural network with a selective kernel (MSCNN-SK). In MSCNN-SK, a multi-scale average difference layer is developed to compute multi-scale average difference sequences, and then these sequences are taken as the input of the model, which enables it to mine potential fault characteristics. In addition, a dynamic convolution kernel selection mechanism is introduced to adaptively adjust the receptive field, so that the feature extraction ability of MSCNN-SK is enhanced. Based on two well-known fault diagnosis circuits, comparison experiments are conducted, and experimental results show that our proposed method achieves higher performance.

Mixed Poisson Regression Models with Varying Dispersion Arising from Non-Conjugate Mixing Distributions

In this article we present a class of mixed Poisson regression models with varying dispersion arising from non-conjugate to the Poisson mixing distributions for modelling overdispersed claim counts in non-life insurance. The proposed family of models combined with the adopted modelling framework can provide sufficient flexibility for dealing with different levels of overdispersion. For illustrative purposes, the Poisson-lognormal regression model with regression structures on both its mean and dispersion parameters is employed for modelling claim count data from a motor insurance portfolio. Maximum likelihood estimation is carried out via an expectation-maximization type algorithm, which is developed for the proposed family of models and is demonstrated to perform satisfactorily.

On Parameter Identification for Reaction-Dominated Pore-Scale Reactive Transport Using Modified Bee Colony Algorithm

Parameter identification is an important research topic with a variety of applications in industrial and environmental problems. Usually, a functional has to be minimized in conjunction with parameter identification; thus, there is a certain similarity between the parameter identification and optimization. A number of rigorous and efficient algorithms for optimization problems were developed in recent decades for the case of a convex functional. In the case of a non-convex functional, the metaheuristic algorithms dominate. This paper discusses an optimization method called modified bee colony algorithm (MBC), which is a modification of the standard bees algorithm (SBA). The SBA is inspired by a particular intelligent behavior of honeybee swarms. The algorithm is adapted for the parameter identification of reaction-dominated pore-scale transport when a non-convex functional has to be minimized. The algorithm is first checked by solving a few benchmark problems, namely finding the minima for Shekel, Rosenbrock, Himmelblau and Rastrigin functions. A statistical analysis was carried out to compare the performance of MBC with the SBA and the artificial bee colony (ABC) algorithm. Next, MBC is applied to identify the three parameters in the Langmuir isotherm, which is used to describe the considered reaction. Here, 2D periodic porous media were considered. The simulation results show that the MBC algorithm can be successfully used for identifying admissible sets for the reaction parameters in reaction-dominated transport characterized by low Pecklet and high Damkholer numbers. Finite element approximation in space and implicit time discretization are exploited to solve the direct problem.

A Heuristic Methods-Based Power Distribution System Optimization Toolbox

This paper proposes a toolbox for simulating the effective integration of renewable energy sources into distribution systems. The toolbox uses four heuristic methods: the particle swarm optimization (PSO) method, and three recently developed methods, namely Gray Wolf Optimization (GWO), Ant Lion Optimization (ALO), and Whale Optimization Algorithm (WOA), for the efficient operation of power distribution systems. The toolbox consists of two main functionalities. The first one allows the user to select the test system to be solved (33-, 69-, or 141-bus test systems), the locations of the distributed generators (DGs), and the voltage regulators. In addition, the user selects the daily active power output profiles of the DGs, and the tool solves the voltage deviation problem for the specified time of day. The second functionality involves the simulation of energy storage systems and provides the optimal daily power output of the resources. With this program, a graphical user interface (GUI) allows users to select the test system, the optimization method to be used, the number of DGs and locations, the locations and number of battery energy storage systems (BESSs), and the tap changer locations. With the simple user interface, the user can manage the distribution system simulation and see the results by making appropriate changes to the test systems.