Parallel ACO with a Ring Neighborhood for Dynamic TSP

The current paper introduces a new parallel computing technique based on ant colony optimization for a dynamic routing problem. In the dynamic traveling salesman problem the distances between cities as travel times are no longer fixed. The new technique uses a parallel model for a problem variant that allows a slight movement of nodes within their Neighborhoods. The algorithm is tested with success on several large data sets.Comment: 8 pages, 1 figure; accepted J. Information Technology Researc

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Inverse Design of Three-Dimensional Frequency Selective Structures and Metamaterials using Multi-Objective Lazy Ant Colony Optimization

With the rise of big data and the “internet of things,” wireless signals permeate today’s environment more than ever before. As the demand for information and security continues to expand, the need for filtering a crowded signal space will become increasingly important. Although existing devices can achieve this with additional components, such as in-line filters and low noise amplifiers, these approaches introduce additional bulk, cost and complexity. An alternative, low-cost solution to filtering these signals can be achieved through the use of Frequency Selective Surfaces (FSSs), which are commonly used in antennas, polarizers, radomes, and intelligent architecture. FSSs typically consist of a doubly-periodic array of unit cells, which acts as a spatial electromagnetic filter that selectively rejects or transmits electromagnetic waves, based on the unit cell’s geometry and material properties. Unlike traditional analog filters, spatial filters must also account for the polarization and incidence angle of signals; thus, an ideal FSS maintains a given frequency response for all polarizations and incidence angles. Traditional FSS designs have ranged from planar structures with canonical shapes to miniaturized and multi-layer designs using fractals and other space-filling geometries. More recently, FSS research has expanded into three-dimensional (3D) designs, which have demonstrated enhanced fields of view over traditional planar and multi-layer designs. To date, nearly all FSSs still suffer from significant shifts in resonant frequencies or onset of grating lobes at incidence angles beyond 60 degrees in one or more polarizations. Additionally, while recent advances in additive manufacturing techniques have made fully 3D FSS designs increasingly popular, design tools to exploit these fabrication methods to develop FSSs with ultra-wide Fields of View (FOV) do not currently exist. In this dissertation, a Multi-Objective Lazy Ant Colony Optimization (MOLACO) scheme will be introduced and applied to the problem of 3D FSS design for extreme FOVs. The versatility of this algorithm will further be demonstrated through application to the design of meander line antennas, optical antennas, and phase-gradient metasurfaces

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Optimization Algorithms in Project Scheduling

Scheduling, or planning in a general perspective, is the backbone of project management; thus, the successful implementation of project scheduling is a key factor to projects’ success. Due to its complexity and challenging nature, scheduling has become one of the most famous research topics within the operational research context, and it has been widely researched in practical applications within various industries, especially manufacturing, construction, and computer engineering. Accordingly, the literature is rich with many implementations of different optimization algorithms and their extensions within the project scheduling problem (PSP) analysis field. This study is intended to exhibit the general modelling of the PSP, and to survey the implementations of various optimization algorithms adopted for solving the different types of the PSP

Feature subset selection in large dimensionality domains

Searching for an optimal feature subset from a high dimensional feature space is known to be an NP-complete problem. We present a hybrid algorithm, SAGA, for this task. SAGA combines the ability to avoid being trapped in a local minimum of Simulated Annealing with the very high rate of convergence of the crossover operator of Genetic Algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of Generalized Regression Neural Networks. We compare the performance over time of SAGA and well-known algorithms on synthetic and real datasets. The results show that SAGA outperforms existing algorithms

Swarm-based Algorithms for Neural Network Training

The main focus of this thesis is to compare the ability of various swarm intelligence algorithms when applied to the training of artificial neural networks. In order to compare the performance of the selected swarm intelligence algorithms both classification and regression datasets were chosen from the UCI Machine Learning repository. Swarm intelligence algorithms are compared in terms of training loss, training accuracy, testing loss, testing accuracy, hidden unit saturation, and overfitting. Our observations showed that Particle Swarm Optimization (PSO) was the best performing algorithm in terms of Training loss and Training accuracy. However, it was also found that the performance of PSO dropped considerably when examining the testing loss and testing accuracy results. For the classification problems, it was found that firefly algorithm, ant colony optimization, and fish school search outperformed PSO for testing loss and testing accuracy. It was also observed that ant colony optimization was the algorithm that performed the best in terms of hidden unit saturation

Novel Computationally Intelligent Machine Learning Algorithms for Data Mining and Knowledge Discovery

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of features in real time, we present an improved hybrid algorithm: SAGA. SAGA combines the ability to avoid being trapped in local minima of Simulated Annealing with the very high convergence rate of the crossover operator of Genetic Algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of generalized regression neural networks (GRNN). For imputing missing values and forecasting univariate time series, we propose a homogeneous neural network ensemble. The proposed ensemble consists of a committee of Generalized Regression Neural Networks (GRNNs) trained on different subsets of features generated by SAGA and the predictions of base classifiers are combined by a fusion rule. This approach makes it possible to discover all important interrelations between the values of the target variable and the input features. The proposed ensemble scheme has two innovative features which make it stand out amongst ensemble learning algorithms: (1) the ensemble makeup is optimized automatically by SAGA; and (2) GRNN is used for both base classifiers and the top level combiner classifier. Because of GRNN, the proposed ensemble is a dynamic weighting scheme. This is in contrast to the existing ensemble approaches which belong to the simple voting and static weighting strategy. The basic idea of the dynamic weighting procedure is to give a higher reliability weight to those scenarios that are similar to the new ones. The simulation results demonstrate the validity of the proposed ensemble model