Multiple Cooperative Swarms for Data Clustering

Exploring a set of unlabeled data to extract the similar clusters, known as data clustering, is an appealing problem in machine learning. In other words, data clustering organizes the underlying data into different groups using a notion of similarity between patterns. A new approach to solve the data clustering problem based on multiple cooperative swarms is introduced. The proposed approach is inspired by the social swarming behavior of biological bird flocks which search for food situated in several places. The proposed approach is composed of two main phases, namely, initialization and exploitation. In the initialization phase, the aim is to distribute the search space among several swarms. That is, a part of the search space is assigned to each swarm in this phase. In the exploitation phase, each swarm searches for the center of its associated cluster while cooperating with other swarms. The search proceeds to converge to a near-optimal solution. As compared to the single swarm clustering approach, the proposed multiple cooperative swarms provide better solutions in terms of fitness function measure for the cluster centers, as the dimensionality of data and number of clusters increase. The multiple cooperative swarms clustering approach assumes that the number of clusters is known a priori. The notion of stability analysis is proposed to extract the number of clusters for the underlying data using multiple cooperative swarms. The mathematical explanations demonstrating why the proposed approach leads to more stable and robust results than those of the single swarm clustering are also provided. Application of the proposed multiple cooperative swarms clustering is considered for one of the most challenging problems in speech recognition: phoneme recognition. The proposed approach is used to decompose the recognition task into a number of subtasks or modules. Each module involves a set of similar phonemes known as a phoneme family. Basically, the goal is to obtain the best solution for phoneme families using the proposed multiple cooperative swarms clustering. The experiments using the standard TIMIT corpus indicate that using the proposed clustering approach boosts the accuracy of the modular approach for phoneme recognition considerably

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

The effects of resistance training on cognition and brain health in older adults at risk for diabetes: A pilot feasibility study

Type 2 diabetes is associated with neurocognitive deficits and increased risk for dementia, with high prevalence of diabetes occurring in old age. There are many known risk factors for diabetes, including physical inactivity, obesity, and prediabetes. Studies show that individuals who are at risk for diabetes (i.e., have one or more risk factors) already experience some brain deficits seen in diabetes. One way to combat these deficits is aerobic exercise; however, the effects of resistance exercise in this population are relatively unknown. The objectives of this thesis were to report on the current evidence of brain deficits in prediabetes, and to assess the feasibility and preliminary efficacy of resistance training to improve cognition and brain health (structure and function) in older adults at risk for diabetes. A systematic review of cross-sectional and longitudinal studies assessing brain dysfunction in prediabetes was conducted, as well as a 26-week pilot feasibility randomized controlled trial of resistance exercise among older adults at risk for diabetes (i.e., those living with prediabetes and/or obesity). The systematic review found that adults with prediabetes may experience deficits in structural connectivity, but whether deficits in brain volume and cerebrovascular health are present is somewhat inconclusive and may be due to inconsistencies across study methodologies. Results from the pilot feasibility trial found that resistance exercise, compared to balance and stretching exercise, may improve selective cognitive functions, mainly task-switching, selective attention, and response inhibition. Resistance exercise also led to less age-related decline in total brain volume, less hippocampal atrophy, and increased functional activation patterns that mimic that of younger adults and healthy older adults. When assessing feasibility, study adherence, retention, and self-reported enjoyment were high, but recruitment was shown to be challenging. As such, important recruitment recommendations for improving future trials are included in this thesis. In conclusion, resistance exercise may lead to some improvements in cognition and brain health in older adults at risk for diabetes, however a full-scale, powered RCT is needed to further explore these possible effects

Computational Methods for the Analysis of Genomic Data and Biological Processes

In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality