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

A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification

Electrocardiogram (ECG) signals, which capture the heart's electrical activity, are used to diagnose and monitor cardiac problems. The accurate classification of ECG signals, particularly for distinguishing among various types of arrhythmias and myocardial infarctions, is crucial for the early detection and treatment of heart-related diseases. This paper proposes a novel approach based on an improved differential evolution (DE) algorithm for ECG signal classification for enhancing the performance. In the initial stages of our approach, the preprocessing step is followed by the extraction of several significant features from the ECG signals. These extracted features are then provided as inputs to an enhanced multi-layer perceptron (MLP). While MLPs are still widely used for ECG signal classification, using gradient-based training methods, the most widely used algorithm for the training process, has significant disadvantages, such as the possibility of being stuck in local optimums. This paper employs an enhanced differential evolution (DE) algorithm for the training process as one of the most effective population-based algorithms. To this end, we improved DE based on a clustering-based strategy, opposition-based learning, and a local search. Clustering-based strategies can act as crossover operators, while the goal of the opposition operator is to improve the exploration of the DE algorithm. The weights and biases found by the improved DE algorithm are then fed into six gradient-based local search algorithms. In other words, the weights found by the DE are employed as an initialization point. Therefore, we introduced six different algorithms for the training process (in terms of different local search algorithms). In an extensive set of experiments, we showed that our proposed training algorithm could provide better results than the conventional training algorithms.Comment: 44 pages, 9 figure

Advanced Modeling and Research in Hybrid Microgrid Control and Optimization

This book presents the latest solutions in fuel cell (FC) and renewable energy implementation in mobile and stationary applications. The implementation of advanced energy management and optimization strategies are detailed for fuel cell and renewable microgrids, and for the multi-FC stack architecture of FC/electric vehicles to enhance the reliability of these systems and to reduce the costs related to energy production and maintenance. Cyber-security methods based on blockchain technology to increase the resilience of FC renewable hybrid microgrids are also presented. Therefore, this book is for all readers interested in these challenging directions of research

Futures of the Study of Culture: Interdisciplinary Perspectives, Global Challenges

How can we approach possible but unknown futures of the study of culture? This volume explores this question in the context of a changing global world. The contributions in this volume discuss the necessity of significant shifts in our conceptual and epistemological frameworks. Taking into account changing institutional research settings, the authors develop pathways to future cultural research, addressing the crucial concerns of the cultural and social worlds themselves. The contributions thereby utilize contact zones within a wide range of disciplines such as cultural anthropology, sociology, cultural history, literary studies, the history of science and bioethics as well as the environmental and medical humanities. Examining emerging inter- and transdisciplinary points of reference, the volume invites scholars in the humanities and social sciences to take part in a conversation about theories, methods, and practices for the future study of culture

Futures of the Study of Culture

By exploring possible futures of the study of culture, this volume goes beyond the potentials and challenges of recent and emerging topics, theories, and methods. It unfolds interdisciplinary and international research perspectives in the context of a changing global world. The contributions utilize contact zones between disciplines to open up new directions in the study of culture, addressing crucial issues in contemporary public discourse