Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

Models, Simulations, and the Reduction of Complexity

Modern science is a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play? To address these questions which are highly relevant to scientists as well as to philosophers of science, 8 leading natural, engineering and social scientists reflect upon their modeling work, and 8 philosophers provide a commentary

Models, Simulations, and the Reduction of Complexity

Modern science is a model-building activity. But how are models contructed? How are they related to theories and data? How do they explain complex scientific phenomena, and which role do computer simulations play? To address these questions which are highly relevant to scientists as well as to philosophers of science, 8 leading natural, engineering and social scientists reflect upon their modeling work, and 8 philosophers provide a commentary

Swarm Intelligence

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence

Time experience and judgement in depression : A theory of isomorphic general relativity (TIGR)

This thesis presents studies assessing aspects of time experience and judgement in depression. It focuses on a phenomenon called time dilation, which is the perception of slow temporal flow in conscious experience. This thesis by publication explains a novel theory of time dilation in depression, called the Theory of Isomorphic General Relativity (TIGR), and elaborates this theory to propose a general framework for consciousness and cognition according to timescale. The final outcome is a dual-pronged theory of time consciousness and the experience of time dilation in depression that has the same form as Einstein’s (1920) general theory of relativity. The thesis begins with a published paper called “Duration perception versus perception duration: A proposed model for the consciously experienced moment” (Kent, 2019). This paper defines temporal flow in conscious experience in terms of an interval of time perception known as the ‘experienced moment’ (Wittmann, 2011). In this paper, I reviewed evidence for a view of time dilation in depression that is distinct from either immediate sensory integration or working memory (WM) activity. The thesis continues with a second published paper called “Time dilation and acceleration in depression” (Kent, Van Doorn, & Klein, 2019) that reviews the literature specific to time perception in depression, and meta-analytically tests the preceding definition of time dilation within the experienced moment. This paper also details the experimental methodology used and proposes the TIGR as a descriptive and explanatory theory of time perception. xx The third published paper, “Bayes, time perception, and relativity: The central role of hopelessness” (Kent, Van Doorn, Hohwy, & Klein, 2019), formulates and tests the TIGR in a time perception experiment using the methodology outlined in the second paper. The time judgement and experience data of 64 participants, with and without sub-clinical symptoms of depression, were analysed using a statistical version of a Bayesian prediction error minimisation framework called ‘distrusting the present’ (Hohwy, Paton, & Palmer, 2016). The results showed that hopelessness was associated with slower time experience, while arousal was associated with faster time experience. The paper also supported the use of a relative difference equation to model these effects. This relative difference equation has the same general form as a basic general relativity equation used to calculate time dilation due to gravity, called the Schwarzschild metric (Schwarzschild, 1916). The fourth paper, “Time perception in depression: A perceived delay cues feelings of hopelessness” (Kent, Van Doorn, Hohwy, & Klein, under review), is under review by the journal Acta Psychologica. It looks more closely at the experimental effect reported in the third paper to explore the clinical implications of an increase in hopelessness caused by a brief time production task. The analysis showed that a particular sub-factor of the Beck Hopelessness Scale (BHS) called ‘feelings of hopelessness’ was more affected than other facets of hopelessness (Beck, Weissman, Lester, & Trexler, 1974). The fifth paper, “Systema temporis: A time-based dimensional framework for consciousness and cognition” (Kent, Van Doorn, & Klein, under review), is currently under review by the journal Consciousness and Cognition. In this paper, we extend elements of the TIGR related to consciousness in the first four papers xxi to argue that time consciousness can be used to systematise aspects of consciousness and cognition. The paper proposes a hierarchical framework that reflects the commonly-conceived structure of memory, intelligence, and emotional intelligence. This framework integrates aspects of consciousness including experience, wakefulness, and self-consciousness. The final paper, submitted to the journal Personality and Social Psychology Review and entitled “Systema psyches: A time-based framework for consciousness, cognition and related psychological and social theories” (Kent, Van Doorn, & Klein, submitted) extends the ‘Systema Temporis’ paper to incorporate extended timeframes and theories of social cognition including personality, cognitive and moral development, and personal values. The analysis suggests that time consciousness is also a facet of collective experience and so, in framing the closing discussion around time dilation in depression, the thesis concludes that the TIGR extends beyond the narrow domain of individual psychopathology to incorporate timescales of collective memory and human evolution.Doctor of Philosoph

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'

In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems