Introductory Review of Swarm Intelligence Techniques

With the rapid upliftment of technology, there has emerged a dire need to fine-tune or optimize certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of optimization through experimentation or simulation, for their generic problem-solving abilities and promising efficacy with the least human intervention. In recent times, the inducement of natural phenomena into algorithm design has immensely triggered the efficiency of optimization process for even complex multi-dimensional, non-continuous, non-differentiable and noisy problem search spaces. This chapter deals with the Swarm intelligence (SI) based algorithms or Swarm Optimization Algorithms, which are a subset of the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence involves the collective study of individuals and their mutual interactions leading to intelligent behavior of the swarm. The chapter presents various population-based SI algorithms, their fundamental structures along with their mathematical models.Comment: Submitted to Springe

A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

Nature employs interactive images to incorporate end users2019; awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

A Survey on Natural Inspired Computing (NIC): Algorithms and Challenges

Nature employs interactive images to incorporate end users’ awareness and implication aptitude form inspirations into statistical/algorithmic information investigation procedures. Nature-inspired Computing (NIC) is an energetic research exploration field that has appliances in various areas, like as optimization, computational intelligence, evolutionary computation, multi-objective optimization, data mining, resource management, robotics, transportation and vehicle routing. The promising playing field of NIC focal point on managing substantial, assorted and self-motivated dimensions of information all the way through the incorporation of individual opinion by means of inspiration as well as communication methods in the study practices. In addition, it is the permutation of correlated study parts together with Bio-inspired computing, Artificial Intelligence and Machine learning that revolves efficient diagnostics interested in a competent pasture of study. This article intend at given that a summary of Nature-inspired Computing, its capacity and concepts and particulars the most significant scientific study algorithms in the field

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Evolving machine learning and deep learning models using evolutionary algorithms

Despite the great success in data mining, machine learning and deep learning models are yet subject to material obstacles when tackling real-life challenges, such as feature selection, initialization sensitivity, as well as hyperparameter optimization. The prevalence of these obstacles has severely constrained conventional machine learning and deep learning methods from fulfilling their potentials. In this research, three evolving machine learning and one evolving deep learning models are proposed to eliminate above bottlenecks, i.e. improving model initialization, enhancing feature representation, as well as optimizing model configuration, respectively, through hybridization between the advanced evolutionary algorithms and the conventional ML and DL methods. Specifically, two Firefly Algorithm based evolutionary clustering models are proposed to optimize cluster centroids in K-means and overcome initialization sensitivity as well as local stagnation. Secondly, a Particle Swarm Optimization based evolving feature selection model is developed for automatic identification of the most effective feature subset and reduction of feature dimensionality for tackling classification problems. Lastly, a Grey Wolf Optimizer based evolving Convolutional Neural Network-Long Short-Term Memory method is devised for automatic generation of the optimal topological and learning configurations for Convolutional Neural Network-Long Short-Term Memory networks to undertake multivariate time series prediction problems. Moreover, a variety of tailored search strategies are proposed to eliminate the intrinsic limitations embedded in the search mechanisms of the three employed evolutionary algorithms, i.e. the dictation of the global best signal in Particle Swarm Optimization, the constraint of the diagonal movement in Firefly Algorithm, as well as the acute contraction of search territory in Grey Wolf Optimizer, respectively. The remedy strategies include the diversification of guiding signals, the adaptive nonlinear search parameters, the hybrid position updating mechanisms, as well as the enhancement of population leaders. As such, the enhanced Particle Swarm Optimization, Firefly Algorithm, and Grey Wolf Optimizer variants are more likely to attain global optimality on complex search landscapes embedded in data mining problems, owing to the elevated search diversity as well as the achievement of advanced trade-offs between exploration and exploitation

A Study of Psychiatric Morbidity and Stressful Life Events in Psoriasis.

Introduction: Psychosomatic medicine emphasizes the unity of mind and body and the Interaction between them. The general notion is that psychological factors are Important in the development of all diseases. The history of psychosomatic Medicine has its roots in ancient beliefs that the body can be affected by External forces. For example, in Bible (Isamuel 5:4-38) ‘Nabal’ has been Regarded the first recorded psychosomatic death from myocardial infarction. ‘Walter canon’ in the early part of the twentieth century conducted first Systematic study of the relation of stress to disease. Harold Wolft, Hans selye, Helen Deutsch, Adolph Mayer, Leven Eisenberg were among the few who Contributed much to the psychosomatic medicine. According to the contemporary psychiatric research, Mind or body Responds not only to biological factors but also to the social factors. Psychosomatic medicine includes a wide range of diseases, but the most Common diseases include those affecting the gastro intestinal, respiratory, Endocrine and cutaneous system. A relationship between dermatological conditions and psychological Factors has long been observed. It has been estimated that approximately a Third of the patients presenting with dermatological disorders have some Psychological comorbidity (Rostenberg, 1960). Several possible mechanisms Explain this association. The dermis and brain share a common embryological Origin. Also, because the skin is exposed to view, dermatological conditions That affect appearance may elicit reaction from other people that has impact on The sufferer (Van moffaert, 1992). The term psychocutaneous disorder describes several distinct psychiatric Disorders in which the skin is affected. Koblenzer (1999) has proposed that These conditions can be loosely grouped into: Psychiatric disorders in which the skin is the focus of symptoms. Dermatological disorders in which psychological distress Contributes to the degree of severity. Psychiatric disorders secondary to chronic dermatological or Disfiguring conditions. SKIN DISORDERS Among the various psychosomatic diseases, skin diseases are the most Important because of the following factors I) Skin does more than presenting one’s face to the world. As our most Ancient interface, skin retains the ability to respond to both Exogenous and endogenous stimuli, sensing and integrating Environmental cues while transmitting intrinsic condition to the Outside world (Richard 1988). Ii) Sulzberger (1983) stated that of all illnesses, skin diseases affect the Mind the most and can be a great handicap in work and social Settings. The recognition and management of psychological factors Have become part of dermatological practice because of the complex Interaction between the skin and psyche. Psoriasis Psoriasis is an inflammatory, non infectious proliferative disease of the Skin characterized by chronic well defined scaly plaques, predominantly on the Extensor aspect of the body and scalp. Incidence may range from 0.1-2.8% (Ginsburg, 1989). The disease can virtually affect any age group. The course of the disease Is unpredictable, but it is usually chronic with exacerbations and remissions. Exact etiology is unknown but the factors involved may be genetic, Biochemical and immunopathological ones. Precipitating factors include Trauma, infection, sunlight, drugs and emotion. Various types like Palmoplantar, psoriasis vulgaris, pustular, guttate, and elephantine and Erythrodermic varieties have been described. Treatment is mainly in the form Of topical steroids, PUVA, methotrexate and tazoretene and retinoids. Although many skin diseases produce psychological morbidity, only Psoriasis is chosen because, I) Prevalence of psychiatric morbidity is high in psoriasis and only Few Indian studies are available. (Mattoo, sharma 2001). Ii) Stressful life events may exacerbate psoriasis, acne, eczema and Urticaria. Among the above mentioned discussions, only psoriasis Has shown consistent association with stress