REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit

The R-package REPPlab is designed to explore multivariate data sets using one-dimensional unsupervised projection pursuit. It is useful as a preprocessing step to find clusters or as an outlier detection tool for multivariate data. Except from the packages tourr and rggobi, there is no implementation of exploratory projection pursuit tools available in R. REPPlab is an R interface for the Java program EPP-lab that implements four projection indices and three biologically inspired optimization algorithms. It also proposes new tools for plotting and combining the results and specific tools for outlier detection. The functionality of the package is illustrated through some simulations and using some real data

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Gentelligent processes in biologically inspired manufacturing

Production systems have to meet quality requirements despite increasing product individuality, varying batch sizes and a scarcity of resources. The transfer of experience-based knowledge in a flexible and self-optimizing production and process planning offers the potential to meet these challenges. Biological systems solve conceptually similar challenges pertaining to the transfer of knowledge, flexibility of individual reactions and adaptation over time. Thus, in the context of digital transformation, mechanisms derived from biology are interpreted and applied to the knowledge domain of production technology. To be able to exploit the potential of bio-inspired production systems, genetic and intelligent properties of technical components and machines were identified and brought together under the concept of “Gentelligence”. Expanding upon this concept with the new idea of process-DNA and biologically inspired optimization algorithms facilitates a more flexible, learning and self-optimizing production, which is shown in three different applications. By using the new concept of gentelligent process planning it is possible to determine machine-specific process parameters in turning processes in order to ensure appropriate roughness within the requirements. Furthermore, the combination of the concept with a material removal simulation allows the determination of the resulting process force in tool grinding for subsequent unknown workpiece geometries. As a result of using the process-DNA, a workpiece-independent knowledge transfer and thus process adaptation for shape error compensation becomes possible. Gentelligent production scheduling enables a process-parallel, holistically optimized machine allocation, and as a result, a significantly reduced lead time. © 2020 The Author

Comparison of particle swarm and ant colony optimization in wireless sensor network routing

Wireless Sensor Networks (WSNs) represent an indispensable means for data acquisition from distributed devices within Industry 4.0. WSN consists of sensor nodes placed randomly in a plant. As a rule, their allocation does not provide the possibility for direct transmission of data between two nodes, and multi-hop data transfer is necessary. WSN energy efficiency is an important issue and to address it the communication effort should be minimized. One of the most effective ways to achieve this is routing, i.e. the process of finding the optimal path from the transmitting to the receiving node. In this paper, we compare four different routing algorithms, based on particle swarm and ant colony optimization

Comparison of particle swarm and ant colony optimization in wireless sensor network routing

Wireless Sensor Networks (WSNs) represent an indispensable means for data acquisition from distributed devices within Industry 4.0. WSN consists of sensor nodes placed randomly in a plant. As a rule, their allocation does not provide the possibility for direct transmission of data between two nodes, and multi-hop data transfer is necessary. WSN energy efficiency is an important issue and to address it the communication effort should be minimized. One of the most effective ways to achieve this is routing, i.e. the process of finding the optimal path from the transmitting to the receiving node. In this paper, we compare four different routing algorithms, based on particle swarm and ant colony optimization

Optimización inspirada en la naturaleza y en la biología: Lo bueno, lo malo, lo feo y lo esperanzador

Nowadays, optimization has become an important issue for industrial systems and product development. From an engineering perspective, optimization implies adjusting or fine-tuning system designs considering one or more performance factors. Unfortunately, for many complex problems there is no optimization technique that can achieve the optimum solution in a reasonable computation time. As a result, the optimization process is often done manually. In recent years a myriad of optimization techniques have appeared, all inspired by phenomena observed in nature, such as behavioral patterns in animals (such as the exploration and search for food, moving, hunting, …), physical and chemical processes [1]. These techniques, often referred to as nature- or bio-inspired optimization algorithms, allow users to optimize a problem without requiring special knowledge about it: they only need to be informed about the fitness function to be optimized, and the mechanisms by which new candidate solutions can be produced. Each algorithm defines how existing solutions can be combined and modified to create new ones in an intelligent way to search for the best solution. Although they cannot guarantee that the optimum solution will be eventually achieved, they can automatically yield good solutions in reasonable computation times. These features make bio-inspired optimization proposals a promising research area and a great alternative to optimize complex processes, as has been already showcased in many real-world problems. In this work we present nature- and bio-inspired optimization from a global perspective. We describe techniques falling in this area, their evolution, how they operate, and why they bridge an important gap not covered by previous optimization techniques. On a critical note, we also give a clear view of the current situation in the area, indicating the positive aspects and issues that should be urgently improved. Considering this critical view, we suggest promising trends that we believe will lead us to a brighter future in nature- and bio-inspired optimization, plenty of successful examples of their application to real-world engineering problems. The manuscript is structured as follows: Section 2 describes bio-inspired optimization and exposes the reasons and advantages that make this area interesting from the scientific and practical points of view (focusing on introducing what they are and why they are useful). In Section 3 we examine the exciting panorama of recent applications in which nature- and bio-inspired optimization has become a central technology (the good), the upsurge of novel metaphors for the design of new proposals that do not lead to innovative solutions (the bad), and poor methodological practices that draw misleading conclusions that must be avoided in this field (the ugly). Finally, Section 4 summarizes the paper and highlights what is next to be done in the area of bio-inspired optimization (the hopeful), especially for engineering applications

Facial Skin Disease Detection using Image Processing

Busy lifestyle, modernization, increasing pollution and unhealthy diet have led to problems which people are neglecting. Not drinking enough water, stress and hormonal changes are causing problems to skin. Causes may be situational or genetic. Few skin conditions are minor while others can be life-threatening. The skin is the largest organ of the body and is composed of water, proteins, fats and minerals. Problems appear on outer layer of the skin that is epidermis. Skin diseases are considered to be the fourth most common cause of human illness. Skin diseases are observed to increase with age and were seen frequently in both men and women. Skin disorders can be temporary or permanent. Skin diseases have an impact on individual, family and social life caused by inadequate self-treatment which may also induce psychological problems. In recent years, use of computer technologies is becoming practically universal for both personal and professional issues. Facial skin problem identification and recognition has evolved to a great extent over the years. Detection of skin diseases is done using Convolution Neural Network (CNN) and image processing methods. CNN yields better performance in terms of accuracy, precision and results than the existing conventional methods. Image processing uses digital computer to process the images through an algorithm. We focus on features like skin tone, skin texture and color. We present a brief review about various facial skin problems providing more insight about the effective models and algorithms used

Hybridization of Biologically Inspired Algorithms for Discrete Optimisation Problems

In the field of Optimization Algorithms, despite the popularity of hybrid designs, not enough consideration has been given to hybridization strategies. This paper aims to raise awareness of the benefits that such a study can bring. It does this by conducting a systematic review of popular algorithms used for optimization, within the context of Combinatorial Optimization Problems. Then, a comparative analysis is performed between Hybrid and Base versions of the algorithms to demonstrate an increase in optimization performance when hybridization is employed

Bioinspired algorithms for optimizing the harmonics contents of a PWM power inverter

Este trabajo se centra en la evaluación de diferentes técnicas de algoritmos bio-inspirados, que permitan reducir la distorsión armónica (THD) de la modulación de ancho de pulso (PWM) en inversores de potencia. Se realizó un amplio estudio que identifica y desarrolla algoritmos de optimización de inspiración biológica basados principalmente en la búsqueda tabú, algoritmos genéticos, optimización por enjambre de partículas y colonia de hormigas. Los métodos de optimización bio-inspirados son usados principalmente para encontrar los mejores parámetros operacionales de un PWM aplicado a inversores de potencia. Los mejores resultados fueron obtenidos cuando la posición de pulso (Pp) se localiza cerca a la posición central (Pp=0.5) o a la simetría del pulso. Dentro de los cuatro métodos estudiados el mejor resultado se encontró usando la técnica de optimización por colonia de hormigas, debido al valor de THD más bajo encontrado e igualmente con el 5 y 7 armónico con menos impulsos (Np= 38), casi 5 veces menor que el resultado usando la técnica optimización por enjambre de partículas, logrando reducir considerablemente las pérdidas bajando la frecuencia de conmutación (4560 Hz) de los dispositivos de potencia. Los algoritmos desarrollados pueden fácilmente adaptarse a cualquier problema de optimización, solo haciendo cambios en el número de variables y en la selección (o eliminación) de los criterios y así de esta manera obtener mejores resultados en problemas complejos.This paper deals with the evaluation of different bio-inspired algorithms techniques for reduction of harmonic distortion (THD) in pulse width modulation (PWM) of power inverters. A comprehensive study is performed, that identifies and develops biologically inspired optimization algorithms based mainly on tabu search, genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO). These bio-inspired optimization methods were used to find the best operational parameters of a PWM applied to a power inverter. The best results were achieved when the pulse position is near to the middle position (Pp=0.5) or symmetry of the pulse. For the four methods analyzed he best result was obtained using ACO method based on the lowest THD content and less 5th and 7th harmonics magnitude with fewer pulses (Np=38), almost 5 times lower than the result of PSO (Np=179). It was achieved to reduce losses considerably with a reduction in the switching frequency of the power devices (4560 Hz). The algorithms developed can be easily adapted to any minimization problem, only making changes in the number of variables and selection (or elimination) criteria to obtain better results in complex problems

A Seed-based Plant Propagation Algorithm: The Feeding Station Model

The seasonal production of fruit and seeds resembles opening a feeding station, such as a restaurant agents/ customers will arrive at a certain rate and pick fruit (get served) at a certain rate following some appropriate processes. Therefore, dispersion follows the resource process. Modelling this process results in a search/ optimisation algorithm that used dispersion as an exploration tool that, if well captured, will find the optimum of a function over a given search space. This paper presents such an algorithm and tests it on non-trivial problems