Designing Conducting Polymers Using Bioinspired Ant Algorithms

Ant algorithms are inspired in real ants and the main idea is to create virtual ants that travel into the space of possible solution depositing virtual pheromone proportional to how good a specific solution is. This creates a autocatalytic (positive feedback) process that can be used to generate automatic solutions to very difficult problems. In the present work we show that these algorithms can be used coupled to tight-binding hamiltonians to design conducting polymers with pre-specified properties. The methodology is completely general and can be used for a large number of optimization problems in materials science

Resilient Bioinspired Algorithms: A Computer System Design Perspective

This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in Cotta, C., Olague, G. (2022). Resilient Bioinspired Algorithms: A Computer System Design Perspective. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_39Resilience can be defined as a system's capability for returning to normal operation after having suffered a disruption. This notion is of the foremost interest in many areas, in particular engineering. We argue in this position paper that is is a crucial property for bioinspired optimization algorithms as well. Following a computer system perspective, we correlate some of the defining requirements for attaining resilient systems to issues, features, and mechanisms of these techniques. It is shown that bioinspired algorithms do not only exhibit a notorious built-in resilience, but that their plasticity also allows accommodating components that may boost it in different ways. We also provide some relevant research directions in this area.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Optimization and synthesis of multilayer frequency selective surfaces via bioinspired hybrid techniques

In this study, two bioinspired computation (BIC) techniques are discussed and applied to the project and synthesis of multilayer frequency selective surfaces (FSS) within the microwave band, specifically for C, X and Ku bands. The proposed BIC techniques consist of combining an artificial, general regression neural network to a genetic algorithm (GA) and a cuckoo search algorithm, respectively. The objective is to find the optimal values of separation between the investigated FSS. Numerical analysis of the electromagnetic properties of the device is made possible with the finite integration method (FIT) and validated through the finite element method (FEM), utilizing both softwares CST Microwave Studio and Ansys HFSS respectively. Thus, the BIC-optimized devices present good phase / angular stability for angles 10°, 20°, 30° and 40°, as well as being polarization independent. The cutoff frequencies to control the operating frequency range of the FSS, referring to transmission coefficient in decibels (dB), were obtained at a threshold of –10dB. Numerical results denote good accordance with measured data

Modeling of Biological Intelligence for SCM System Optimization

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms

Soft Computing Techiniques for the Protein Folding Problem on High Performance Computing Architectures

The protein-folding problem has been extensively studied during the last fifty years. The understanding of the dynamics of global shape of a protein and the influence on its biological function can help us to discover new and more effective drugs to deal with diseases of pharmacological relevance. Different computational approaches have been developed by different researchers in order to foresee the threedimensional arrangement of atoms of proteins from their sequences. However, the computational complexity of this problem makes mandatory the search for new models, novel algorithmic strategies and hardware platforms that provide solutions in a reasonable time frame. We present in this revision work the past and last tendencies regarding protein folding simulations from both perspectives; hardware and software. Of particular interest to us are both the use of inexact solutions to this computationally hard problem as well as which hardware platforms have been used for running this kind of Soft Computing techniques.This work is jointly supported by the FundaciónSéneca (Agencia Regional de Ciencia y Tecnología, Región de Murcia) under grants 15290/PI/2010 and 18946/JLI/13, by the Spanish MEC and European Commission FEDER under grant with reference TEC2012-37945-C02-02 and TIN2012-31345, by the Nils Coordinated Mobility under grant 012-ABEL-CM-2014A, in part financed by the European Regional Development Fund (ERDF). We also thank NVIDIA for hardware donation within UCAM GPU educational and research centers.Ingeniería, Industria y Construcció

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