scholarly journals Nature Plus Plus Inspired Computing - The Superset of Nature Inspired Computing

2020 ◽  
pp. 53-55
Author(s):  
Satish Gajawada ◽  
Hassan M. H. Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Literature Review ◽  
Programming Language ◽  
Particle Swarm Optimization Algorithm ◽  
Swarm Optimization ◽  
C Programming Language ◽  
C Programming ◽  
Nature Inspired Computing ◽  
Definition Of

The term "Nature Plus Plus Inspired Computing" is coined by us in this article. The abbreviation for this new term is "N++IC." Just like the C++ programming language is a superset of C programming language, Nature Plus Plus Inspired Computing (N++IC) field is a superset of the Nature Inspired Computing (NIC) field. We defined and introduced "Nature Plus Plus Inspired Computing Field" in this work. Several interesting opportunities in N++IC Field are shown for Artificial Intelligence Field Scientists and Students. We show a literature review of the N++IC Field after showing the definition of Nature Inspired Computing (NIC) Field. The primary purpose of publishing this innovative article is to show a new path to NIC Field Scientists so that they can come up with various innovative algorithms from scratch. As the focus of this article is to introduce N++IC to researchers across the globe, we added N++IC Field concepts to the Particle Swarm Optimization algorithm and created the "Children Cycle Riding Algorithm (CCR Algorithm)." Finally, results obtained by CCR Algorithm are shown, followed by Conclusions.

scholarly journals Deep Loving - The Friend of Deep Learning

2020 ◽  
pp. 51-53
Author(s):  
Satish Gajawada ◽  
Hassan M. H. Mustafa
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Literature Review ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Definition Of

Artificial Intelligence and Deep Learning are good fields of research. Recently, the brother of Artificial Intelligence titled "Artificial Satisfaction" was introduced in literature [10]. In this article, we coin the term “Deep Loving”. After the publication of this article, "Deep Loving" will be considered as the friend of Deep Learning. Proposing a new field is different from proposing a new algorithm. In this paper, we strongly focus on defining and introducing "Deep Loving Field" to Research Scientists across the globe. The future of the "Deep Loving" field is predicted by showing few future opportunities in this new field. The definition of Deep Learning is shown followed by a literature review of the "Deep Loving" field. The World's First Deep Loving Algorithm (WFDLA) is designed and implemented in this work by adding Deep Loving concepts to Particle Swarm Optimization Algorithm. Results obtained by WFDLA are compared with the PSO algorithm.

An effective analysis of online education model based on artificial intelligence

2020 ◽  
pp. 1-12
Author(s):  
Lihua Peng
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Online Education ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Online Course ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Resource Generation

With the development of artificial intelligence in education, online education has been recognized by the society as a new teaching method. It can make full use of the advantages of the network across regions, and make full use of the advantages of network technology to share the resources of colleges and universities, which is a promising educational method. In response to the demand of online education for learner information, this paper proposes the learner model Neighbor Mean Variation Multi-Objective Particle Swarm Optimization-Genetic Algorithm (NMVMOPSO-GA). This model includes the learner’s learning interest sub-model, the learner’s cognitive ability sub-model and the learner’s knowledge sub-model. The modelling techniques of the three sub-models are discussed separately, and their status and role in the online education system are analyzed. At the same time, for the knowledge model that reflects the learner’s learning progress and knowledge mastery, a learner knowledge sub-model constructed with Bayesian networks is proposed. The neighbor mean mutation operator is introduced to optimize the multi-objective particle swarm optimization algorithm and improve the convergence performance and stability of the multi-objective particle swarm optimization algorithm. We study the application of multi-objective particle swarm optimization algorithm in online course resource generation service. Through simulation experiments, it is verified that the multi-objective particle swarm optimization algorithm can improve the performance and stability of online course resource generation.

FLUJO ÓPTIMO DE POTENCIA UTILIZANDO ALGORITMOS EVOLUTIVOS PROGRAMACIÓN EN DIGSILENT

2009 ◽  
Vol 5 (1) ◽  
Author(s):  
Edgar A. Moreno ◽  
Víctor H. Hinojosa
Keyword(s):  
Particle Swarm Optimization ◽  
Programming Language ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
La Red

La planificación, diseño y el análisis de la operación de los sistemas de potencia requieren estudios a fin de evaluar el desempeño del sistema existente, confiabilidad, seguridad y economía. Con el objetivo de mejorar la operación en Sistemas de Suministro de Energía Eléctrica se realiza el análisis para conocer si los parámetros eléctricos (voltaje y flujos de potencia) y reservas garantizan que el servicio se brinde dentro de los estándares de calidad, confiabilidad y seguridad. Una concepción del análisis de Sistemas Eléctricos de Potencia para cumplir con este objetivo se basa en el flujo óptimo de potencia. En este trabajo se presenta la aplicación de un Algoritmo Evolutivo (Particle Swarm Optimization) al Flujo Óptimo de Potencia (activa y reactiva). El planteamiento del problema abarca restricciones en la generación de potencia activa y reactiva, capacidad de transmisión por los elementos de la red (líneas de transmisión y transformadores) y bandas de voltaje (Economía, Confiabilidad y Calidad). Se implementa un método de penalizaciones, para poder incorporar las restricciones en la función objetivo. Se realiza la programación del algoritmo evolutivo en DIgSILENT Programming Language, debido a las ventajas que posee esta plataforma para Análisis, Modelación y Simulación de Sistemas Eléctricos de Potencia.

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