Special Issue "Advances in Natural Computing: Methods and Application"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 1191

Special Issue Editor

Prof. Dr. Gaige Wang
E-Mail Website
Guest Editor
Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
Interests: swarm intelligence; natural computing; evolutionary computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One of the ever-present grand challenges and central goals of computer science is to understand the world around us in terms of information processing. Each time progress is made in achieving this goal, both the world around us and computer science benefit.

Nature is a dominating part of the world around us, and one way to understand it in terms of information processing is to study computing taking place in nature. Natural computing is concerned with this type of computing and with its main benefit for computer science, viz., human-designed computing inspired by nature.

By its very nature, the science of natural computing is genuinely interdisciplinary; therefore, natural computing forms a bridge between natural sciences and computer science. In this way, natural computing elevates computer science to an even more prominent role in the broad rainbow of scientific disciplines.

Human-designed computing inspired by nature is based on the use of paradigms, principles, and mechanisms underlying natural systems. Some disciplines of human-designed computing are relatively old and are well established by now. Well-known examples of such disciplines are evolutionary computing and neural computing. Evolutionary algorithms are based on the concepts of mutation, recombination, and natural selection from the theory of evolution, while neural networks are based on concepts originating in the study of the highly interconnected neural structures in the brain and nervous system. On the other hand, molecular computing and quantum computing are younger disciplines of natural computing: molecular computing is based on paradigms from molecular biology, while quantum computing is based on quantum physics and exploits quantum parallelism.

Natural computing refers to computational processes observed in nature, and human-designed computing inspired by nature. When complex natural phenomena are analyzed in terms of computational processes, our understanding of both the nature and essence of computation is enhanced. Characteristic for human-designed computing inspired by nature is the metaphorical use of concepts, principles, and mechanisms underlying natural systems. Natural computing includes evolutionary algorithms, neural networks, molecular computing, and quantum computing.

The purpose of this Special Issue is to gather a collection of articles reflecting the latest developments in different fields of evolutionary algorithms, neural networks, molecular computing, quantum computing and artificial immune systems, and others.

Prof. Dr. Gaige Wang
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • natural computing
  • evolutionary algorithms
  • swarm intelligence
  • neural networks
  • molecular computing
  • quantum computing
  • artificial immune systems

Published Papers (2 papers)

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Research

Article
A Comparative Study in Forming Behavior of Different Grades of Steel in Cold Forging Backward Extrusion by Integrating Artificial Neural Network (ANN) with Differential Evolution (DE) Algorithm
Appl. Sci. 2023, 13(3), 1276; https://doi.org/10.3390/app13031276 - 18 Jan 2023
Viewed by 166
Abstract
The cold forging backward extrusion is employed to produce parts that are characterized by better mechanical strength. However, in this process, punches are often prone to breakages because of the large forces encountered in deforming the steel billets. The service life of the [...] Read more.
The cold forging backward extrusion is employed to produce parts that are characterized by better mechanical strength. However, in this process, punches are often prone to breakages because of the large forces encountered in deforming the steel billets. The service life of the punches is affected majorly by the geometrical attributes, the type of steel undergoing deformation, and hence the present investigation focuses on the applications of natural computing algorithms such as artificial neural network (ANN) and differential evolution (DE) optimization algorithm to study the differential influence on the forming behavior of different grades steel and enhance the punch service life. The AISI steel grades, such as AISI 1010, 1018, and 1045, employed extensively in the production of automotive components, have been compared in terms of forming behavior, such as effective stress, strain, strain rate, and punch force. The multi-layer feed-forward ANN architecture was utilized for process modeling with forming responses of finite element (FE) simulations that are strategically planned through the design of experiments (DoE) approach. Considerable variations were found for the effective stress and punch force amongst the steels, while marginal deviations were observed for effective strain and strain rates. Confirmatory experiments were conducted to validate the results of optimal combinations obtained through the DE optimization technique, and the deviations were observed to be in the acceptable range. The cold forging backward extruded components have also been examined for better mechanical soundness through microstructure and micro-hardness analysis that clearly revealed the mechanical integrity and strength enhancement within the forged components. The proposed study would assist the industries engaged in the production of cold-forged steel components in determining the appropriate values of variables to minimize the forming responses and, thus, help in enhancing the life of the tooling. Full article
(This article belongs to the Special Issue Advances in Natural Computing: Methods and Application)
Article
Natural Computing-Based Designing of Hybrid UHMWPE Composites for Orthopedic Implants
Appl. Sci. 2022, 12(20), 10408; https://doi.org/10.3390/app122010408 - 15 Oct 2022
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Abstract
The current study deals with the design of ultra-high molecular weight polyethylene (UHMWPE) composites by integrating various micro and nanoparticles as reinforcements for enhanced performance of acetabular cups in hip prostheses. For the design, a data-driven design approach was implemented, exploiting natural computing [...] Read more.
The current study deals with the design of ultra-high molecular weight polyethylene (UHMWPE) composites by integrating various micro and nanoparticles as reinforcements for enhanced performance of acetabular cups in hip prostheses. For the design, a data-driven design approach was implemented, exploiting natural computing techniques such as Artificial Neural Network (ANN) and Genetic Algorithm (GA). Experimental data related to UHMWPE reinforced with carbon nanotube, graphene, carbon fiber, and hydroxyapatite were gathered from the published works of previous researchers. To study the relationship between the volume fraction and the morphology of the particles with the tribological and mechanical properties of the composites, ANN modeling and sensitivity analyses were used. Optimization of the properties was done with the developed ANN models as objective functions in order to find the optimal combinations of reinforcements, which helps to achieve enhanced tribo-mechanical properties of the composites. This natural computing approach of designing the UHMWPE composites paved a way for experimentation. Full article
(This article belongs to the Special Issue Advances in Natural Computing: Methods and Application)
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