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Applied Sciences
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Bio-Inspired Computing and Its Applications
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Bio-Inspired Computing and Its Applications

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 (20 June 2023) | Viewed by 4249

Special Issue Editors

Dr. Viorica Rozina Chifu

E-Mail Website
Guest Editor
Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 26-28 Baritiu Street, 400027 Cluj-Napoca, Romania
Interests: bio-inspired computing; machine learning; smart environments; ontologies and semantics
Special Issues, Collections and Topics in MDPI journals
Dr. Tudor Cioara

E-Mail Website
Guest Editor
Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400027, 26-28 Baritiu street, Cluj-Napoca, Romania
Interests: blockchain; smart environments; complex distributed systems; machine learning; energy efficiency and smart grid
Special Issues, Collections and Topics in MDPI journals
Dr. Ionut Anghel

E-Mail Website
Guest Editor
Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400027, 26-28 Baritiu Street, Cluj-Napoca, Romania
Interests: ambient assistive living; adaptive systems; blockchain; decentralized distributed systems
Special Issues, Collections and Topics in MDPI journals
Dr. Cristina Bianca Pop

E-Mail Website1 Website2
Guest Editor
Computer Science Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400027, 26-28 Baritiu street, Cluj-Napoca, Romania
Interests: bio-inspired computing; smart environments; complex distributed systems; machine learning

Special Issue Information

Dear Colleagues,

Biology provides many metaphors for designing decentralized models and algorithms for the self-organization, management, and optimization of complex systems. For example, they can be inspired by how: (i) animals or insects can efficiently find food sources, (ii) the biological immune system fights against harmful pathogens, or (iii) the human neural system can efficiently make decisions by the acquisition of useful information, self-organization, reasoning, and learning.

Bio-inspired computing is a frontier research domain that deals with the development of models, techniques, and algorithms inspired by biological mechanisms and living phenomena. Its applications include but are not limited to features selection in machine learning, parameters optimization in deep neural networks, build of self-organizing systems, design of robots, adaptability, and energy efficiency in large-scale distributed systems.   

The purpose of this Special Issue is to present recent advancements in the design and development of bio-inspired computational methods and their applications in different fields such as healthcare, transport systems, logistic chains, smart grids, and smart cities, etc.

Potential topics include but are not limited to the following:

  • Smart transportation and logistic chains;
  • Decentralised coordination of swarm of robots/drones;
  • Management of smart energy grids;
  • Bio-inspired communication and blockchain technology;
  • Artificial intelligence and machine learning;
  • Industry 4.0;
  • Metaverse and AR/VR;
  • Smart cities;
  • Health care and active aging.

Dr. Viorica Rozina Chifu
Dr. Tudor Cioara
Dr. Ionut Anghel
Dr. Cristina Bianca Pop
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • bio-inspired computing
  • machine learning
  • artificial intelligence
  • descentralzied applications
  • self-organization, adaption, and evolution
  • energy efficiency
  • metaverse

Published Papers (2 papers)

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Research

20 pages, 5290 KiB  
Article
Identification of Daily Living Recurrent Behavioral Patterns Using Genetic Algorithms for Elderly Care
by Viorica Rozina Chifu, Tudor Cioara, Cristina Bianca Pop, Ionut Anghel, David Demjen and Ioan Salomie
Appl. Sci. 2022, 12(21), 11030; https://doi.org/10.3390/app122111030 - 31 Oct 2022
Cited by 1 | Viewed by 1567
Abstract
A person’s routine is a sequence of activities of daily living patterns recurrently performed. Sticking daily routines is a great tool to support the care of persons with dementia, and older adults in general, who are living in their homes, and also being [...] Read more.
A person’s routine is a sequence of activities of daily living patterns recurrently performed. Sticking daily routines is a great tool to support the care of persons with dementia, and older adults in general, who are living in their homes, and also being useful for caregivers. As state-of-the-art tools based on self-reporting are subjective and rely on a person’s memory, new tools are needed for objectively detecting such routines from the monitored data coming from wearables or smart home sensors. In this paper, we propose a solution for detecting the daily routines of a person by extracting the sequences of recurrent activities and their duration from the monitored data. A genetic algorithm is defined to extract activity patterns featuring small differences that relate to the day-to-day contextual variations that occur in a person’s daily routine. The quality of the solutions is evaluated with a probabilistic-based fitness function, while a tournament-based strategy is employed for the dynamic selection of mutation and crossover operators applied for generating the offspring. The time variability of activities of daily living is addressed using the dispersion of the values of duration of that activity around the average value. The results are showing an accuracy above 80% in detecting the routines, while the optimal values of population size and the number of generations for fitness function evolution and convergence are determined using multiple linear regression analysis. Full article
(This article belongs to the Special Issue Bio-Inspired Computing and Its Applications)
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26 pages, 3523 KiB  
Article
Modified Coral Reef Optimization Methods for Job Shop Scheduling Problems
by Chin-Shiuh Shieh, Thanh-Tuan Nguyen, Wan-Wei Lin, Dinh-Cuong Nguyen and Mong-Fong Horng
Appl. Sci. 2022, 12(19), 9867; https://doi.org/10.3390/app12199867 - 30 Sep 2022
Cited by 2 | Viewed by 1224
Abstract
The job shop scheduling problem (JSSP) is a fundamental operational research topic with numerous applications in the real world. Since the JSSP is an NP-hard (nondeterministic polynomial time) problem, approximation approaches are frequently used to rectify it. This study proposes a novel biologically-inspired [...] Read more.
The job shop scheduling problem (JSSP) is a fundamental operational research topic with numerous applications in the real world. Since the JSSP is an NP-hard (nondeterministic polynomial time) problem, approximation approaches are frequently used to rectify it. This study proposes a novel biologically-inspired metaheuristic method named Coral Reef Optimization in conjunction with two local search techniques, Simulated Annealing (SA) and Variable Neighborhood Search (VNS), with significant performance and finding-solutions speed enhancement. The two-hybrid algorithms’ performance is evaluated by solving JSSP of various sizes. The findings demonstrate that local search strategies significantly enhance the search efficiency of the two hybrid algorithms compared to the original algorithm. Furthermore, the comparison results with two other metaheuristic algorithms that also use the local search feature and five state-of-the-art algorithms found in the literature reveal the superior search capability of the two proposed hybrid algorithms. Full article
(This article belongs to the Special Issue Bio-Inspired Computing and Its Applications)
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