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Algorithms
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Nature-Inspired Algorithms—Advances in Theory, Methods, Applications, and Reviews
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Nature-Inspired Algorithms—Advances in Theory, Methods, Applications, and Reviews

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 18377

Special Issue Editors

Prof. Dr. Thomas Hanne

E-Mail Website
Guest Editor
Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, 4142 Münchenstein, Switzerland
Interests: computational intelligence; optimization simulation; multicriteria decision analysis; logistics and supply chain management; evolutionary computation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Danilo Pelusi

E-Mail Website
Guest Editor
Department of Computer Science, University of Teramo, 64100 Teramo, Italy
Interests: fuzzy logic; machine learning; evolutionary algorithms; computational intelligence; information theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metaheuristic techniques have become very popular. This feature is due to several reasons: the gradient-free mechanism, flexibility, and premature convergence avoidance of these algorithms. Generally, nature-inspired algorithms consider optimization problems as a black box. Therefore, it is not necessary to calculate derivatives of the search space. This fact makes nature-inspired algorithms highly flexible when it comes to solving various kinds of problems. On the other hand, optimization techniques are employed to find several optimal values to generate a candidate solution which is able to solve a given problem effectively. The target is to find the optimal value among several available alternatives. The final result of the optimization process is the choice of the best value from all the given options. In literature, nature-inspired algorithms are classified into two classes: evolutionary and swarm intelligence techniques. Among well-known Evolutionary Algorithms, there are Genetic Algorithms (GA), Differential Evolution (DE), Evolution Strategies (ES), and Evolutionary Programming (EP). The most famous swarm intelligent algorithms are Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).

We would like to announce a Special Issue entitled “Nature-Inspired Algorithms—Advances in Theory, Methods, Applications, and Reviews” to be published in the MDPI journal Algorithms. This Special Issue focusses on nature-inspired algorithms for search and optimization, such as methods from swarm intelligence, evolutionary computation, and related areas. We invite papers dealing with such methods that provide original results relating to the theoretical analysis and understanding of such algorithms, which suggest new advances of the algorithms, or which deal with related applications. In addition, we invite comprehensive surveys with interesting food for thought to enhance the state of the art in this area significantly. We encourage authors across the world to submit their original and unpublished works. We have a special interest in works focusing on the topics listed below, but we are open to other works that fit the theme of the Special Issue.

Prof. Dr. Thomas Hanne
Dr. Danilo Pelusi
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • Nature-inspired algorithms
  • Metaheuristics
  • Swarm intelligence
  • Evolutionary computation
  • Ant colony optimization
  • Particle swarm optimization

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Published Papers (4 papers)

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Research

13 pages, 3284 KiB  
Article
Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms
by Deepaa Selva, Balakrishnan Nagaraj, Danil Pelusi, Rajendran Arunkumar and Ajay Nair
Algorithms 2021, 14(8), 224; https://doi.org/10.3390/a14080224 - 26 Jul 2021
Cited by 90 | Viewed by 3925
Abstract
Rapid Internet use growth and applications of diverse military have managed researchers to develop smart systems to help applications and users achieve the facilities through the provision of required service quality in networks. Any smart technologies offer protection in interactions in dispersed locations [...] Read more.
Rapid Internet use growth and applications of diverse military have managed researchers to develop smart systems to help applications and users achieve the facilities through the provision of required service quality in networks. Any smart technologies offer protection in interactions in dispersed locations such as, e-commerce, mobile networking, telecommunications and management of network. Furthermore, this article proposed on intelligent feature selection methods and intrusion detection (ISTID) organization in webs based on neuron-genetic algorithms, intelligent software agents, genetic algorithms, particulate swarm intelligence and neural networks, rough-set. These techniques were useful to identify and prevent network intrusion to provide Internet safety and improve service value and accuracy, performance and efficiency. Furthermore, new algorithms of intelligent rules-based attributes collection algorithm for efficient function and rules-based improved vector support computer, were proposed in this article, along with a survey into the current smart techniques for intrusion detection systems. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms—Advances in Theory, Methods, Applications, and Reviews)
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21 pages, 2044 KiB  
Article
Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
by Afra A. Alabbadi and Maysoon F. Abulkhair
Algorithms 2021, 14(3), 77; https://doi.org/10.3390/a14030077 - 27 Feb 2021
Cited by 17 | Viewed by 3477
Abstract
Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial–temporal tasks during a certain period of time. The main problem in SC platforms is scheduling [...] Read more.
Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial–temporal tasks during a certain period of time. The main problem in SC platforms is scheduling a set of proper workers to achieve a set of spatial tasks based on different objectives. In actuality, real-world applications of SC need to optimize multiple objectives together, and these objectives may sometimes conflict with one another. Furthermore, there is a lack of research dealing with the multi-objective optimization (MOO) problem within an SC environment. Thus, in this work we focused on task scheduling based on multi-objective optimization (TS-MOO) in SC, which is based on maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers. To solve the previous problem, we developed a new method, i.e., the multi-objective task scheduling optimization (MOTSO) model that consists of two algorithms, namely, the multi-objective particle swarm optimization (MOPSO) algorithm with our fitness function Alabbadi, et al. and the ranking strategy algorithm based on the task entropy concept and task execution duration. The main purpose of our ranking strategy is to improve and enhance the performance of our MOPSO. The primary goal of the proposed MOTSO model is to find an optimal solution based on the multiple objectives that conflict with one another. We conducted our experiment with both synthetic and real datasets; the experimental results and statistical analysis showed that our proposed model is effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and balancing the workload between workers. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms—Advances in Theory, Methods, Applications, and Reviews)
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20 pages, 1154 KiB  
Article
A Hybrid Grasshopper Optimization Algorithm Applied to the Open Vehicle Routing Problem
by Valeria Soto-Mendoza, Irma García-Calvillo, Efraín Ruiz-y-Ruiz and Jaime Pérez-Terrazas
Algorithms 2020, 13(4), 96; https://doi.org/10.3390/a13040096 - 16 Apr 2020
Cited by 15 | Viewed by 5109
Abstract
This paper presents a hybrid grasshopper optimization algorithm using a novel decoder and local search to solve instances of the open vehicle routing problem with capacity and distance constraints. The algorithm’s decoder first defines the number of vehicles to be used and then [...] Read more.
This paper presents a hybrid grasshopper optimization algorithm using a novel decoder and local search to solve instances of the open vehicle routing problem with capacity and distance constraints. The algorithm’s decoder first defines the number of vehicles to be used and then it partitions the clients, assigning them to the available routes. The algorithm performs a local search in three neighborhoods after decoding. When a new best solution is found, every route is locally optimized by solving a traveling salesman problem, considering the depot and clients in the route. Three sets containing a total of 30 benchmark problems from the literature were used to test the algorithm. The experiments considered two cases of the problem. In the first, the primary objective is to minimize the total number of vehicles and then the total distance to be traveled. In the second case, the total distance traveled by the vehicles is minimized. The obtained results showed the algorithm’s proficient performance. For the first case, the algorithm was able to improve or match the best-known solutions for 21 of the 30 benchmark problems. For the second case, the best-known solutions for 18 of the 30 benchmark problems were found or improved by the algorithm. Finally, a case study from a real-life problem is included. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms—Advances in Theory, Methods, Applications, and Reviews)
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21 pages, 3620 KiB  
Article
Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media
by George A. Papakostas, John W. Nolan and Athanasios C. Mitropoulos
Algorithms 2020, 13(3), 65; https://doi.org/10.3390/a13030065 - 16 Mar 2020
Cited by 4 | Viewed by 4301
Abstract
One of the most challenging problems that are still open in the field of materials science is the 3D reconstruction of porous media using information from a single 2D thin image of the original material. Such a reconstruction is only feasible subject to [...] Read more.
One of the most challenging problems that are still open in the field of materials science is the 3D reconstruction of porous media using information from a single 2D thin image of the original material. Such a reconstruction is only feasible subject to some important assumptions that need to be made as far as the statistical properties of the material are concerned. In this study, the aforementioned problem is investigated as an explicitly formulated optimization problem, with the phase of each porous material point being decided such that the resulting 3D material model shows the same statistical properties as its corresponding 2D version. Based on this problem formulation, herein for the first time, several traditional (genetic algorithms—GAs, particle swarm optimization—PSO, differential evolution—DE), as well as recently proposed (firefly algorithm—FA, artificial bee colony—ABC, gravitational search algorithm—GSA) nature-inspired optimization algorithms were applied to solve the 3D reconstruction problem. These algorithms utilized a newly proposed data representation scheme that decreased the number of unknowns searched by the optimization process. The advantages of addressing the 3D reconstruction of porous media through the application of a parallel heuristic optimization algorithm were clearly defined, while appropriate experiments demonstrating the greater performance of the GA algorithm in almost all the cases by a factor between 5%–84% (porosity accuracy) and 3%–15% (auto-correlation function accuracy) over the PSO, DE, FA, ABC, and GSA algorithms were undertaken. Moreover, this study revealed that statistical functions of a high order need to be incorporated into the reconstruction procedure to increase the reconstruction accuracy. Full article
(This article belongs to the Special Issue Nature-Inspired Algorithms—Advances in Theory, Methods, Applications, and Reviews)
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