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Entropy
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Unconventional Methods for Particle Swarm Optimization II
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Unconventional Methods for Particle Swarm Optimization II

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 6856

Special Issue Editor

Prof. Dr. Leonardo Vanneschi

E-Mail Website
Guest Editor
NOVA Information Management School (NOVA IMS), Universidade Nova of Lisbon, Campus de Campolide, 1070-312 Lisboa, Portugal
Interests: machine learning; genetic programming; particle swarm optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Particle swarm optimization (PSO) is a population-based optimization metaheuristic inspired by the collective dynamics of groups of animals, like insects, birds, and fishes. Recent research trends have indicated the potentiality of the approach and its possibilities for improvement. With the term “unconventional methods for PSO”, we mean modifications of the standard PSO algorithm with the objective of improving its performance or bestowing on it some particular properties. Examples include new methods for choosing the inertia weight, constriction factor, or cognition and social weights; parallelizing PSO in several different ways; defining hybrid algorithms in which PSO is integrated with other types of metaheuristic optimization methods; and entropy-based PSO. The study of unconventional methods for PSO is a very lively and active research field, and the objective of this Special Issue is to present a collection of studies in this recent and exciting area, with a particular focus on entropic, information-theoretic, or probability-theoretic techniques.

Prof. Dr. Leonardo Vanneschi
Guest Editor

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. Entropy 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 2600 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

  • entropy-based PSO
  • information theory for PSO
  • probability theory for PSO
  • theoretically motivated hybrid PSO systems
  • theoretically motivated parallelizations of PSO
  • theoretically motivated niching
  • new acceleration strategies
  • automatic, static, or dynamic parameter setting
  • improvements and/or specializations of particle movements
  • PSO for the optimization/improvement of machine learning methods
  • real-life applications using theoretically motivated unconventional PSO systems

Published Papers (2 papers)

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Research

20 pages, 1350 KiB  
Article
A Novel Hybrid Gradient-Based Optimizer and Grey Wolf Optimizer Feature Selection Method for Human Activity Recognition Using Smartphone Sensors
by Ahmed Mohamed Helmi, Mohammed A. A. Al-qaness, Abdelghani Dahou, Robertas Damaševičius, Tomas Krilavičius  and Mohamed Abd Elaziz
Entropy 2021, 23(8), 1065; https://0-doi-org.brum.beds.ac.uk/10.3390/e23081065 - 17 Aug 2021
Cited by 42 | Viewed by 3344
Abstract
Human activity recognition (HAR) plays a vital role in different real-world applications such as in tracking elderly activities for elderly care services, in assisted living environments, smart home interactions, healthcare monitoring applications, electronic games, and various human–computer interaction (HCI) applications, and is an [...] Read more.
Human activity recognition (HAR) plays a vital role in different real-world applications such as in tracking elderly activities for elderly care services, in assisted living environments, smart home interactions, healthcare monitoring applications, electronic games, and various human–computer interaction (HCI) applications, and is an essential part of the Internet of Healthcare Things (IoHT) services. However, the high dimensionality of the collected data from these applications has the largest influence on the quality of the HAR model. Therefore, in this paper, we propose an efficient HAR system using a lightweight feature selection (FS) method to enhance the HAR classification process. The developed FS method, called GBOGWO, aims to improve the performance of the Gradient-based optimizer (GBO) algorithm by using the operators of the grey wolf optimizer (GWO). First, GBOGWO is used to select the appropriate features; then, the support vector machine (SVM) is used to classify the activities. To assess the performance of GBOGWO, extensive experiments using well-known UCI-HAR and WISDM datasets were conducted. Overall outcomes show that GBOGWO improved the classification accuracy with an average accuracy of 98%. Full article
(This article belongs to the Special Issue Unconventional Methods for Particle Swarm Optimization II)
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15 pages, 5731 KiB  
Article
A Hybrid Rao-NM Algorithm for Image Template Matching
by Xinran Liu, Zhongju Wang, Long Wang, Chao Huang and Xiong Luo
Entropy 2021, 23(6), 678; https://0-doi-org.brum.beds.ac.uk/10.3390/e23060678 - 27 May 2021
Cited by 7 | Viewed by 2799
Abstract
This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is [...] Read more.
This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems. Full article
(This article belongs to the Special Issue Unconventional Methods for Particle Swarm Optimization II)
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