Applications of Evolutionary and Swarm Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (15 September 2023) | Viewed by 3948

Special Issue Editor


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Guest Editor
School of Computing & Mathematical Sciences, Faculty of Engineering and Science, University of Greenwich, London SE10 9LS, UK
Interests: swarm intelligence; evolutionary computation; tomographic reconstruction; computational creativity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Swarm intelligence (SI) and evolutionary computation (EC) techniques are thriving research topics, especially in areas in which conventional methods fail to deal with the size and nature of the problem space.

The self-organizing nature of swarm intelligence and evolutionary computation in both nature and computational models is key to the attractiveness of such techniques; they not only explain and reflect on the natural-and-social phenomena but also their application to solve complex problems in many disciplines.

Additionally, noisy environments and/or incomplete data are often at the heart of real-world data where search- and optimization-related problems are amongst the core issues. Ever since the inception of SI and EC techniques, researchers have been attracted to the complex emergent behavior, robustness, and easy-to-understand architecture of nature-inspired swarm intelligence algorithms. In challenging search environments, these methods have often proved more useful than the conventional approaches.

The aim of this Special Issue is to facilitate the discussion of emerging topics in this context; PhD students, early-career researchers, and senior academics are encouraged to engage in a dialogue surrounding the applications and theories based on swarm intelligence and evolutionary computation techniques.

Topics of interest for this symposium include but are not limited to:

  • applied and theoretical research in swarm intelligence and evolutionary computation;
  • applications of swarm intelligence and evolutionary computation techniques for real-world problems;
  • studies on the behavior of social insects, social animals, and natural phenomena in the context of swarm intelligence and evolutionary computation techniques.

Dr. Mohammad Majid al-Rifaie
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net 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

  • large-scale optimization
  • multi-objective optimization
  • hyperheuristics
  • complex systems
  • premature convergence
  • stagnation
  • particle swarm optimization
  • differential evolution
  • genetic algorithms
  • dispersive fly optimization
  • ant-colony optimization

Published Papers (2 papers)

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20 pages, 1684 KiB  
Article
Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization
by Petr Kadlec
Algorithms 2023, 16(6), 307; https://doi.org/10.3390/a16060307 - 20 Jun 2023
Viewed by 1314
Abstract
This paper aims to solve the space robot pathfinding problem, formulated as a multi-objective (MO) optimization problem with a variable number of dimensions (VND). This formulation enables the search and comparison of potential solutions with different model complexities within a single optimization run. [...] Read more.
This paper aims to solve the space robot pathfinding problem, formulated as a multi-objective (MO) optimization problem with a variable number of dimensions (VND). This formulation enables the search and comparison of potential solutions with different model complexities within a single optimization run. A novel VND MO algorithm based on the well-known particle swarm optimization (PSO) algorithm is introduced and thoroughly described in this paper. The novel VNDMOPSO algorithm is validated on a set of 21 benchmark problems with different dimensionality settings and compared with two other state-of-the-art VND MO algorithms. Then, it is applied to solve five different instances of the space robot pathfinding problem formulated as a VND MO problem where two objectives are considered: (1) the minimal distance of the selected path, and (2) the minimal energy cost (expressed as the number of turning points). VNDMOPSO shows at least comparable or better convergence on the benchmark problems and significantly better convergence properties on the VND pathfinding problems compared with other VND MO algorithms. Full article
(This article belongs to the Special Issue Applications of Evolutionary and Swarm Systems)
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20 pages, 1033 KiB  
Article
Modeling Firm Search and Innovation Trajectory Using Swarm Intelligence
by Ren-Raw Chen, Cameron D. Miller and Puay Khoon Toh
Algorithms 2023, 16(2), 72; https://doi.org/10.3390/a16020072 - 22 Jan 2023
Cited by 2 | Viewed by 1581
Abstract
We developed a swarm intelligence-based model to study firm search across innovation topics. Firm search modeling has primarily been “firm-centric,” emphasizing the firm’s own prior performance. Fields interested in firm search behavior—strategic management, organization science, and economics—lack a suitable simulation model to incorporate [...] Read more.
We developed a swarm intelligence-based model to study firm search across innovation topics. Firm search modeling has primarily been “firm-centric,” emphasizing the firm’s own prior performance. Fields interested in firm search behavior—strategic management, organization science, and economics—lack a suitable simulation model to incorporate a more robust set of influences, such as the influence of competitors. We developed a swarm intelligence-based simulation model to fill this gap. To demonstrate how to fit the model to real world data, we applied latent Dirichlet allocation to patent abstracts to derive a topic search space and then provide equations to calibrate the model’s parameters. We are the first to develop a swarm intelligence-based application to study firm search and innovation. The model and data methodology can be extended to address a number of questions related to firm search and competitive dynamics. Full article
(This article belongs to the Special Issue Applications of Evolutionary and Swarm Systems)
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