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Applied Sciences
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Exploration and Application of Swarm Intelligence and Evolutionary Computation
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Exploration and Application of Swarm Intelligence and Evolutionary Computation

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

Deadline for manuscript submissions: 20 April 2025 | Viewed by 3476

Special Issue Editors

Prof. Dr. Hugo Valadares Siqueira

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Guest Editor
Graduate Program in Electrical Engineer (PPGEE), Universidade Tecnológica Federal do Paraná (UTFPR), Ponta Grossa 81217-220, PR, Brazil
Interests: artificial intelligence; neural networks; genetic algorithm; echo state networks; extreme learning machines; bio-inspired computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fernando Buarque

E-Mail Website
Guest Editor
Graduate Program in Computer Engineering, Polytechnic School of Pernambuco, University of Pernambuco, Recife 50720-001, Brazil
Interests: artificial intelligence; swarm and evolutionary optimization; new algorithms; Net-Sci; semiotics and AI; AI for compliance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andries P. Engelbrecht

E-Mail Website
Guest Editor
Division of Computer Science, Stellenbosch University, Stellenbosch 7600, South Africa
Interests: artificial intelligence; swarm and evolutionary optimization; hyper-heuristics; data analytics; machine learning; neural networks

Special Issue Information

Dear Colleagues,

This Special Issue aims to collect high-quality original research papers and reviews in the field of Swarm Intelligence and Evolutionary Computation. We encourage researchers to contribute with their latest developments, or to invite relevant experts and colleagues to do so. New and improved versions of well-sedimented algorithms are welcome, as well as applications to real-word problems. The authors should provide a comprehensive and scientifically sound overview of the most recent research and methodological approaches. Both experimental and methodological contributions are welcome.

In this regard, this Special Issue aims to encourage both academic and industrial researchers to present their latest findings concerning the previously cited aspects, which can significantly contribute to the achievement of new methods to develop algorithms, processes and devices.

The Editors of this Special Issue welcome submissions that address issues including, but not limited to:

  • Swarm intelligence;
  • Evolutionary algorithms;
  • Nature-inspired algorithms;
  • Combinatorial optimization;
  • Binary optimization;
  • Real-world applications;
  • Feature selection;
  • Clustering;
  • Classification;
  • Reinforced learning;
  • Time series forecasting.

Prof. Dr. Hugo Valadares Siqueira
Prof. Dr. Fernando Buarque
Prof. Dr. Andries P. Engelbrecht
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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

29 pages, 4645 KiB  
Article
Carbon Emission Analysis of Low-Carbon Technology Coupled with a Regional Integrated Energy System Considering Carbon-Peaking Targets
by Yipu Zeng, Yiru Dai, Yiming Shu and Ting Yin
Appl. Sci. 2024, 14(18), 8277; https://doi.org/10.3390/app14188277 - 13 Sep 2024
Viewed by 714
Abstract
Analyzing the carbon emission behavior of a regional integrated energy system (RIES) is crucial for aligning with carbon-peaking development strategies and ensuring compliance with carbon-peaking implementation pathways. This study focuses on a building cluster area in Shanghai, China, aiming to provide a comprehensive [...] Read more.
Analyzing the carbon emission behavior of a regional integrated energy system (RIES) is crucial for aligning with carbon-peaking development strategies and ensuring compliance with carbon-peaking implementation pathways. This study focuses on a building cluster area in Shanghai, China, aiming to provide a comprehensive analysis from both macro and micro perspectives. From a macro viewpoint, an extended STIRPAT model, incorporating the environmental Kuznets curve, is proposed to predict the carbon-peaking trajectory in Shanghai. This approach yields carbon-peaking implementation pathways for three scenarios: rapid development, stable development, and green development, spanning the period of 2020–2040. At a micro scale, three distinct RIES system configurations—fossil, hybrid, and clean—are formulated based on the renewable energy penetration level. Utilizing a multi-objective optimization model, this study explores the carbon emission behavior of a RIES while adhering to carbon-peaking constraints. Four scenarios of carbon emission reduction policies are implemented, leveraging green certificates and carbon-trading mechanisms. Performance indicators, including carbon emissions, carbon intensity, and marginal emission reduction cost, are employed to scrutinize the carbon emission behavior of the cross-regional integrated energy system within the confines of carbon peaking. Full article
(This article belongs to the Special Issue Exploration and Application of Swarm Intelligence and Evolutionary Computation)
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17 pages, 5960 KiB  
Article
A Division-of-Labour Approach to Traffic Light Scheduling
by Hendrik Raubenheimer and Andries Engelbrecht
Appl. Sci. 2024, 14(17), 8022; https://doi.org/10.3390/app14178022 - 7 Sep 2024
Viewed by 748
Abstract
Traffic light scheduling is a critical aspect of traffic management with many recently developed solutions that incorporate computational intelligence approaches. This paper presents a traffic light scheduling algorithm based on a task allocation model that simulates the division of labour among insects in [...] Read more.
Traffic light scheduling is a critical aspect of traffic management with many recently developed solutions that incorporate computational intelligence approaches. This paper presents a traffic light scheduling algorithm based on a task allocation model that simulates the division of labour among insects in a colony, specifically ant colonies. The developed algorithm switches the green light based on a probability calculated every second from the traffic volume around the traffic light. The application of this algorithm to several benchmark simulated traffic scenarios shows optimal performance compared to five other traffic scheduling algorithms. Full article
(This article belongs to the Special Issue Exploration and Application of Swarm Intelligence and Evolutionary Computation)
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23 pages, 23949 KiB  
Article
A Metaheuristic Framework with Experience Reuse for Dynamic Multi-Objective Big Data Optimization
by Xuanyu Zheng, Changsheng Zhang, Yang An and Bin Zhang
Appl. Sci. 2024, 14(11), 4878; https://doi.org/10.3390/app14114878 - 4 Jun 2024
Viewed by 1128
Abstract
Dynamic multi-objective big data optimization problems (DMBDOPs) are challenging because of the difficulty of dealing with large-scale decision variables and continuous problem changes. In contrast to classical multi-objective optimization problems, DMBDOPs are still not intensively explored by researchers in the optimization field. At [...] Read more.
Dynamic multi-objective big data optimization problems (DMBDOPs) are challenging because of the difficulty of dealing with large-scale decision variables and continuous problem changes. In contrast to classical multi-objective optimization problems, DMBDOPs are still not intensively explored by researchers in the optimization field. At the same time, there is lacking a software framework to provide algorithmic examples to solve DMBDOPs and categorize benchmarks for relevant studies. This paper presents a metaheuristic software framework for DMBDOPs to remedy these issues. The proposed framework has a lightweight architecture and a decoupled design between modules, ensuring that the framework is easy to use and has enough flexibility to be extended and modified. Specifically, the framework now integrates four basic dynamic metaheuristic algorithms, eight test suites of different types of optimization problems, as well as some performance indicators and data visualization tools. In addition, we have proposed an experience reuse method, speeding up the algorithm’s convergence. Moreover, we have implemented parallel computing with Apache Spark to enhance computing efficiency. In the experiments, algorithms integrated into the framework are tested on the test suites for DMBDOPs on an Apache Hadoop cluster with three nodes. The experience reuse method is compared to two restart strategies for dynamic metaheuristics. Full article
(This article belongs to the Special Issue Exploration and Application of Swarm Intelligence and Evolutionary Computation)
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