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Swarm Intelligence and Evolutionary Algorithms for Real World Applications (2nd...
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Swarm Intelligence and Evolutionary Algorithms for Real World Applications (2nd Edition)

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 3425

Special Issue Editor

Dr. Mohammad Majid al-Rifaie

E-Mail Website
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 have been thriving research topics, especially in areas where conventional methods fail to deal with the size and nature of the problem space.

The self-organising nature of swarm intelligence and evolutionary computation in both natural and computational models is key to the attractiveness of such techniques; they not only explain and reflect natural and social phenomena but also 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 optimisation-related problems are among the core issues. Ever since the inception of SI and EC techniques, researchers have been attracted to the complex emergent behaviour, robustness, and easy-to-understand architecture of nature-inspired swarm intelligence and evolutionary algorithms. In challenging search environments, these methods have often proved more useful than conventional approaches.

This Special Issue will facilitate the discussion of emerging topics in this context and encourage PhD students, early-career researchers, and senior academics to engage in a dialogue surrounding the real-world applications of swarm intelligence and evolutionary computation techniques.

Dr. Mohammad Majid al-Rifaie
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 250 words) can be sent to the Editorial Office for assessment.

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 1800 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

  • swarm intelligence
  • evolutionary computation
  • large-scale optimisation
  • multi-objective optimisation
  • complex systems
  • hybridisation
  • premature convergence
  • stagnation
  • particle swarm optimisation
  • differential evolutionary
  • genetic algorithms
  • dispersive flies optimisation
  • ant colony optimisation

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Related Special Issue

Published Papers (6 papers)

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Research

29 pages, 1839 KB  
Article
Efficient Selection of Investment Portfolios in Real-World Markets: A Multi-Objective Optimization Approach
by Antonio J. Hidalgo-Marín, Antonio J. Nebro and José García-Nieto
Algorithms 2026, 19(1), 20; https://doi.org/10.3390/a19010020 - 24 Dec 2025
Viewed by 416
Abstract
As financial markets become increasingly complex, optimizing investment portfolios under multiple conflicting objectives has become a central challenge for decision-makers. This paper presents a comprehensive benchmarking framework for multi-objective portfolio optimization based on metaheuristics, designed to operate on real-world financial data. This framework [...] Read more.
As financial markets become increasingly complex, optimizing investment portfolios under multiple conflicting objectives has become a central challenge for decision-makers. This paper presents a comprehensive benchmarking framework for multi-objective portfolio optimization based on metaheuristics, designed to operate on real-world financial data. This framework integrates preprocessing, and optimization using four state-of-the-art algorithms: NSGA-II, MOEA/D, SMS-EMOA, and SMPSO. Using historical data from over 11,000 assets listed on U.S. exchanges, including ARCA, NYSE, NASDAQ, OTC, AMEX, and BATS, we define a suite of benchmark scenarios with increasing dimensionality and constraint complexity. Our results highlight algorithmic strengths and limitations, reveal significant trade-offs between return and risk, and demonstrate the effectiveness of multi-objective metaheuristics in constructing diversified, high-performance investment portfolios. Each portfolio is encoded as a real-valued vector combining asset selection and allocation, enabling fine-grained diversification control. All datasets and source code are publicly available to ensure reproducibility. Full article
(This article belongs to the Special Issue Swarm Intelligence and Evolutionary Algorithms for Real World Applications (2nd Edition))
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18 pages, 475 KB  
Article
RAMA: A Meta-Algorithmic Framework for Ramanujan-Style Heuristic Discovery Using Large Language Models
by Jordi Vallverdú
Algorithms 2026, 19(1), 7; https://doi.org/10.3390/a19010007 - 21 Dec 2025
Viewed by 699
Abstract
This work introduces RAMA (Recursive Aesthetic Modular Approximation), a metaheuristic framework that models a restricted form of mathematical intuition inspired by the notebooks of Srinivasa Ramanujan. While Ramanujan often produced deep results without formal proofs, the heuristic processes guiding such discoveries remain poorly [...] Read more.
This work introduces RAMA (Recursive Aesthetic Modular Approximation), a metaheuristic framework that models a restricted form of mathematical intuition inspired by the notebooks of Srinivasa Ramanujan. While Ramanujan often produced deep results without formal proofs, the heuristic processes guiding such discoveries remain poorly understood. RAMA treats large language models (LLMs) as proposal mechanisms within an iterative search that generates, evaluates, and refines candidate conjectures under an explicit energy functional balancing fit, description length, and aesthetic structure. A small set of Ramanujan-inspired heuristics—modular symmetries, integrality cues, aesthetic compression, and near-invariance detection—is formalized as micro-operators acting on symbolic states. We instantiate RAMA in two domains: (i) inverse engineering eta-quotients from partial q-series data and (ii) designing cyclotomic fingerprints with shadow gadgets for quantum circuits. In both settings, RAMA recovers compact structures from limited information and improves separation from classical baselines, illustrating how intuitive heuristic patterns can be rendered as explicit, reproducible computational procedures. Full article
(This article belongs to the Special Issue Swarm Intelligence and Evolutionary Algorithms for Real World Applications (2nd Edition))
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24 pages, 575 KB  
Article
Sensitivity-Constrained Evolutionary Feature Selection for Imbalanced Medical Classification: A Case Study on Rotator Cuff Tear Surgery Prediction
by José María Belmonte, Fernando Jiménez, Gracia Sánchez, Santiago Gabardo, Natalia Martínez-Catalán, Emilio Calvo, Gregorio Bernabé and José Manuel García
Algorithms 2025, 18(12), 774; https://doi.org/10.3390/a18120774 - 8 Dec 2025
Viewed by 348
Abstract
While most patients with degenerative rotator cuff tears respond to conservative treatment, a minority progress to surgery. To anticipate these cases under class imbalance, we propose a sensitivity-constrained evolutionary feature selection framework prioritizing surgical-class recall, benchmarked against traditional methods. Two variants are proposed: [...] Read more.
While most patients with degenerative rotator cuff tears respond to conservative treatment, a minority progress to surgery. To anticipate these cases under class imbalance, we propose a sensitivity-constrained evolutionary feature selection framework prioritizing surgical-class recall, benchmarked against traditional methods. Two variants are proposed: (i) a single-objective search maximizing balanced accuracy and (ii) a multi-objective search also minimizing the number of selected features. Both enforce a minimum-sensitivity constraint on the minority class to limit false negatives. The dataset includes 347 patients (66 surgical, 19%) described by 28 clinical, imaging, symptom, and functional variables. We compare against 62 widely adopted pipelines, including oversampling, undersampling, hybrid resampling, cost-sensitive classifiers, and imbalance-aware ensembles. The main metric is balanced accuracy, with surgical-class F1-score as secondary. Pairwise Wilcoxon tests with a win–loss ranking assessed statistical significance. Evolutionary models rank among the top; the multi-objective variant with a Balanced Bagging Classifier performs best, achieving a mean balanced accuracy of 0.741. Selected subsets recurrently include age, tear location/severity, comorbidities, and pain/functional scores, matching clinical expectations. The constraint preserved minority-class recall without discarding or synthesizing data. Sensitivity-constrained evolutionary feature selection thus offers a data-preserving, interpretable solution for pre-surgical decision support, improving balanced performance and supporting safer triage decisions. Full article
(This article belongs to the Special Issue Swarm Intelligence and Evolutionary Algorithms for Real World Applications (2nd Edition))
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19 pages, 796 KB  
Article
The ACO-BmTSP to Distribute Meals Among the Elderly
by Sílvia de Castro Pereira, Eduardo J. Solteiro Pires and Paulo B. de Moura Oliveira
Algorithms 2025, 18(10), 667; https://doi.org/10.3390/a18100667 - 21 Oct 2025
Cited by 1 | Viewed by 405
Abstract
The aging of the Portuguese population is a multifaceted challenge that requires a coordinated and comprehensive response from society. In this context, social service institutions play a fundamental role in providing aid and support to the elderly, ensuring that they can enjoy a [...] Read more.
The aging of the Portuguese population is a multifaceted challenge that requires a coordinated and comprehensive response from society. In this context, social service institutions play a fundamental role in providing aid and support to the elderly, ensuring that they can enjoy a dignified and fulfilling life even in the face of the challenges of aging. This research proposes a Balanced Multiple Traveling Salesman Problem based on the Ant Colony Optimization algorithm (ACO-BmTSP) to solve a distribution of meals problem in the municipality of Mogadouro, Portugal. The Multiple Traveling Salesman Problem (mTSP) is an NP-complete problem where m salesmen perform a shortest tour between different cities, visiting each only once. The primary purpose is to minimize the sum of all distance traveled by all salesmen keeping the tours balanced. This paper shows the results of computing obtained for three, four, and five agents with this new approach and their comparison with other approaches like the standard Particle Swarm Optimization and Ant Colony Optimization algorithms. As can be seen, the ACO-BmTSP, in addition to obtaining much more equitable paths, also achieves better results in lower total costs. In conclusion, some benchmark problems were used to evaluate the efficiency of ACO-BmTSP, and the results clearly indicate that this algorithm represents a strong alternative to be considered when the problem size involves fewer than one hundred locations. Full article
(This article belongs to the Special Issue Swarm Intelligence and Evolutionary Algorithms for Real World Applications (2nd Edition))
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21 pages, 7562 KB  
Article
An Adaptive Rapidly-Exploring Random Trees Algorithm Based on Cross-Entropy Optimization
by Duo Zhao, Qichao Tang, Lei Ma, Yongkui Sun and Jieyu Lei
Algorithms 2025, 18(10), 615; https://doi.org/10.3390/a18100615 - 29 Sep 2025
Viewed by 424
Abstract
In this paper a novel adaptive rapidly-exploring random trees algorithm based on cross-entropy optimization (CE-RRT) is proposed. We seek to provide a low-cost, fast, and effective solution for path planning of robots in various complex environments. Firstly, an adaptive sampling strategy is introduced [...] Read more.
In this paper a novel adaptive rapidly-exploring random trees algorithm based on cross-entropy optimization (CE-RRT) is proposed. We seek to provide a low-cost, fast, and effective solution for path planning of robots in various complex environments. Firstly, an adaptive sampling strategy is introduced to make the search directional. Then, an adaptive step adjustment strategy is proposed to improve the search efficiency of the algorithm. Finally, the cross-entropy algorithm is introduced to optimize redundant nodes in feasible paths and improve path quality. In order to verify the feasibility and effectiveness of the proposed algorithm, it is used to solve path planning problems in two two-dimensional environments and one three-dimensional environment. The RRT and RRT* algorithms are used as benchmarks to measure the effectiveness of the three optimization strategies. The simulation demonstrates that the proposed CE-RRT algorithm can effectively improve search efficiency and path quality. Particularly (path shortened by 26%, 22.70%, and 49.11%), the CE-RRT algorithm exhibits stronger robustness in three-dimensional environments. In addition, the proposed CE-RRT algorithm can be used to plan a reasonable path for the dual robot based on the dual Sawyer simulation platform. Full article
(This article belongs to the Special Issue Swarm Intelligence and Evolutionary Algorithms for Real World Applications (2nd Edition))
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18 pages, 393 KB  
Article
A Comparison of Energy Consumption and Quality of Solutions in Evolutionary Algorithms
by Francisco Javier Luque-Hernández, Sergio Aquino-Britez, Josefa Díaz-Álvarez and Pablo García-Sánchez
Algorithms 2025, 18(9), 593; https://doi.org/10.3390/a18090593 - 22 Sep 2025
Viewed by 656
Abstract
Evolutionary algorithms are extensively used to solve optimisation problems. However, it is important to consider and reduce their energy consumption, bearing in mind that programming languages also significantly affect energy efficiency. This research work compares the execution of four frameworks—ParadisEO (C++), ECJ (Java), [...] Read more.
Evolutionary algorithms are extensively used to solve optimisation problems. However, it is important to consider and reduce their energy consumption, bearing in mind that programming languages also significantly affect energy efficiency. This research work compares the execution of four frameworks—ParadisEO (C++), ECJ (Java), DEAPand Inspyred (Python)—running on two different architectures: a laptop and a server. The study follows a design that combines three population sizes (26, 210, 214 individuals) and three crossover probabilities (0.01; 0.2; 0.8) applied to four benchmarks (OneMax, Sphere, Rosenbrock and Schwefel). This work makes a relevant methodological contribution by providing a consistent implementation of the metric η=fitness/kWh. This metric has been systematically applied in four different frameworks, thereby setting up a standardized and replicable protocol for the evaluation of the energy efficiency of evolutionary algorithms. The CodeCarbon software was used to estimate energy consumption, which was measured using RAPL counters. This unified metric also indicates the algorithmic productivity. The experimental results show that the server speeds up the number of generations by a factor of approximately 2.5, but the energy consumption increases four- to sevenfold. Therefore, on average, the energy efficiency of the laptop is five times higher. The results confirm the following conclusions: the computer power does not guarantee sustainability, and population size is a key factor in balancing quality and energy. Full article
(This article belongs to the Special Issue Swarm Intelligence and Evolutionary Algorithms for Real World Applications (2nd Edition))
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