Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.1
Journals
Algorithms
Special Issues
Bio-Inspired Algorithms: 2nd Edition
share announcement

Bio-Inspired Algorithms: 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: 30 May 2026 | Viewed by 8607

Special Issue Editors

Prof. Dr. Sándor Szénási

E-Mail Website
Guest Editor
John von Neumann Faculty of Informatics, Óbuda University, H-1034 Budapest, Hungary
Interests: machine learning; neural networks; simulation; GPU programming
Special Issues, Collections and Topics in MDPI journals
Dr. Gábor Kertész

E-Mail Website
Guest Editor
John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary
Interests: machine learning; deep neural networks; parallel programming
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the field of applied informatics, the algorithmic-based procedural approach has distinguised itself with indisputable advantages, but it also has several limitations with respect to hard problems without exact solutions due to incomplete or imperfect information and high computation demands.

It is frequently worth looking towards biology to better understand and model solutions for complex real-world problems. Nature is a great source of inspiration for optimization methods for solving large, indeterministic, inscrutable problems for which information is lacking. Several efficient methods and method groups are based on the process of natural selection, the behavior of living creatures (or groups of living creatures), physical phenomena, or, notably, on the mechanisms of the brain.

For this Special Issue, "Bio-Inspired Algorithms: 2nd Edition", we seek original research papers about novel, bio-inspired methods, analyses of already-existing techniques, or high-level practical applications from the field of computer science or any interdisciplinary field. We welcome manuscripts discussing evolutional (Genetic Algorithms, NSGA, etc.), swarm-intelligence-based (Particle Swarm Optimization, Ant Colony Optimization, the Fireworks Algorithm, etc.), or brain-inspired computing (Neural Networks, Deep Learning, etc.) methods applied in any kind of research project (image processing, natural language processing, general optimization, physical simulations, etc.).

Prof. Dr. Sándor Szénási
Dr. Gábor Kertész
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 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

  • design and analysis of bio-inspired methods
  • application of bio-inspired methods
  • limitations of bio-inspired methods
  • ant colony optimization
  • particle swarm optimization
  • firefly algorithm
  • fireworks algorithm
  • bees algorithm
  • evolutionary algorithms
  • neural networks
  • deep learning
  • soft computing methods
  • nature-inspired heuristics

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

30 pages, 14474 KB  
Article
A Data-Driven Spatiotemporal Feature Fusion Method for Traffic Flow Prediction
by Long Li, Zhiwen Wang and Haoxu Wang
Algorithms 2026, 19(4), 314; https://doi.org/10.3390/a19040314 - 16 Apr 2026
Viewed by 196
Abstract
In response to the current severe traffic congestion issues, highly reliable traffic flow prediction serves as a fundamental prerequisite for optimizing municipal road networks and mitigating systemic vehicular congestion. Aiming to elevate the precision of short-term traffic flow prediction, this paper first addresses [...] Read more.
In response to the current severe traffic congestion issues, highly reliable traffic flow prediction serves as a fundamental prerequisite for optimizing municipal road networks and mitigating systemic vehicular congestion. Aiming to elevate the precision of short-term traffic flow prediction, this paper first addresses the low precision of the Dung Beetle Optimizer (DBO) algorithm by introducing an exponential adaptive weight in the way of position update for the ball-rolling dung beetle, along with incorporating a Cauchy–Gaussian mutation strategy. We propose the Multi-strategy improved Dung Beetle Optimizer (MDBO), which is validated using eight benchmark test functions, demonstrating that MDBO outperforms common optimization algorithms in solution accuracy. Secondly, we adopt a combined prediction model, Traffic Flow Temporal-Spatio Network (TFTSNet), which constructs spatial feature modules and temporal feature modules in parallel fusion. Finally, we achieve short-term traffic flow prediction by optimizing the TFTSNet combined prediction model using MDBO. The experiment evaluated model performance using publicly available traffic flow datasets. The results demonstrate that, compared to other state-of-the-art models, the proposed joint prediction model based on MDBO-optimized TFTSNet achieves substantial enhancements in both prediction precision and generalization capability. Root mean square error (RMSE) decreased by 8.7–35.7%, mean absolute error (MAE) decreased by 6.6–40.0%, and R2 reached 0.975, showcasing robust predictive capabilities and engineering reference value. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms: 2nd Edition)
►▼ Show Figures

Figure 1

28 pages, 8120 KB  
Article
Genetic Programming Algorithm Evolving Robust Unary Costs for Efficient Graph Cut Segmentation
by Reem M. Mostafa, Emad Mabrouk, Ahmed Ayman, Hamdy Z. Zidan and Abdelmonem M. Ibrahim
Algorithms 2026, 19(4), 256; https://doi.org/10.3390/a19040256 - 27 Mar 2026
Viewed by 414
Abstract
Accurate cell and nuclei segmentation remains challenging due to the sensitivity of classical graph-cut methods to parameter tuning. While deep learning models like U-Net offer strong performance, they require large annotated datasets and substantial GPU resources. This work presents a cost-effective alternative: a [...] Read more.
Accurate cell and nuclei segmentation remains challenging due to the sensitivity of classical graph-cut methods to parameter tuning. While deep learning models like U-Net offer strong performance, they require large annotated datasets and substantial GPU resources. This work presents a cost-effective alternative: a genetic programming (GP) framework that jointly optimizes unary cost functions and regularization parameters for graph-cut segmentation, coupled with automatic seed selection. Evaluation is conducted under two distinct protocols: (1) oracle-guided per-image optimization, establishing upper-bound performance (mean Dice 0.822, IoU 0.733), and (2) true generalization via train/test split, where expressions learned on 50 images are applied to 50 unseen images (mean Dice 0.695, IoU 0.588). The fixed-model generalization still significantly outperforms the baseline graph cut (+0.158 Dice, p<0.001). Cross-dataset validation on MoNuSeg (H&E histopathology) achieves a Dice score of 0.823 with the fixed GP model, significantly outperforming the baseline (+0.272). This result uses a single fixed model—the best-performing expression from BBBC038 training—applied in a zero-shot manner to MoNuSeg without any retraining or domain adaptation. All 100 images showed non-negative improvement under oracle optimization in the experiments. The method requires no GPU training, runs in 550 s per image for oracle search, and offers interpretable symbolic cost functions. Code and annotations are provided to ensure reproducibility. This approach offers a practical, interpretable alternative in resource-constrained biomedical imaging settings. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms: 2nd Edition)
►▼ Show Figures

Graphical abstract

33 pages, 2980 KB  
Article
Phymastichus–Hypothenemus Algorithm for Minimizing and Determining the Number of Pinned Nodes in Pinning Control of Complex Networks
by Jorge A. Lizarraga, Alberto J. Pita, Javier Ruiz-Leon, Alma Y. Alanis, Luis F. Luque-Vega, Rocío Carrasco-Navarro, Carlos Lara-Álvarez, Yehoshua Aguilar-Molina and Héctor A. Guerrero-Osuna
Algorithms 2025, 18(10), 637; https://doi.org/10.3390/a18100637 - 9 Oct 2025
Viewed by 691
Abstract
Pinning control is a key strategy for stabilizing complex networks through a limited set of nodes. However, determining the optimal number and location of pinned nodes under dynamic and structural constraints remains a computational challenge. This work proposes an improved version of the [...] Read more.
Pinning control is a key strategy for stabilizing complex networks through a limited set of nodes. However, determining the optimal number and location of pinned nodes under dynamic and structural constraints remains a computational challenge. This work proposes an improved version of the Phymastichus–Hypothenemus Algorithm—Minimized and Determinated (PHA-MD) to solve multi-constraint, hybrid optimization problems in pinning control without requiring a predefined number of control nodes. Inspired by the parasitic behavior of Phymastichus coffea on Hypothenemus hampei, the algorithm models each agent as a parasitoid capable of propagating influence across a network, inheriting node importance and dynamically expanding search dimensions through its “offspring.” Unlike its original formulation, PHA-MD integrates variable-length encoding and V-stability assessment to autonomously identify a minimal yet effective pinning set. The method was evaluated on benchmark network topologies and compared against state-of-the-art heuristic algorithms. The results show that PHA-MD consistently achieves asymptotic stability using fewer pinned nodes while maintaining energy efficiency and convergence robustness. These findings highlight the potential of biologically inspired, dimension-adaptive algorithms in solving high-dimensional, combinatorial control problems in complex dynamical systems. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms: 2nd Edition)
►▼ Show Figures

Figure 1

25 pages, 760 KB  
Article
Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
by Daniel Onwuchekwa, Omar Hekal and Roman Obermaisser
Algorithms 2025, 18(8), 456; https://doi.org/10.3390/a18080456 - 22 Jul 2025
Viewed by 1390
Abstract
This paper presents a novel metascheduling algorithm to enhance communication efficiency in off-chip time-triggered multi-processor system-on-chip (MPSoC) platforms, particularly for safety-critical applications in aerospace and automotive domains. Time-triggered communication standards such as time-sensitive networking (TSN) and TTEthernet effectively enable deterministic and reliable communication [...] Read more.
This paper presents a novel metascheduling algorithm to enhance communication efficiency in off-chip time-triggered multi-processor system-on-chip (MPSoC) platforms, particularly for safety-critical applications in aerospace and automotive domains. Time-triggered communication standards such as time-sensitive networking (TSN) and TTEthernet effectively enable deterministic and reliable communication across distributed systems, including MPSoC-based platforms connected via Ethernet. However, their dependence on static resource allocation limits adaptability under dynamic operating conditions. To address this challenge, we propose an offline metascheduling framework that generates multiple precomputed schedules corresponding to different context events. The proposed algorithm introduces a selective communication strategy that synchronizes context information exchange with key decision points, thereby minimizing unnecessary communication while maintaining global consistency and system determinism. By leveraging knowledge of context event patterns, our method facilitates coordinated schedule transitions and significantly reduces communication overhead. Experimental results show that our approach outperforms conventional scheduling techniques, achieving a communication overhead reduction ranging from 9.89 to 32.98 times compared to a two-time-unit periodic sampling strategy. This work provides a practical and certifiable solution for introducing adaptability into Ethernet-based time-triggered MPSoC systems without compromising the predictability essential for safety certification. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms: 2nd Edition)
►▼ Show Figures

Figure 1

20 pages, 1246 KB  
Article
Plane Frame Structures: Optimization and Design Solutions Clustering
by Joana S. D. Gaspar, Maria A. R. Loja and Joaquim I. Barbosa
Algorithms 2025, 18(7), 375; https://doi.org/10.3390/a18070375 - 20 Jun 2025
Viewed by 970
Abstract
This work aims to constitute a framework dataflow based on the prediction, optimization, and characterization of optimal solutions. To this purpose, a metaheuristic optimization method is used to obtain the optimal design solutions for discrete plane frame structures considering as objective function the [...] Read more.
This work aims to constitute a framework dataflow based on the prediction, optimization, and characterization of optimal solutions. To this purpose, a metaheuristic optimization method is used to obtain the optimal design solutions for discrete plane frame structures considering as objective function the minimization of their maximum resultant displacement, subjected to side and behavioral constraints. The design variables that lead to the optimal solutions are constituted into datasets which are subsequently submitted to a clustering analysis. The results obtained provide pertinent insights about the optimal solutions clusters’ ranges, giving effective support to a specific solution selection. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms: 2nd Edition)
►▼ Show Figures

Figure 1

15 pages, 356 KB  
Article
Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics
by Sándor Szénási, Gábor Légrádi and Gábor Kovács
Algorithms 2025, 18(5), 298; https://doi.org/10.3390/a18050298 - 21 May 2025
Viewed by 1604
Abstract
Machine Learning-assisted metaheuristics is a new and promising research topic, combining the advantages of both method families. Metaheuristics are widely used general problem solvers that can be fine-tuned by prior knowledge about the search space; however, this adaptation can be a very time-consuming [...] Read more.
Machine Learning-assisted metaheuristics is a new and promising research topic, combining the advantages of both method families. Metaheuristics are widely used general problem solvers that can be fine-tuned by prior knowledge about the search space; however, this adaptation can be a very time-consuming and complex task. This paper proposes a hybrid variation of the Hill Climbing method using a Machine Learning model to learn this domain-specific knowledge in advance to help determine the optimal step size of each iteration. A Deep Feedforward Neural Network was trained on the steps of thousands of Hill Climbing runs. This model was used in a novel alternating method (using traditional and Machine Learning-based steps) to predict the optimal step size for each iteration. This hybrid algorithm was compared to the already-known variants. The results show that the novel hybrid method is able to find slightly better results than the original Hill Climbing method, requiring significantly fewer fitness calculations. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms: 2nd Edition)
►▼ Show Figures

Figure 1

Other

Jump to: Research

35 pages, 2828 KB  
Systematic Review
A Systematic Review of Bio-Inspired Metaheuristic Optimization Algorithms: The Untapped Potential of Plant-Based Approaches
by Hossein Jamali, Sergiu M. Dascalu and Frederick C. Harris, Jr.
Algorithms 2025, 18(11), 686; https://doi.org/10.3390/a18110686 - 29 Oct 2025
Cited by 1 | Viewed by 2447
Abstract
Nature has evolved sophisticated optimization strategies over billions of years, yet computational algorithms inspired by plants remain remarkably underexplored. We present a comprehensive systematic review following PRISMA 2020 guidelines, analyzing 175 studies (2000–2025) of plant-inspired metaheuristic optimization algorithms and their predominantly animal-inspired counterparts. [...] Read more.
Nature has evolved sophisticated optimization strategies over billions of years, yet computational algorithms inspired by plants remain remarkably underexplored. We present a comprehensive systematic review following PRISMA 2020 guidelines, analyzing 175 studies (2000–2025) of plant-inspired metaheuristic optimization algorithms and their predominantly animal-inspired counterparts. Despite constituting only 9.7% of bio-inspired optimization literature, plant-inspired algorithms demonstrate competitive and often superior performance compared to animal-inspired approaches. Through a meta-analysis of empirical studies, we document that algorithms like Phototropic Growth and Binary Plant Rhizome Growth achieve 97% superiority on CEC2017 benchmarks and 81% accuracy on high-dimensional feature-selection tasks—significantly exceeding established animal-inspired methods like Particle Swarm Optimization and Genetic Algorithms (p < 0.05). However, our review reveals a critical gap: the majority of these algorithms lack the formal theoretical foundations of their counterparts. This paper systematically documents these theoretical deficiencies and positions them as a key area for future research. Our framework maps botanical processes to computational operators, providing structured guidance for future algorithm development. Plant-inspired approaches excel particularly in distributed optimization, resource allocation, and multi-objective problems by leveraging unique mechanisms evolved for survival in sessile, resource-limited environments. These findings establish plant-inspired approaches as a promising yet severely underexplored frontier in optimization theory, with immediate applications in sustainable computing, resilient network design, and resource-constrained artificial intelligence. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms: 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop