Advances in Algorithms Through Heuristics: Theory, Applications, and Innovations

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 15 December 2025 | Viewed by 863

Special Issue Editor

Special Issue Information

Dear Colleagues,

We are pleased to announce a Special Issue dedicated to cutting-edge research and developments in the field of algorithms. This issue aims to gather high-quality contributions that explore novel algorithmic techniques, their theoretical foundations, and practical applications across diverse scientific and engineering domains.

This Special Issue welcomes original research articles, comprehensive reviews, and case studies focusing on, but not limited to:

  • Metaheuristic and evolutionary algorithms, including genetic algorithms for optimization and problem-solving.
  • Derivative-free iterative methods for solving nonlinear equations and root-finding problems.
  • Algorithmic approaches in chemical and instrumental analysis, including applications in quantitative structure-activity relationships (QSAR) and compound similarity assessment.Statistical and experimental design algorithms, such as error analysis in randomized and Latin square designs.
  • Algorithmic innovations in data analysis, machine learning, and computational biology.
  • Applications of algorithms in materials science, energy storage systems, and medical informatics.

Authors are invited to submit manuscripts that present original and unpublished research. Submissions will undergo rigorous peer review to ensure high scientific standards.

We welcome research articles, communications, reviews, and technical notes. We look forward to your valuable contributions to this Special Issue, which promises to advance the understanding and application of algorithms in science and engineering.

Prof. Dr. Lorentz Jäntschi
Guest Editor

Manuscript Submission Information

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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

  • heuristic algorithms
    • metaheuristics
    • algorithm design and analysis
    • optimization algorithms
    • swarm intelligence
    • hybrid algorithms
    • intelligent systems
    • bio-inspired algorithms
    • computational complexity
    • admissible heuristics
  • theoretical foundations
    • algorithmic theory
    • non-asymptotic analysis
    • search optimization problems
    • heuristic search hypothesis
    • symbolic computation
    • convergence analysis
  • applications
    • network optimization
    • resource scheduling (cloud/edge computing)
    • energy-efficient routing
    • security in software-defined networking (SDN)
    • machine learning and data mining
    • computational biology and bioinformatics
    • robotics and motion planning
    • fraud detection and cybersecurity
    • intelligent communication systems
    • dynamic spectrum allocation
    • real-time systems
  • innovations and emerging topics
    • particle swarm optimization
    • hybrid and ensemble heuristic methods
    • multi-objective optimization
    • intelligent reflecting surfaces (IRS) deployment
    • data-driven heuristics
    • parallel and distributed heuristic algorithms
    • adaptive and self-learning heuristics
    • heuristic-based modeling and simulation
  • general and cross-cutting themes
    • applications of heuristic algorithms in science and engineering
    • comparative studies of heuristic and classical algorithms
    • guidelines for developing new heuristic techniques
    • surveys of existing heuristic approaches
    • implementation and scalability of heuristics
    • performance evaluation and benchmarking

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Published Papers (2 papers)

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Research

17 pages, 267 KB  
Article
Student Surpasses the Teacher: Apprenticeship Learning for Quadratic Unconstrained Binary Optimisation
by Jack Cakebread, Warren G. Jackson, Daniel Karapetyan, Andrew J. Parkes and Ender Özcan
Algorithms 2025, 18(8), 516; https://doi.org/10.3390/a18080516 - 15 Aug 2025
Viewed by 294
Abstract
This study introduces a novel train-and-test approach referred to as apprenticeship learning (AL) for generating selection hyper-heuristics to solve the Quadratic Unconstrained Binary Optimisation (QUBO) problem. The primary goal is to automate the design of hyper-heuristics by learning from a state-of-the-art expert and [...] Read more.
This study introduces a novel train-and-test approach referred to as apprenticeship learning (AL) for generating selection hyper-heuristics to solve the Quadratic Unconstrained Binary Optimisation (QUBO) problem. The primary goal is to automate the design of hyper-heuristics by learning from a state-of-the-art expert and to evaluate whether the apprentice can outperform that expert. The proposed method collects detailed search trace data from the expert and trains the apprentice based on the machine learning models to predict heuristic selection and parameter settings. Multiple data filtering and class balancing techniques are explored to enhance model performance. The empirical results on unseen QUBO instances show that indeed, “student surpasses the teacher”; the hyper-heuristic with the generated heuristic selection not only outperforms the expert but also generalises quite well by solving unseen QUBO instances larger than the ones on which the apprentice was trained. These findings highlight the potential of AL to generalise expert behaviour and improve heuristic search performance. Full article
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17 pages, 3816 KB  
Article
Charging Station Siting and Capacity Determination Based on a Generalized Least-Cost Model of Traffic Distribution
by Mingzhao Ma, Feng Wang, Lirong Xiong, Yuhonghao Wang and Wenxin Li
Algorithms 2025, 18(8), 479; https://doi.org/10.3390/a18080479 - 4 Aug 2025
Viewed by 344
Abstract
With the popularization of electric vehicles and the continuous expansion of the electric vehicle market, the construction and management of charging facilities for electric vehicles have become important issues in research and practice. In some remote areas, the charging stations are idle due [...] Read more.
With the popularization of electric vehicles and the continuous expansion of the electric vehicle market, the construction and management of charging facilities for electric vehicles have become important issues in research and practice. In some remote areas, the charging stations are idle due to low traffic flow, resulting in a waste of resources. Areas with high traffic flow may have fewer charging stations, resulting in long queues and road congestion. The purpose of this study is to optimize the location of charging stations and the number of charging piles in the stations based on the distribution of traffic flow, and to construct a bi-level programming model by analyzing the distribution of traffic flow. The upper-level planning model is the user-balanced flow allocation model, which is solved to obtain the optimal traffic flow allocation of the road network, and the output of the upper-level planning model is used as the input of the lower-layer model. The lower-level planning model is a generalized minimum cost model with driving time, charging waiting time, charging time, and the cost of electricity consumed to reach the destination of the trip as objective functions. In this study, an empirical simulation is conducted on the road network of Hefei City, Anhui Province, utilizing three algorithms—GA, GWO, and PSO—for optimization and sensitivity analysis. The optimized results are compared with the existing charging station deployment scheme in the road network to demonstrate the effectiveness of the proposed methodology. Full article
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