The Official Journal of the World Federation on Soft Computing (WFSC) http://www.softcomputing.org

Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems. Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The focus is to publish the highest quality research in application, advance and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Swarm Intelligence and other similar techniques to address real world complexities.

Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.

Major Topics:

The scope of this journal covers the following soft computing and related techniques, interactions between several soft computing techniques, and their industrial applications:

    Evolutionary Computing
    Fuzzy Computing
    Hybrid Methods
    Immunological Computing
    Neuro Computing
    Swarm Intelligence
    Machine and Deep Learning
    Rough Sets

The application areas of interest include but are not limited to applications of soft computing to:

    Agricultural Machinery, Smart Farming
    Autonomous Reasoning
    Big Data, IoT, Edge Computing
    Combinatorial Optimization
    Data Mining
    Decision Support
    Engineering Design Optimization
    Fault Diagnosis
    Finance
    Human-Machine Interface
    Intelligent Agents
    Manufacturing Systems
    Power Electronics
    Multi-objective Optimization
    Power and Energy
    Process and System Control
    Robotics
    Security
    Sensor Systems
    Signal or Image Processing
    Software Engineering
    Supply Chain Economy
    System Identification and Modelling
    Telecommunications
    Time Series Prediction
    Extended Reality, Metaverse, Digital Twins
    Vision or Pattern Recognition

Special Issue on Interpretable Reinforcement Learning
截稿日期: 2025-12-31

Reinforcement Learning (RL) has achieved significant successes in a variety of domains, from game playing to autonomous driving, control systems, and decision-making problems. However, the interpretability of RL models remains a critical challenge. Interpretable Reinforcement Learning (IRL) focuses on creating models that not only perform well but are also understandable to humans. Enhancing the interpretability of RL models could also significantly aid in addressing the reality gap—the performance difference between simulations and real-world applications—as more transparent models provide better insights into decision-making processes and facilitate smoother transitions from simulations to real environments. This field has recently gained significant attention from both the academic and industrial communities, leading to various initiatives such as the Interpretable Control Competition at GECCO 2024. IRL has also been identified as one of the main areas where soft computing techniques, such as evolutionary algorithms, may be an enabling factor.

This special issue seeks to gather cutting-edge research that advances the theory, methodologies, and applications of interpretable reinforcement learning, with particular emphasis on approaches based on soft computing (such as, but not limited to, evolutionary computation).

Guest editors:

Dr. Leonardo Lucio Custode
University of Trento, Trento, Italy
Research Interests: Interpretable and Explainable Artificial Intelligence, Reinforcement Learning, Machine Learning, Large Language Models, and Optimization.
Email: leonardo.custode@gmail.com

Prof. Giovanni Iacca
University of Trento, Trento, Italy
Research Interests: Computational Intelligence, Distributed Systems, Explainable AI, and Analysis of Biomedical Data
Email: giovanni.iacca@unitn.it

Prof. Eric Medvet
University of Trieste, Trieste, Italy
Research Interests: Evolutionary Computation (with a focus on genetic programming and grammar-guided genetic programming), Artificial Life, and the Application of Machine Learning Techniques to engineering and computer security problems, including robotics.
Email: emedvet@units.it

Dr. Giorgia Nadizar
University of Trieste, Trieste, Italy
Research Interests: Explainable AI, Evolutionary Machine Learning, Interpretable Control, Evolutionary Robotics
Email: giorgia.nadizar@phd.units.it

Dr. Erica Salvato
University of Trieste, Trieste, Italy
Research Interests: Control system, Artificial Intelligence, Reinforcement Learning, Robotics
Email: erica.salvato@dia.units.it

Special issue information:

Full scope of the Special Issue:

Reinforcement Learning (RL) has achieved significant successes in a variety of domains, from game playing to autonomous driving, control systems, and decision-making problems. However, the interpretability of RL models remains a critical challenge [1]. Interpretable Reinforcement Learning (IRL) focuses on creating models that not only perform well but are also understandable to humans. Enhancing the interpretability of RL models could also significantly aid in addressing the reality gap—the performance difference between simulations and real-world applications—as more transparent models provide better insights into decision-making processes and facilitate smoother transitions from simulations to real environments. This field has recently gained significant attention from both the academic and industrial communities, leading to various initiatives such as the Interpretable Control Competition at GECCO 2024 [2]. IRL has also been identified as one of the main areas where soft computing techniques, such as evolutionary algorithms, may be an enabling factor [3].

This special issue seeks to gather cutting-edge research that advances the theory, methodologies, and applications of interpretable reinforcement learning, with particular emphasis on approaches based on soft computing (such as, but not limited to, evolutionary computation).

We invite high-quality submissions on topics including, but not limited to:

Theoretical Foundations of Interpretable RL:

    New frameworks for interpretable decision-making in RL.
    Formal definitions and metrics for interpretability in RL contexts.
    Analytical and empirical studies on the trade-offs between interpretability and performance.

Methods and Techniques:

    Techniques for extracting interpretable policies from complex RL models.
    Novel algorithms that inherently produce interpretable solutions, such as evolutionary and swarm intelligence techniques.
    Visualization tools and methods for RL models and policies.
    Use of symbolic, rule-based, or other interpretable models in RL.

Applications:

    Case studies demonstrating the application of interpretable RL in real-world scenarios.
    Interpretable RL in healthcare, robotics, finance, and other critical domains.
    Comparative studies showing the impact of interpretability on user trust and system usability.

Human-in-the-Loop Systems:

    Techniques for incorporating human feedback into RL systems to improve interpretability.
    Studies on the effectiveness of human-in-the-loop approaches for developing interpretable RL systems.

Evaluation and Validation:

    Benchmarks and datasets for evaluating interpretability in RL.
    User studies assessing the interpretability of RL models and their decisions.
    Validation frameworks and experimental protocols for interpretable RL.

[1] Rudin, Cynthia, et al. "Interpretable machine learning: Fundamental principles and 10 grand challenges." Statistic Surveys 16 (2022): 1-85.
[2] https://giorgia-nadizar.github.io/interpretable-control-competition/
[3] Zhou, Ryan, et al. "Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems." arXiv preprint arXiv:2406.07811 (2024).

Manuscript submission information:

Important Dates:

Submission deadline: December 31, 2025
Final Decision: June 01, 2026

Paper submissions for the special issue should follow the submission format and guidelines for regular papers and be submitted at Editorial Manager®.

All the papers will be peer-reviewed following Applied Soft Computing reviewing procedures. Guest editors will make an initial assessment of the suitability and scope of all submissions. Papers will be evaluated based on their originality, presentation, relevance, and contributions, as well as their suitability to the special issue.

Each submission must contribute to soft computing related methodology. Papers that either lack originality or clarity in presentation or fall outside the scope of the special issue will be desk-rejected and will not be sent for review. Authors should select “VSI:ASOC_Interpretable Reinforcement Learning” when they reach the “Article Type” step in the submission process. The submitted papers must propose original research that has not been published nor is currently under review in other venues.

Keywords:

((interpretable) OR (interpretability)) AND ((RL) OR (Reinforcement Learning))

Special Issue on Artificial Bee Colony (ABC) Algorithm: Background, Advances and Future Directions
截稿日期: 2026-02-28

Swarm intelligence algorithms simulate the collective abilities of a group of living creatures that collectively exhibit intelligent behavior through their interactions and experiences, and that have no central supervision and can self-organize. One of the prominent members of the swarm intelligence algorithms is the Artificial Bee Colony, briefly ABC, algorithm, and it has been proposed for solving the continuous optimization problems in the year 2005, being inspired by the collective behaviors of the real foraging honeybees in a hive. ABC has a good alignment between the real-world honeybee foraging behavior and its components. In this strong analogy to the behavior of real bees, there are three different types of forager honeybees which correspond to the phases of algorithm: scout bees, employed bees, and onlooker bees and each food source around the hive represents a possible solution within the search space. Having a good balance between exploitation and exploration, ABC is a distinctive and qualified algorithm used in solving many different optimization problems in various fields successfully. The increase in size and complexity of optimization problems requires more powerful and effective versions of optimization algorithms. In the present call for papers, it was aimed to develop and disseminate powerful and effective new versions of the ABC algorithm in commemoration of the 20th anniversary of the ABC algorithm.

Guest editors:

Prof. Dr. Mustafa Servet Kiran
Konya Technical University
Email: mskiran@ktun.edu.tr

Prof. Dr. Dervis Karaboga
Erciyes University
Email: karaboga@erciyes.edu.tr

Prof. Dr. Bahriye Akay
Erciyes University
Email: bahriye@erciyes.edu.tr

Special issue information:

Full scope of the Special Issue:

The main objective of the special issue is to bring together advances in theory and applications of ABC algorithm. The special issue is open to all methodological research on ABC, including but not limited to:

    Implementing an ABC algorithm framework to solve discrete optimization problems with the novel approaches.
    Developing new versions of ABC algorithms for solving multi objective optimization problems.
    Improving the performance of ABC algorithm on the high dimensional optimization problems.
    Examining the behavior of ABC algorithm in dynamic and uncertain environments.
    Investigating the structural bias in ABC algorithm.
    Analyzing time complexity and stability of ABC algorithm.
    Integrating ABC algorithm in deep learning architectures and AutoML field.
    Using machine learning approaches to guide ABC search process.
    Employing ABC algorithm in cyber-security.
    Applying quantum computing for ABC algorithm.

For all manuscripts submitted to this special issue, it is expected that the performance of the methods will be validated through comprehensive tests that are well-established in the literature. In the special issue, any new metaheuristic algorithm proposal and known applications of the ABC algorithm will not be accepted for evaluation and publication.All manuscripts are meticulously evaluated according to the journal Applied Soft Computing's evaluation policies. Articles are prioritized for a scope assessment, and manuscripts not within the scope of the special issue are desk-rejected.

Manuscript submission information:

Important Dates:

    Submission deadline: February 28, 2026
    Final Decision (Accept/Reject) : June 30, 2026

Articles submitted for the special issue must follow the submission format and guidelines for regular articles and must be submitted via Editorial Manager. Authors should select "VSI:ASOC_ABC_20" when they reach the "Article Type" step during the submission process.

Please refer to the Guide for Authors to prepare your manuscript. For any further information, the authors may contact the Guest Editors.

Keywords:

artificial bee colony; continuous optimization; discrete optimization; multi objective optimization; high dimensional optimization