Advanced Algorithms for Intelligent Decision-Making in Complex Systems

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

Deadline for manuscript submissions: 1 May 2027 | Viewed by 233

Editor

School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China
Interests: optimization; machine learning algorithms for service system
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Special Issue Information

Dear Colleagues,

Complex systems—spanning engineering equipment reliability, intelligent transportation networks, and educational platforms—present increasingly sophisticated decision-making challenges that classical methods struggle to address. Despite their diversity, these domains share a common need for advanced algorithms capable of handling uncertainty, high dimensionality, dynamic environments, and large-scale optimization.

Recent advances in reinforcement learning, Markov decision processes, probabilistic graphical models, and combinatorial optimization have opened new pathways for tackling such challenges. However, bridging the gap between algorithmic theory and real-world deployment remains a critical open problem, particularly as systems grow more interconnected, data-rich, and autonomous.

This Special Issue invites original research articles and reviews that propose, analyze, or evaluate advanced algorithms for intelligent decision-making in complex systems. Contributions addressing novel algorithm design, theoretical guarantees, computational efficiency, and practical validation across diverse application domains are particularly encouraged.

I look forward to receiving your contributions.

Dr. Tao Wang
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-anonymized 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.

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Keywords

  • intelligent decision-making
  • complex systems
  • algorithm design

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Published Papers (1 paper)

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Research

28 pages, 1643 KB  
Article
A Hybrid Fuzzy Cognitive Map and Genetic Algorithm Approach with Least-Influence Weighting for Decision-Support Forecasting
by Brian A. Polin, Alexander Rotshtein, Denis Katelnikov and Oksana Zelinska
Algorithms 2026, 19(7), 553; https://doi.org/10.3390/a19070553 - 6 Jul 2026
Abstract
We propose a hybrid intelligent methodology for forecasting outcomes in complex human-centered systems characterized by uncertainty and reliance on expert knowledge. The framework integrates fuzzy cognitive maps (FCMs), a novel Least-Influence Method for estimating causal arc weights, and genetic algorithms for model tuning. [...] Read more.
We propose a hybrid intelligent methodology for forecasting outcomes in complex human-centered systems characterized by uncertainty and reliance on expert knowledge. The framework integrates fuzzy cognitive maps (FCMs), a novel Least-Influence Method for estimating causal arc weights, and genetic algorithms for model tuning. The proposed influence comparison method simplifies expert elicitation by reducing the cognitive load of direct weight estimation, while the genetic algorithm ensures alignment of forecasts with observed or expert-derived data. A forecasting algorithm based on incremental changes in concept levels enhances the sensitivity of the output variable to factor variations. To illustrate the applicability of the framework, we construct a decision-support model for predicting weight-loss success under diverse psychological, behavioral, and environmental conditions. Simulation results demonstrate how factor ranking, scenario modeling, and paired influence analysis provide actionable insights for decision-making. Beyond the weight-loss domain, the approach is generalizable to a wide range of knowledge-based systems requiring robust integration of expert judgment, fuzzy reasoning, and evolutionary optimization. Full article
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