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Mathematics
Special Issues
Intelligent and Nature-Inspired Heuristics for Optimization and Decision-Making Problems
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Intelligent and Nature-Inspired Heuristics for Optimization and Decision-Making Problems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 30 August 2026 | Viewed by 1491

Special Issue Editor

Dr. Sara Pérez-Carabaza

E-Mail Website
Guest Editor
Department of Applied Mathematics and Computational Sciences, University of Cantabria, Santander, Spain
Interests: metaheuristic optimization; ant colony optimization; swarm intelligence; multi-agent systems; UAV trajectory planning; operations research; combinatorial optimization; decision-making under uncertainty
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many real-world optimization and decision-making problems are highly complex, dynamic, and often NP-hard, making exact solutions computationally intractable. In such contexts, intelligent and nature-inspired heuristics provide powerful alternatives that deliver high-quality solutions within reasonable computational times.

This Special Issue aims to highlight recent advances in intelligent and nature-inspired heuristic methods for solving complex, multi-objective, or dynamic optimization and decision-making problems. We welcome contributions focused on the design, analysis, and application of metaheuristics such as ant colony optimization, particle swarm optimization, evolutionary algorithms, and other swarm- or population-based techniques.

In addition, we encourage works that explore hybrid approaches combining heuristics with machine learning, mathematical programming, or problem-specific heuristic design to improve performance, adaptability, or interpretability.

Applications may include vehicle routing and scheduling, multi-agent coordination, resource allocation, logistics and transportation, energy systems, and other real-world optimization challenges. Both theoretical developments and practical implementations are welcome.

We particularly welcome contributions that advance the understanding, design, and application of intelligent and nature-inspired heuristics in optimization and decision-making.

Dr. Sara Pérez-Carabaza
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • metaheuristics
  • nature-inspired optimization
  • ant colony optimization
  • swarm intelligence
  • real-world applications
  • matheuristics
  • combinatorial optimization
  • optimization under uncertainty

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Published Papers (3 papers)

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56 pages, 15159 KB  
Article
Smart Exploration of Lentic Cyanobacterial Water Bodies Supported by Model-Based Simulation, Autonomous Surface Vehicles and Evolutionary Algorithms
by Gonzalo Carazo-Barbero, Eva Besada-Portas, José Antonio López-Orozco and José Luis Risco-Martín
Mathematics 2026, 14(11), 1821; https://doi.org/10.3390/math14111821 - 24 May 2026
Viewed by 109
Abstract
Cyanobacterial blooms in lakes and reservoirs pose significant environmental and public health risks. This paper presents an effective exploration strategy to detect them from Autonomous Surface Vehicles (ASVs) equipped with probes, whose sensing trajectories are optimized by an AI-based planner that considers the [...] Read more.
Cyanobacterial blooms in lakes and reservoirs pose significant environmental and public health risks. This paper presents an effective exploration strategy to detect them from Autonomous Surface Vehicles (ASVs) equipped with probes, whose sensing trajectories are optimized by an AI-based planner that considers the 3D spatial-temporal evolution of the cyanobacteria concentration obtained by a multiphysics model. The planner, simultaneously working on the AI decision-making and robotic domains, optimizes the surface displacement of the ASV and the depth of its probe by solving a constrained multi-objective optimization problem that minimizes the mission duration and trajectory length, maximizes the possibilities of the probe to overpass areas with high concentration of cyanobacteria, and satisfies operational constraints (such as ASV velocity or acceleration limits, and obstacle avoidance). The optimization is supported by two well-known versions of the Non-Sorted Generic Algorithm (NSGA-II and NSGA-III) and by encoding the trajectories with spline curves whose number of control points can be fixed, progressively increased, or freely manipulated by the algorithm. The performance of different configurations of the planner is tested against six scenarios obtained from different simulations of the multiphysics model (which couples water dynamics and temperature, light transmission, daily vertical migration of the cyanobacteria and their growth). The statistical analysis of the planner results determines that NSGA-III working with variable-length chromosomes and NSGA-II with the progressive increment of spline points as the best configurations for maximizing cyanobacteria detection, and minimizing mission duration and trajectory length. Full article
(This article belongs to the Special Issue Intelligent and Nature-Inspired Heuristics for Optimization and Decision-Making Problems)
25 pages, 3202 KB  
Article
Population-Based Metaheuristic Algorithms for a Hybrid Batch-Continuous Production Scheduling Problem in a Distributed Pharmaceutical Supply Chain
by Seung Jae Lee and Byung Soo Kim
Mathematics 2026, 14(6), 1044; https://doi.org/10.3390/math14061044 - 19 Mar 2026
Viewed by 334
Abstract
We study a pharmaceutical scheduling problem with a hybrid batch-continuous manufacturing process in a distributed supply chain. The supply chain consists of heterogeneous plants and one distribution center. Each plant adopts an unrelated permutation flowshop layout consisting of a hybrid batch-continuous production line. [...] Read more.
We study a pharmaceutical scheduling problem with a hybrid batch-continuous manufacturing process in a distributed supply chain. The supply chain consists of heterogeneous plants and one distribution center. Each plant adopts an unrelated permutation flowshop layout consisting of a hybrid batch-continuous production line. Each pharmaceutical order is split and produced in multi-production sites located in various regions. The pharmaceutical medicines manufactured by the production sites are directly shipped to a distribution center. To minimize the makespan, we formulate the addressed scheduling problem as a mathematical model. To solve this model, we propose four metaheuristic variants by applying two population-based metaheuristics to two distinct solution structures. We compare the proposed metaheuristics to evaluate their performance in the numerical experiments. Additionally, we present managerial insights through sensitivity analysis. Full article
(This article belongs to the Special Issue Intelligent and Nature-Inspired Heuristics for Optimization and Decision-Making Problems)
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34 pages, 2671 KB  
Article
A Tuning-Free Constrained Team-Oriented Swarm Optimizer (CTOSO) for Engineering Problems
by Adel BenAbdennour and Abdulmajeed M. Alenezi
Mathematics 2026, 14(1), 176; https://doi.org/10.3390/math14010176 - 2 Jan 2026
Cited by 1 | Viewed by 706
Abstract
Constrained optimization problems (COPs) are frequent in engineering design yet remain challenging due to complex search spaces and strict feasibility requirements. Existing swarm-based optimizers often rely on penalty functions or algorithm-specific control parameters, whose performance is sensitive to problem-dependent tuning and may lead [...] Read more.
Constrained optimization problems (COPs) are frequent in engineering design yet remain challenging due to complex search spaces and strict feasibility requirements. Existing swarm-based optimizers often rely on penalty functions or algorithm-specific control parameters, whose performance is sensitive to problem-dependent tuning and may lead to premature convergence or infeasible solutions when feasible regions are narrow. This paper introduces the Constrained Team-Oriented Swarm Optimizer (CTOSO), a tuning-free metaheuristic that adapts the ETOSO framework by replacing linear exploiter movement with spiral search and integrating Deb’s feasibility rule. The population divides into Explorers, promoting diversity through neighbor-guided navigation, and Exploiters, performing intensified local search around the global best solution. Extensive evaluation on twelve constrained engineering benchmark problems shows that CTOSO achieves a 100% feasibility rate and attains the highest overall composite performance score among the compared algorithms under limited function-evaluation budgets. On the CEC 2017 constrained benchmark suite, CTOSO attains an average feasibility rate of 79.78%, generating feasible solutions on 14 out of 15 problems. Statistical analysis using Wilcoxon signed-rank tests and Friedman ranking with Nemenyi post hoc comparison indicates that CTOSO performs significantly better than several baseline optimizers, while exhibiting no statistically significant differences with leading evolutionary methods under the same experimental conditions. The algorithm’s design, requiring no tuning of algorithm-specific control parameters, makes it suitable for real-world engineering applications where tuning effort must be minimized. Full article
(This article belongs to the Special Issue Intelligent and Nature-Inspired Heuristics for Optimization and Decision-Making Problems)
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