Aim and Scope
The Journal of Artificial Intelligence and Metaheuristics (JAIM) is an international peer-reviewed journal publishing original research and review articles in two closely connected fields: artificial intelligence and machine learning and metaheuristic optimization. The journal provides a platform for theoretical advances, algorithmic development, rigorous experimental evaluation, and significant real-world applications.
The artificial intelligence and machine-learning pillar covers the development, analysis, and application of intelligent computational methods, including machine learning, deep learning, computational intelligence, explainable and trustworthy AI, intelligent data analysis, predictive modelling, computer vision, and decision-support systems.
The metaheuristic optimization pillar covers the design, analysis, improvement, and application of metaheuristic algorithms, including nature-inspired computing, evolutionary computation, swarm intelligence, trajectory-based and population-based methods, hybrid and memetic algorithms, hyper-heuristics, and single-objective, multi-objective, many-objective, constrained, combinatorial, and large-scale optimization.
JAIM particularly welcomes research connecting these two pillars, including the use of metaheuristics for feature selection, hyperparameter optimization, neural architecture search, and machine-learning model improvement, as well as the use of artificial intelligence and machine learning to design, select, configure, or enhance optimization algorithms.
Papers may address either pillar independently or investigate their integration, provided that they make a substantive theoretical, methodological, computational, or applied contribution within the journal’s scope. Therefore, an artificial intelligence paper is not required to contain a metaheuristic component, and a metaheuristic optimization paper is not required to incorporate machine learning.
Applications may include, but are not limited to, healthcare, bioinformatics, energy systems, supply chains and logistics, transportation, manufacturing, finance, telecommunications, Internet of Things, cloud computing, cybersecurity, autonomous robotics, network design and routing, image and signal processing, and environmental systems.
Submissions should clearly identify their original contribution, apply appropriate evaluation methods and comparative baselines, report results transparently, and discuss the significance and limitations of the findings. Papers that only apply established software or algorithms to a new dataset or problem without sufficient scientific or methodological contribution are outside the journal’s scope.
Topics Covered
Topics of interest include, but are not limited to:
Artificial intelligence and machine-learning algorithms
Deep learning and neural-network architectures
Computational intelligence
Explainable, trustworthy, and responsible AI
Predictive modelling, classification, and forecasting
Intelligent data analysis and decision-support systems
Computer vision and image processing
Nature-inspired and bio-inspired computing
Swarm-intelligence algorithms
Evolutionary algorithms and evolutionary computation
Trajectory-based and population-based optimization
Hybrid metaheuristic models
Memetic algorithms
Hyper-heuristics and automated algorithm selection
Parallel and distributed metaheuristics
Single-objective, multi-objective, and many-objective optimization
Constrained, discrete, and combinatorial optimization
Large-scale and NP-hard optimization problems
Metaheuristic feature selection and hyperparameter optimization
Metaheuristic-based neural architecture search
Machine-learning-assisted optimization
Theoretical analysis of artificial intelligence and metaheuristic methods
Convergence, complexity, and scalability analysis
Benchmarking, statistical analysis, and performance evaluation
Artificial intelligence and optimization applications in science, engineering, business, and society