Journal of Artificial Intelligence and Metaheuristics

The Journal of Artificial Intelligence and Metaheuristics (JAIM) is an international, peer-reviewed journal dedicated to publishing original research that advances artificial intelligence, metaheuristic optimization, and their integration. The journal welcomes theoretical, methodological, computational, and application-oriented contributions that demonstrate clear scientific novelty, rigorous evaluation, and relevance to intelligent problem-solving.

JAIM publishes research on the design, analysis, improvement, and implementation of artificial intelligence and optimization methods. Relevant contributions include machine learning, deep learning, reinforcement learning, explainable and trustworthy artificial intelligence, evolutionary computation, swarm intelligence, nature-inspired computing, and hybrid intelligent systems.

The journal particularly encourages research that integrates artificial intelligence with metaheuristic optimization, including automated machine learning, feature selection, hyperparameter optimization, neural architecture search, intelligent parameter estimation, and optimization-assisted predictive modelling.

Research proposing a new or modified algorithm must demonstrate a substantive methodological contribution. Such manuscripts should provide appropriate mathematical or computational justification, comparisons with relevant and recent methods, statistical analysis, computational-complexity assessment, and sufficient information to support reproducibility. Introducing a new name, metaphor, or terminology without a meaningful algorithmic contribution is not considered adequate novelty.

Application-oriented manuscripts are welcome when they present a significant advancement in artificial intelligence or optimization methodology. Papers that merely apply an established algorithm to a new dataset or application domain without sufficient methodological innovation, analysis, or practical insight are outside the journal’s scope.

Covered Topics

The journal’s areas of interest include, but are not limited to:

• Artificial intelligence and intelligent computational systems;

• Machine learning and deep learning;

• Reinforcement learning and intelligent decision-making;

• Explainable, trustworthy, and responsible artificial intelligence;

• Nature-inspired computing;

• Swarm intelligence, including particle swarm optimization, ant colony optimization, and artificial bee colony methods;

• Evolutionary algorithms, including genetic algorithms, differential evolution, and genetic programming;

• Trajectory-based and population-based optimization methods;

• Hybrid and ensemble intelligent models;

• Memetic algorithms;

• Hyper-heuristics and automated algorithm selection;

• Automated machine learning and neural architecture search;

• Feature selection and hyperparameter optimization;

• Single-objective, multi-objective, and many-objective optimization;

• Constrained, dynamic, stochastic, and large-scale optimization;

• Parallel and distributed metaheuristics;

• Optimization of complex and NP-hard problems;

• Theoretical analysis of artificial intelligence and metaheuristic methods;

• Convergence, complexity, scalability, and stability analysis;

• Benchmarking, statistical validation, and performance evaluation;

• Predictive modelling and forecasting using artificial intelligence and metaheuristics;

• Intelligent optimization for the Internet of Things and sensor networks;

• Cloud, edge, and distributed computing optimization;

• Network design, routing, and resource scheduling;

• Autonomous systems, robotics, and path planning;

• Computer vision, image processing, and pattern recognition;

• Bioinformatics and computational biology;

• Energy systems, financial modelling, supply chains, and other complex application domains where a substantial artificial intelligence or optimization contribution is demonstrated.

Types of Manuscripts and Scope Limitations

JAIM publishes original research articles presenting new theoretical findings, methodological developments, algorithms, computational models, experimental results, or rigorously validated applications.

Review articles, survey papers, bibliometric studies, tutorials, and perspective articles are outside the journal’s current scope.

JAIM does not consider manuscripts that:

• fall outside artificial intelligence or metaheuristic optimization;

• apply established methods without a substantive scientific contribution;

• introduce algorithms distinguished mainly by a new name or metaphor;

• lack appropriate comparisons with relevant methods;

• provide inadequate experimental or statistical validation;

• make unsupported claims of superiority; or

• do not provide sufficient methodological detail to permit evaluation and reproducibility.