Journal of Artificial Intelligence and Metaheuristics

JAIM is envisioned to present cutting-edge research and development at the intersection of artificial intelligence and metaheuristic computing. This platform disseminates knowledge on all aspects of theoretical foundations, algorithmic design, and practical applications of metaheuristics fo solving complex optimization problems. Papers featuring novel metaheuristic algorithms, hybrid models, and their rigorous theoretical analyses are welcome. The journal will give keen insight into research dealing with nature-inspired computing, swarm intelligence, evolutionary algorithms, and their integration with machine learning and deep learning architectures. This journal also focuses on sharing recent advances in the application of these intelligent algorithms to real world, NP-hard problems across various domains. JAIM also welcomes topics related to large-scale optimization, multi-objective optimization, and heuristic learning in the domains of IoT, cloud computing, big data analytics, and cognitive systems. The journal prefers applications in Supply Chain Logistics, Network Design, Bio-informatics, Financial Modelling, Energy Systems, and Autonomous Robotics. Articles are expected to emphasize algorithmic innovation, performance validation, and practical relevance.

List of Covered Topics

·       Nature-Inspired Computing

·       Swarm Intelligence (e.g., PSO, ACO, ABC)

·       Evolutionary Algorithms (e.g., GA, DE, GP)

·       Trajectory-Based and Population-Based Methods

·       Hybrid Metaheuristic Models

·       Memetic Algorithms

·       Hyper-heuristics and Automated Algorithm Selection

·       Parallel and Distributed Metaheuristics

·       Multi-Objective and Many-Objective Optimization

·       Metaheuristics for Large-Scale and NP-hard Problems

·       Metaheuristic-based Deep Learning Architecture Search

·       Predictive Modelling and Forecasting using Metaheuristics

·       Theoretical Analysis of Metaheuristics

·       Convergence and Complexity Analysis

·       Benchmarking and Performance Evaluation

·       Algorithmic Complexity and Scalability

·       Stochastic Modeling and Probabilistic Analysis

·       Optimization in Internet of Things (IoT) and Sensor Networks

·       Cloud Computing and Resource Scheduling

·       Bioinformatics and Computational Biology

·       Autonomous Robotics and Path Planning

·       Network Design and Routing

·       Image Processing and Computer Vision