Aim and Scope
Metaheuristic Optimization Review is an international, peer-reviewed journal dedicated to publishing high-quality review-oriented scholarship in metaheuristic optimization and related intelligent optimization methods. The journal provides a platform for systematic reviews, survey papers, bibliometric analyses, tutorial reviews, and critical perspective articles that synthesize current knowledge, evaluate methodological developments, and identify emerging directions in optimization research.
The journal focuses on the analysis, comparison, and critical assessment of metaheuristic, nature-inspired, and hybrid optimization approaches, including their theoretical foundations, algorithmic characteristics, benchmarking practices, and application domains. Particular emphasis is placed on review contributions that offer clear scholarly value through structured synthesis, critical evaluation, methodological insight, and guidance for future research.
Metaheuristic Optimization Review welcomes manuscripts that examine areas such as swarm intelligence, evolutionary computation, hybrid and adaptive metaheuristics, population-based search, and related intelligent optimization frameworks. The journal also considers review-oriented contributions in adjacent fields, including machine learning, intelligent systems, robotics, software engineering, natural language processing, and multi-agent systems, provided that metaheuristic optimization constitutes a central methodological focus.
The journal is intended for researchers, academics, and practitioners seeking authoritative, up-to-date, and analytically rigorous review resources on optimization methods and their applications across computational intelligence, engineering, and applied sciences.
Topics of interest include, but are not limited to:
Metaheuristic Optimization
Swarm Intelligence
Evolutionary Algorithms
Nature-Inspired Optimization Methods
Hybrid and Adaptive Metaheuristics
Benchmarking and Comparative Analysis of Optimization Algorithms
Optimization in Machine Learning and Data Analytics
Optimization in Software Engineering
Intelligent Robotics and Autonomous Systems
Optimization in Natural Language Processing
Multi-Agent and Distributed Optimization Systems
Applications of Metaheuristic Methods in Engineering and Computational Intelligence