<![CDATA[This review examines advancements in metaheuristic techniques for multi-objective problems, where conflicting goals must be optimized simultaneously. Conventional methods often struggle, while approaches such as genetic algorithms, particle swarm optimization, ant colony optimization, and simulated annealing effectively explore large solution spaces to find near-optimal results. The paper analyzes enhancements including parameter tuning, hybridization, problem-specific knowledge integration, and improved exploration–exploitation balance. Key models discussed include NSGA-II, MOPSO, MACO, and MOSA. Performance is evaluated using benchmark metrics like hypervolume, generational distance, and coverage, showing improved speed and solution quality over traditional methods. Applications across engineering, economics, logistics, and supply chains highlight practical value. Future directions emphasize integrating machine learning, real-time adaptation, and uncertainty handling to further strengthen metaheuristic optimization for complex multi-objective challenges]]>
Copyrights © 2027
