With the emerging need for high intensity and successful implementation across all the real-time scenarios, the significance of the Metaheuristic algorithms has been rapidly grown in the past decades. Metaheuristic models are inspired by the behaviors of the living being in nature, such as insects, animals, or organic living beings.
Metaheuristic algorithm plays a vital role in optimizing the solutions everywhere, including business planning, holiday planning, or industrial design, to name a few. Metaheuristic optimization techniques have been used to select the optimal solution from a huge number of available options. With the increased robustness and simplicity, metaheuristic computing has gained significant attention among other methods.
In contrast to the iterative methods, simple heuristics, or optimization algorithms, metaheuristic computing often finds the optimal solutions with minimal computational effort. Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are several popular metaheuristic methods.
Metaheuristic Optimizations-Classification of Metaheuristic Optimization- Hybrid Metaheuristic Optimization-Evolutionary Algorithms-Swarm Intelligence Algorithms-Hybrid Metaheuristic Optimization-Local Search Optimization-Scalability of Metaheuristic Algorithms-Exploration and Exploitation in Evolutionary Algorithms-Metaheuristic Inspired by Quantum Computing-Intelligent Sampling for Metaheuristic Algorithms-Parallel Metaheuristics-Human Inspiration Algorithms-Natural Phenomena based Algorithms-Local Search Optimization-Bio Inspired Metaheuristics-Population-based Metaheuristic-Multi Objective Optimization-Physics based Metaheuristics-Chemistry based Metaheuristics-Game Based Metaheuristics-Application areas-Cloud Computing-Edge Computing-Machine Learning-Routing Problems-Wireless Network Optimization-