Metaheuristic Computing is adaptive computing that applies general heuristic rules in solving a category of computational problems. A heuristic is a technique that solves a problem faster when traditional techniques are too slow. Metaheuristic models have been greatly developed and inspired by natural phenomena such as wildlife, insects, plants, animals, birds, living organisms, laws of physics, biological sciences, genetics, games, natural human activities, and other natural evolutionary processes.
Metaheuristics are applied in many areas, including vehicle routing and transport scheduling problems. Metaheuristic algorithms can be classified into three main categories: evolutionary algorithms, swarm algorithms, and physical-based algorithms. Evolutionary algorithms, such as genetic or differential evolution, mimic the principles in the natural evolutionary process to develop robust optimization techniques. Swarm algorithms, such as particle swarm optimization algorithms, mimic the collective behaviors of various beings.
The physical-based algorithms are inspired by real-world physical processes such as simulated annealing and gravitational search algorithms. Metaheuristic algorithms are effectively used in solving a wide variety of optimization problems. Metaheuristics computing provides the means to manage the trade-off between performance and quality of solutions and often finds the optimal solutions with minimal computational cost and effort.