Hi. Metaheuristic algorithms consist of many algorithms such as genetic algorithms, particle swarm optimization, artificial bee colony, differential evolution, grey wolf optimizer, lightning search algorithm and so on. As far as I know, the term metaheuristic is a general concept. You can take a look the reference papers of the above algorithms for comparison. Best regards.
heuristics are often problem-dependent, that is, you define an heuristic for a given problem. Metaheuristics are problem-independent techniques that can be applied to a broad range of problems. An heuristic is, for example, choosing a random element for pivoting in Quicksort.
Evolutionary algorithms (EAs), which are population based, are a major, and arguably the most popular, class of metaheuristics like GP or GA
These references are useful
Meta-Heuristics Algorithms: A Survey
DOI:10.5120/ijca2018916427
Introduction to Evolutionary Algorithms
DOI:10.1201/9780429298028-4
In book: Applied Evolutionary Algorithms for Engineers Using Python
Evolutionary algorithms, physics-based algorithms, swarm-based algorithms, and human-based algorithms are the four primary types of meta-heuristic algorithms. These algorithms are based on human or animal behavior, as well as some physical behaviors of molecules, and so forth.
Genetic algorithms, partial swarm optimization, grey wolff optimization, ant colony optimization, and Social-Based Algorithm are some example.