In the book
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a large number of meta-heuristics based loosely on Nature are described. A list is: Simulated Annealing, Genetic Algorithms, Differential Evolution, Particle Swam Optimization, Firefly Algorithms, Cuckoo Search, Bat Algorithms, Flower Pollination Algorithms, But there are actually more to be found:
a large number of meta-heuristics based loosely on Nature are described. A list is: Simulated Annealing, Genetic Algorithms, Differential Evolution, Particle Swam Optimization, Firefly Algorithms, Cuckoo Search, Bat Algorithms, Flower Pollination Algorithms, But there are actually more to be found:
- Moth-flame optimization
- Whale Optimization
- Sine Cosine Algorithm
- Dragonfly Algorithm
- Multi-Verse Optimization
- Ant Lion Optimizer
- Grey Wolfe Optimizer
- Slime Mold Solver (apparently the favorite of Paul Rubin)
- and many more
I am note sure if the development of all these different methods is a good sign. How many should one consider or try out on a given problem?
References
A useful paper with a similar point of view:
Kenneth Sörensen, “Metaheuristics—the metaphor exposed,” International Transactions in Operational Research, Volume 22, Issue 1, January 2015, Pages 3–18
(Updated to reflect comments)