Academic Editor: Youssef EL FOUTAYENI
Received |
Accepted |
Published |
06 February 2020 |
21 February 2020 |
10 March 2020 |
Abstract: Metaheuristics have been used to resolve hard continuous and discrete problems instead of exact methods. Their principal advantages are that they provide good solutions in a reasonable amount of time and are problem independent. In the few last decades metaheuristics inspired from biology have been emerged. They are easy to implement and provide amazing results. We can cite Firefly Algorithm (FA) [1], Cuckoo Search (CS) [2], Bat Algorithm (BA) [3], Biogeography-Based Optimization (BBO) [4] and Shuffled Frog Leaping Algorithm (SFLA) [5]. These bio-inspired metaheuristics were compared to known metaheuristics of state of art like Genetic Algorithm (GA) [6], Ant Colony Optimization (ACO) [7], and Particle Swarm Optimization (PSO) [8]. In this communication we will give a general classification of metaheuristics, a brief review of the five abovementioned bio-inspired metaheuristics, their principals, algorithms, applications and a synthetic comparison table.
