A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.
Department of Control Science and Engineering, Tongji University, Shanghai 201804, China; BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA.
Leilei
L
Cao
Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.
Lihong
L
Xu
BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, MI 48824, USA.
Erik D
ED
Goodman
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D005075:Biological Evolution; D002685:Chiroptera; D003198:Computer Simulation; D008962:Models, Theoretical; D052638:Particulate Matter; D011336:Probability
D052638:Particulate Matter