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 contras...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience 2016-01, Vol.2016 (2016), p.428-437-033
Main Authors: Cao, Leilei, Goodman, Erik D., Xu, Lihong
Format: Article
Language:English
Subjects:
Algorithms
Animals
Artificial Intelligence
Biological Evolution
Chiroptera
Computer Simulation
Dynamics
Evolutionary algorithms
Genetic algorithms
Greedy algorithms
Heuristic
Linear programming
Mathematical research
Models, Theoretical
Mutation
Mutations
Optimization
Particulate Matter
Probability
Random variables
Searching
Strategy
Velocity
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2016/2565809