A Fusion Crossover Mutation Sparrow Search Algorithm

Aiming at the inherent problems of swarm intelligence algorithm, such as falling into local extremum in early stage and low precision in later stage, this paper proposes an improved sparrow search algorithm (ISSA). Firstly, we introduce the idea of flight behavior in the bird swarm algorithm into SS...

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Bibliographic Details
Published in:Mathematical problems in engineering 2021, Vol.2021, p.1-17
Main Authors: Tang, Yanqiang, Li, Chenghai, Li, Song, Cao, Bo, Chen, Chen
Format: Article
Language:English
Subjects:
Crossovers
Exploitation
Food
Foraging behavior
Genetic algorithms
Intelligence
Normal distribution
Optimization
Optimization algorithms
Population
Search algorithms
Swarm intelligence
Velocity
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Summary:Aiming at the inherent problems of swarm intelligence algorithm, such as falling into local extremum in early stage and low precision in later stage, this paper proposes an improved sparrow search algorithm (ISSA). Firstly, we introduce the idea of flight behavior in the bird swarm algorithm into SSA to keep the diversity of the population and reduce the probability of falling into local optimum; Secondly, we creatively introduce the idea of crossover and mutation in genetic algorithm into SSA to get better next-generation population. These two improvements not only keep the diversity of the population at all times but also make up for the defect that the sparrow search algorithm is easy to fall into local optimum at the end of the iteration. The optimization ability of the improved SSA is greatly improved.
ISSN:1024-123X
1563-5147
DOI:10.1155/2021/9952606