Paper Info
Title
Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution Operator
Abstract
In this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad development capability, and unbalanced exploration and development capabilities. The improved algorithm has three parts. First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity. Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability. Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development. A numerical simulation experiment based on 36 different types of benchmark functions showed that while the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA, IAVOA outperforms the other 7 swarm intelligence algorithms in the mean and best values of 33 benchmark functions.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3203813
IEEE ACCESS
Keywords
DocType
Volume
Statistics, Sociology, Mathematical models, Particle swarm optimization, Metaheuristics, Convergence, Benchmark testing, African vulture optimization algorithm, benchmark function, differential evolution, quasi-oppositional learning, swarm intelligence algorithm
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Renju Liu100.34
Tianlei Wang200.34
Jing Zhou332754.75
Xiaoxi Hao400.34
Ying Xu501.01
Jiongzhi Qiu600.68