Improved African Vulture Optimization Algorithm Based on Quasi-Oppositional Differential Evolution Operator
Renju Liu, Tianlei Wang, Jing Zhou, Xiaoxi Hao, Ying Xu, Jiongzhi Qiu
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 the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA while exhibiting superior performance compared to those of other swarm intelligence algorithms.