APBAO: Adaptive and Parallel Beetle Antennae Optimization

• DOI: 10.1109/ithings-greencom-cpscom-smartdata-cybermatics60724.2023.00087
• Publication Date: 2024-05-01T17:26:42Z
• Authors: Xuan Liu; Chenyan Wang; Xiangyu Qu; Chang Xu; Zheng Liu; Shan Jiang
• Keywords: Benchmark (surveying)

Beetle Antennae Search Algorithm (BAS) is a new single-agent intelligent optimization algorithm, which low computational complexity, ease of implementation and fast convergence speed in low-dimensional function optimization. Based on BAS, this paper proposes Adaptive Parallel Optimization (APBAO), evolves from single iterative individual BAS to multiple parallel individuals, improving algorithm efficiency through adaptive step size. Finally, putting forward the elite system, preserves high-quality solutions each iteration. To verify performance conducts tests using standard benchmark functions compares APBAO with Particle Swarm (PSO) Ant Colony (ACO). The experimental results show that improves by 97.39% compared 84.46% 86.98% PSO ACO, respectively.