NEApr 4, 2019

Convergence analysis of beetle antennae search algorithm and its applications

arXiv:1904.02397v171 citations
Originality Synthesis-oriented
AI Analysis

This work addresses a theoretical gap for researchers in optimization algorithms, but it is incremental as it builds on an existing method.

The paper tackled the lack of convergence analysis for the beetle antennae search algorithm by providing theoretical analysis and testing its performance on benchmark functions, showing it outperforms some existing meta-heuristic algorithms.

The beetle antennae search algorithm was recently proposed and investigated for solving global optimization problems. Although the performance of the algorithm and its variants were shown to be better than some existing meta-heuristic algorithms, there is still a lack of convergence analysis. In this paper, we provide theoretical analysis on the convergence of the beetle antennae search algorithm. We test the performance of the BAS algorithm via some representative benchmark functions. Meanwhile, some applications of the BAS algorithm are also presented.

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Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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