Beetle Antennae Search with Chaos BAS BP Algorithm: Research Paper and Code Implementation

Beetle Antennae Search with Chaos (BAS-Chaos) is an intelligent optimization algorithm that integrates biomimetic principles with chaos theory. This algorithm simulates the mechanism of beetles using their antennae to perceive the environment, combined with the randomness and ergodicity of chaotic sequences, significantly enhancing the global optimization capability and local optimum escape ability of traditional beetle antennae search algorithms.

Application in BP Neural Networks BAS-Chaos is commonly used to optimize the initial weights and thresholds of BP neural networks. Traditional BP algorithms rely on gradient descent and are prone to local optima with slow convergence. By introducing the chaotic perturbation mechanism of BAS-Chaos, more efficient exploration in parameter space can be achieved, thereby improving neural network training efficiency and prediction accuracy. Key implementation involves generating chaotic sequences for parameter initialization and applying chaotic perturbations during weight updates.

Core Research Content Related papers typically cover the following areas: Algorithm Design: Integration methods of chaotic systems like Logistic chaotic mapping with beetle antennae search. Convergence Proof: Mathematical analysis validating the global convergence characteristics of the algorithm. Comparative Experiments: Performance comparisons with algorithms like PSO and GA on benchmark functions or practical tasks (e.g., classification, prediction).

Code Implementation Highlights A typical implementation includes modules for chaotic sequence generation, beetle antennae direction updating, and dynamic step size adjustment. Code implementations (MATLAB/Python versions) should pay attention to the impact of chaotic parameter settings (such as fractal coefficients) on results. Key functions typically include chaos_map() for sequence generation and adaptive_step() for step size adjustment.

Extension Directions Multi-objective Optimization: Extending BAS-Chaos to Pareto optimal solution search. Hybrid Strategies: Integration with simulated annealing, differential evolution, and other algorithms.

(Note: For specific papers or code resources, it is recommended to search and download through academic databases or GitHub platforms using relevant keywords.)