BAS Beetle Antennae Search Optimization Algorithm

The Beetle Antennae Search (BAS) algorithm is a novel heuristic optimization algorithm inspired by the foraging behavior of beetles that use their antennae to sense environmental cues. This algorithm simulates how beetles compare environmental differences detected by their left and right antennae to locate optimal solutions, demonstrating strong global search capabilities.

The algorithm's core concept is based on the movement patterns of beetles in three-dimensional space. As a beetle advances, its left and right antennae continuously probe the surroundings, comparing signal strengths from both sides to determine subsequent movement directions. This biological mechanism is abstracted into an efficient optimization strategy: search agents continuously adjust their positions in the solution space, guiding search directions by comparing objective function values at left and right "antennae" positions.

MATLAB implementation typically involves several key steps: First, initialize the beetle's position and search direction using random orientation vectors. Then, during each iteration, calculate symmetrical probe positions on both sides of the current best position using direction vectors and step size parameters. The algorithm evaluates objective function values at these probe positions and updates the beetle's position based on the difference between left and right measurements. This mechanism ensures effective escape from local optima while maintaining global search capabilities.

The algorithm offers advantages such as minimal parameter requirements and straightforward implementation, making it particularly suitable for high-dimensional optimization problems. Compared to traditional optimization algorithms, BAS requires no complex parameter tuning processes and demonstrates faster convergence rates. In practical applications, it has been successfully employed in various engineering optimization scenarios including neural network parameter optimization and power system scheduling.

When compared to other bio-inspired algorithms, Beetle Antennae Search exhibits more stable convergence performance and lower computational complexity. Its unique unidirectional search mechanism maintains strong exploration capabilities while ensuring effective local exploitation during optimization processes.