BAS Beetle Antennae Search Algorithm Implementation

Beetle Antennae Search (BAS) is a bio-inspired intelligent optimization algorithm that mimics the behavior of beetles using their antennae to detect scent intensity during foraging activities to solve multi-objective function optimization problems. Unlike traditional optimization methods, BAS doesn't rely on gradient information or precise mathematical expressions of the objective function. Instead, it employs a simple left-right antennae sensing mechanism to progressively approach the optimal solution. In code implementation, this typically involves initializing a beetle position and defining step size parameters for iterative optimization.

The core algorithm concept is based on the symmetry of beetle antennae—when the left antenna receives a stronger scent signal than the right, the beetle moves toward the scent-concentrated direction, and vice versa. This mechanism is abstracted into an efficient search strategy where the "beetle" in the algorithm compares fitness values (representing scent intensity) on both sides of its current position to determine the next movement direction, enabling rapid convergence to optimal regions in the solution space. Key algorithmic steps include calculating left and right antenna positions, evaluating fitness functions, and updating the beetle's position vector through vector operations.

The BAS algorithm's advantages lie in its minimalist individual structure and fast convergence capability. Since it only maintains a single search individual (one "beetle"), its computational overhead is significantly lower than population-based intelligent methods like genetic algorithms or particle swarm optimization. The implementation typically requires fewer than 50 lines of code for basic versions. Additionally, BAS shows low sensitivity to initial values and can be easily integrated with other optimization techniques, making it suitable for complex optimization scenarios involving high-dimensional, nonlinear, or non-convex functions. The algorithm's simplicity allows for straightforward parameter tuning through variables like step size and antenna length.

In practical applications, researchers often balance the algorithm's global exploration and local exploitation capabilities by adjusting step sizes and antenna lengths. For instance, using larger step sizes initially for broad solution space exploration, then gradually reducing step sizes for precise optimal solution refinement. This characteristic makes BAS particularly adaptable in engineering optimization, parameter tuning, and machine learning hyperparameter search applications. Code implementations commonly include dynamic parameter adjustment mechanisms and convergence criteria checking functions to enhance optimization performance.