Performance Analysis of Bat Algorithm Enhanced with PSO Particle Swarm Optimization

Title: Performance Analysis of Bat Algorithm Enhanced with PSO Particle Swarm Optimization

Swarm intelligence algorithms demonstrate excellent performance in optimization problems, with Bat Algorithm (BA) and Particle Swarm Optimization (PSO) each possessing distinct advantages. This paper explores how to leverage PSO's global search capability to improve the traditional Bat Algorithm, with optimization effectiveness verified through the Sphere benchmark function. Code implementation typically involves defining hybrid velocity update functions and parameter adaptation mechanisms.

The traditional Bat Algorithm simulates bat echolocation behavior for search operations but tends to fall into local optima. In contrast, PSO utilizes information sharing mechanisms among particles to explore solution spaces more effectively. Key integration strategies include:

Velocity Update Strategy Fusion - Incorporating PSO's particle velocity update formula to enhance bats' global exploration capability. Implementation requires creating a hybrid velocity function that combines BA's frequency factor with PSO's social and cognitive components. Position Adjustment Mechanism - Preserving BA's frequency modulation characteristics while integrating PSO's social cognition component. This can be coded through dual-position update rules switching based on convergence conditions. Parameter Adaptation - Dynamically adjusting pulse frequency and loudness parameters to balance search intensity across different algorithm phases. This involves implementing real-time parameter tuning using fitness feedback mechanisms.

Experiments utilize the Sphere benchmark function, comparing pre- and post-improvement algorithm performance through 50 independent runs, focusing on these metrics: Best Value - Reflects the algorithm's ability to find global optima Worst Value - Indicates the algorithm's stability lower bound Average Value - Evaluates overall optimization quality Variance - Measures algorithm run reliability

This hybrid strategy demonstrates superior convergence and stability in complex optimization problems, providing new solutions for engineering optimization challenges. Future work could extend this method to multi-objective optimization or high-dimensional problem scenarios through additional modular coding and constraint handling mechanisms.