Improved Black Widow Optimization Algorithm Based on Information Pheromone Evaporation-Tracking Mechanism

Abstract Compared with traditional algorithms, swarm intelligence optimization algorithms have the advantages of being easy to implement, stable, strong self-organization, and good parallelism. The original Black Widow Optimization algorithm framework is simple, with intuitive parameter settings. It is suitable for a variety of optimization problems, can effectively explore solution space, and has strong global search capabilities. In response to the issues with the Black Widow Optimization algorithm, this paper proposes an improved Black Widow Optimization algorithm based on the mechanism of information pheromone evaporation-tracking, and conducts simulation experiments. The results of the experiment show that the algorithm has better performance, and the improved algorithm has certain feasibility and effectiveness.