Dynamic Environment Optimization Algorithm Based on Dynamic Lion Swarm Algorithm and Its Implementation

In the information age, many practical problems involve dynamic objective functions that change over time or due to other factors. Optimizing in such dynamic environments is crucial both theoretically and practically, as it addresses prominent challenges in optimization today. For instance, dynamic vehicle path planning requires adapting to changing vehicle and road conditions to determine the optimal route. In image processing, dynamic multi-objective segmentation can enhance recognition accuracy by addressing changing image data. In engineering, designing dynamic welded beams involves adjusting for changes in material properties over time. Traditional lion swarm optimization (LSO) algorithms often struggle with dynamic environments, tending to optimize based on outdated conditions and missing global optimal solutions. This paper improves LSO by incorporating dynamic particle swarm optimization mechanisms and the black-winged kite algorithm's hunting and elimination strategies to better track global optima in changing environments. The performance of the improved dynamic lion swarm algorithm is evaluated using the four-peak DF1 dynamic environment model, enhanced by the bimodal DFI model.