Algorithm for Local Extrema Seeking of Black-Box Functions Based on Information Landscape Measure

This paper provides an introduction to the concepts of fitness landscapes and information landscapes(IL), and proposes an improvement to the IL measure, transforming it into an indicator for evaluating the ruggedness of landscapes. A novel extremum-solving algorithm based on the IL measure is introduced for the extremum seeking of high-dimensional functions. The algorithm incorporates Latin hypercube sampling (LHS) from spatial filling designs to sample high-dimensional spaces, thereby obtaining regional sample points in high-dimensional spaces. It then applies the IL measure to calculate intervals, facilitating the extremum solving process of the algorithm. Finally, by comparing this algorithm with other methods, it is demonstrated that the algorithm can find all solutions with fewer initial points, outperforming traditional methods in both speed and solution quality, thus providing a theoretical foundation for subsequent engineering applications.