Unidimensional Search for Solving Continuous High-Dimensional Optimization Problems

This paper presents a performance study of two versions of a unidimensional search algorithm aimed at solving high-dimensional optimization problems. The algorithms were tested on 11 scalable benchmark problems. The aim is to observe how metaheuristics for continuous optimization problems respond with increasing dimension. To this end, we report the algorithms´ performance on the 50, 100, 200 and 500-dimension versions of each function. Computational results are given along with convergence graphs to provide comparisons with other algorithms during the conference and afterwards.