A New Multi-swarm Particle Swarm Optimization for Robust Optimization Over Time

A Simple Yet Effective Approach to Robust Optimization Over Time

“…In this section we describe how we collected the best known results from Table III. Benchmark 1 with λ = 1 is taken from [8], Benchmark 1 with λ = 0 from [11] and Benchmark 2 from [2]. Note that [11] compared himself with the results from [8], [9], [10] and showed that their results are superior.…”

“…Benchmark 1 with λ = 1 is taken from [8], Benchmark 1 with λ = 0 from [11] and Benchmark 2 from [2]. Note that [11] compared himself with the results from [8], [9], [10] and showed that their results are superior. For Benchmark 2 we considered only the random movement which in [2] was denoted as T P 13 .…”

“…In this section, we describe the moving peak benchmark commonly used in the ROOT literature. It is based on [15] and appeared in many papers [2], [7], [8], [9], [10], [11], [12], [13]. However, to the best of our knowledge, no complete and proper description was given in any of these papers.…”

“…Finally, [11] used a different function count. While the original and our approach recomputed the solution at every time step and then computed its survival based on the future values [11] recomputed the solution only when it dropped below the threshold δ. This resulted in the fact that they used approximately 8 times more function evaluations.…”

Robust Optimization Over Time by Learning Problem Space Characteristics

“…In its standard form, all peaks are behaving identical, so no solution is more robust than another. This is why in ROOT researchers used various modified versions [6], [8]- [11], [34].…”