A Novel Parametric benchmark generator for dynamic multimodal optimization

PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods

“…A comprehensive benchmark generator for DMMO should reflect the diverse challenges associated with DMMO. In particular, these challenges can be divided into three groups [18]:…”

“…This allows for analyzing the pros and cons of each DMMO method more reliably. The recently proposed Dynamic Distortion and Rotation Benchmark (DDRB) generator [18] allows for simulation and control of these challenges. When compared with exiting benchmark generators for DMMO, it can simulate more diverse features or at least has the advantages of easy integration with existing static multimodal optimization problems and deterministic and thus platform independent nature (see [18] for in-depth analysis and comparison with other benchmark generators for DMMO).…”

“…• the parameter e c controls the difficulty of multimodal optimization by determining the distortion of the landscape during dynamic changes (see [18] for details), which is the extent of the changes in the shapes, sizes, and irregularity in the distribution of global minima.…”

“…This study provides a user-friendly Python framework for the DDRB generator [18]. This framework, called PyDDRBG, also provides a method that calculates the performance of a static or dynamic multimodal optimization method, which will be explained in Section 3.5.…”

“…• It is easy to use and understand by non-expert users, allowing them to simulate the exact problems employed in [18].…”