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

PyDDRBG is a Python framework for generating tunable test problems for static and dynamic multimodal optimization. It allows for quick and simple generation of a set of predefined problems for non-experienced users, as well as highly customized problems for more experienced users. It easily integrates with an arbitrary optimization method. It can calculate the optimization performance when measured according to the robust mean peak ratio. PyDDRBG is expected to advance the fields of static and dynamic multimodal optimization by providing a common platform to facilitate the numerical analysis, evaluation, and comparison in these fields. (C) 2021 The Author(s). Published by Elsevier B.V.

Automated summary

PyDDRBG is a Python framework for generating tunable test problems for static and dynamic multimodal optimization. It allows for quick and simple generation of predefined problems for non-experienced users, as well as highly customized problems for experienced users. It can integrate with any optimization method and calculate optimization performance based on robust mean peak ratio. PyDDRBG is expected to advance the fields of static and dynamic multimodal optimization by providing a common platform for numerical analysis, evaluation, and comparison.