Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)
It provides a flexible benchmark for researchers in evolutionary computation to test algorithms on diverse dynamic landscapes, but is incremental as it builds on existing benchmark concepts.
The paper introduces the Generalized Moving Peaks Benchmark (GMPB), a tool for generating customizable continuous dynamic optimization problems with controllable dynamic and morphological features, used in competitions like IEEE CEC.
The Generalized Moving Peaks Benchmark (GMPB) is a tool for generating continuous dynamic optimization problem instances with controllable dynamic and morphological characteristics. GMPB has been used in recent Competitions on Dynamic Optimization at prestigious conferences, such as the IEEE Congress on Evolutionary Computation (CEC). This dynamic benchmark generator can create a wide variety of landscapes, ranging from simple unimodal to highly complex multimodal configurations and from symmetric to asymmetric forms. It also supports diverse surface textures, from smooth to highly irregular, and can generate varying levels of variable interaction and conditioning. This document provides an overview of GMPB, emphasizing how its parameters can be adjusted to produce landscapes with customizable characteristics. The MATLAB implementation of GMPB is available on the EDOLAB Platform.