Paper ID: 2106.06174

Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)

Danial Yazdani (1), Michalis Mavrovouniotis (2), Changhe Li (3), Guoyu Chen (4), Wenjian Luo (5), Mohammad Nabi Omidvar (6), Juergen Branke (7), Shengxiang Yang (8), Xin Yao (9) ((1) Business Intelligence Team, WINC, (2) ERATOSTHENES Centre of Excellence, (3) School of Artificial Intelligence, Anhui University of Sciences & Technology, (4) School of Artificial Intelligence, Anhui University of Sciences & Technology, (5) Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, School of Computer Science and Technology, Harbin Institute of Technology and Peng Cheng Laboratory, (6) School of Computing, University of Leeds, and Leeds University Business School, (7) Warwick Business school, University of Warwick, (8) Center for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, (9) Department of Computing and Decision Sciences, Lingnan University, and CERCIA, School of Computer Science, University of Birmingham)

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.

Submitted: Jun 11, 2021