Generating large-scale dynamic optimization problem instances using the generalized moving peaks benchmark

作     者：Omidvar, Mohammad Nabi Yazdani, Danial Branke, Jürgen Li, Xiaodong Yang, Shengxiang Yao, Xin 

作者机构：School of Computing University of Leeds Leeds University Business School Leeds United Kingdom Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Operational Research and Management Sciences Group Warwick Business School University of Warwick CoventryCV4 7AL United Kingdom  RMIT University GPO Box 2476 Melbourne3001 Australia School of Computer Science and Informatics De Montfort University Leicester United Kingdom  School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2021年

核心收录：

主　　题：Optimization 

摘      要：This document describes the generalized moving peaks benchmark (GMPB) [1] and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems. It presents a set 15 benchmark problems, the relevant source code, and a performance indicator, designed for comparative studies and competitions in large-scale dynamic optimization. Although its primary purpose is to provide a coherent basis for running competitions, its generality allows the interested reader to use this document as a guide to design customized problem instances to investigate issues beyond the scope of the presented benchmark suite. To this end, we explain the modular structure of the GMPB and how its constituents can be assembled to form problem instances with a variety of controllable characteristics ranging from unimodal to highly multimodal, symmetric to highly asymmetric, smooth to highly irregular, and various degrees of variable interaction and ill-conditioning. © 2021, CC BY-NC-ND.