2021/07/23 by Mohammad Nabi Omidvar, Danial Yazdani, Omidvar, Mohammad Nabi +9
Computer Science · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Artificial Intelligence (cs.AI) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #FOS: Mathematics #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #Optimization and Control (math.OC) #cs.AI #cs.NE #math.OC
paper · pdf · doi:10.48550/arxiv.2107.11019
arXiv admin note: text overlap with arXiv:2106.06174
arxiv created 2021/07/23 · openalex publication_date 2021/07/23 · arxiv updated 2021/07/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems. It presents a set of 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.