Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark

• URL: http://arxiv.org/abs/2107.11019v1
• Date: Fri, 23 Jul 2021 03:57:50 GMT
• Title: Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark
• Authors: Mohammad Nabi Omidvar, Danial Yazdani, Juergen Branke, Xiaodong Li, Shengxiang Yang, Xin Yao
• Abstract summary: 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.
• Score: 9.109331015600185
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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