基于广义移动峰值基准的大规模动态最优化问题求解
Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark
摘要：
本文介绍了广义移动峰值基准（GMPB），以及如何使用它来生成连续大规模动态最优化问题的问题实例。它提供了一组15个基准问题，相关的信源编码，和一个性能指标，旨在比较研究和竞争的大规模动态最优化。虽然它的主要目的是提供一个连贯的基础上运行的比赛，其一般性允许感兴趣的读者使用本文档作为指导，设计定制的问题实例，调查的问题超出了所提出的基准套件的范围。为此，我们解释了GMPB的模块化结构，以及它的组成部分如何组装形成具有各种可控特性的问题实例，这些特性包括从单峰到高度多峰，从对称到高度不对称，从光滑到高度不规则，以及各种程度的变量相互作用和病态。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
