Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark
This provides a standardized benchmark suite for researchers in dynamic optimization, enabling consistent evaluation and competition, though it is incremental as it builds on existing moving peaks benchmarks.
The authors introduced the Generalized Moving Peaks Benchmark (GMPB) to generate problem instances for continuous large-scale dynamic optimization, providing 15 benchmark problems with source code and a performance indicator for comparative studies and competitions.
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.