A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization

doi:10.1109/TCYB.2019.2896021

A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization

2020-06-01

45

OA

AI

杨圣祥 (Shengxiang Yang)

DOI：10.1109/TCYB.2019.2896021

Original

Save

View PDF

Abstract

En 中文

Dynamic multiobjective optimization (DMO) has gained increasing attention in recent years. Test problems are of great importance in order to facilitate the development of advanced algorithms that can handle dynamic environments well. However, many of the existing dynamic multiobjective test problems have not been rigorously constructed and analyzed, which may induce some unexpected bias when they are used for algorithmic analysis. In this paper, some of these biases are identified after a review of widely used test problems. These include poor scalability of objectives and, more important, problematic overemphasis of static properties rather than dynamics making it difficult to draw accurate conclusion about the strengths and weaknesses of the algorithms studied. A diverse set of dynamics and features is then highlighted that a good test suite should have. We further develop a scalable continuous test suite, which includes a number of dynamics or features that have been rarely considered in literature but frequently occur in real life. It is demonstrated with empirical studies that the proposed test suite is more challenging to the DMO algorithms found in the literature. The test suite can also test algorithms in ways that existing test suites cannot.

Keywords:

Heuristic algorithms

Benchmark testing

Optimization

Shape

Scalability

Cybernetics

Computer science

Adversarial examples

dynamic multiobjective optimization (DMO)

dynamics

Pareto front

scalable test problems

A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization

Abstract

AI Summary

Journal

Organization