A Benchmark-Suite of real-World constrained multi-objective optimization problems and some baseline results

Generally, Synthetic Benchmark Problems (SBPs) are utilized to assess the performance of metaheuristics. However, these SBPs may include various unrealistic properties. As a consequence, performance assessment may lead to underestimation or overestimation. To address this issue, few benchmark suites...

Full description

Saved in:
Bibliographic Details
Published in:Swarm and evolutionary computation Vol. 67; p. 100961
Main Authors: Kumar, Abhishek, Wu, Guohua, Ali, Mostafa Z., Luo, Qizhang, Mallipeddi, Rammohan, Suganthan, Ponnuthurai Nagaratnam, Das, Swagatam
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2021
Subjects:
Benchmark-Suite
Metaheuristics
Multi-Objective constrained optimization
Performance assessment
Ranking scheme
Real-World problems
Real-World problems
Multi-Objective constrained optimization
Benchmark-Suite
Ranking scheme
Metaheuristics
Performance assessment
ISSN:2210-6502
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Generally, Synthetic Benchmark Problems (SBPs) are utilized to assess the performance of metaheuristics. However, these SBPs may include various unrealistic properties. As a consequence, performance assessment may lead to underestimation or overestimation. To address this issue, few benchmark suites containing real-world problems have been proposed for all kinds of metaheuristics except for Constrained Multi-objective Metaheuristics (CMOMs). To fill this gap, we develop a benchmark suite of Real-world Constrained Multi-objective Optimization Problems (RWCMOPs) for performance assessment of CMOMs. This benchmark suite includes 50 problems collected from various streams of research. We also present the baseline results of this benchmark suite by using state-of-the-art algorithms. Besides, for comparative analysis, a ranking scheme is also proposed.
ISSN:2210-6502
DOI:10.1016/j.swevo.2021.100961