Solving Many-Objective Optimization Problems Using Selection Hyper-Heuristics

Adeem Ali Anwar, Guanfeng Liu, Xuyun Zhang

2024

Abstract

To effectively solve discrete optimization problems, meta-heuristics and heuristics have been used but their performance suffers drastically in the cross-domain applications. Hence, hyper-heuristics (HHs) have been used to cater to cross-domain problems. In literature, different HHs and meta-heuristics have been applied to solve the Many-objective Job-Shop Scheduling problem (MaOJSSP) and Many-objective Knapsack problem (MaOKSP) but the results are not convincing. Furthermore, no researchers have tried to solve these problems as cross-domain together using HHs. Additionally, the considered HH known as the cricket-based selection hyper-heuristic (CB-SHH) has not applied to any variation of the Job-shop scheduling problem (JSP) and the knapsack problem (KSP). This paper compares the performance of recently proposed HHs named CB-SHH, H-ACO, MARP-NSGAIII, and meta-heuristics named MPMOGA, MOEA/D on MaOKSP, MaOJSSP and benchmark problems. The performance of state-of-the-art HHs and meta-heuristics have been compared using hypervolume (HV) and µ norm. The main contribution of the paper is to effectively solve the MaOJSSP and MaOKSP using HHs and to prove the effectiveness of the best HHs on benchmark problems. It is proven through experiments that the CB-SHH is the best-performing algorithm on 44 out of 48 instances across all datasets and is the best cross-domain algorithm across the datasets.

Paper Citation

in Harvard Style

Anwar A., Liu G. and Zhang X. (2024). Solving Many-Objective Optimization Problems Using Selection Hyper-Heuristics. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 194-201. DOI: 10.5220/0012314400003636

in Bibtex Style

@conference{icaart24,
author={Adeem Ali Anwar and Guanfeng Liu and Xuyun Zhang},
title={Solving Many-Objective Optimization Problems Using Selection Hyper-Heuristics},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={194-201},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012314400003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Solving Many-Objective Optimization Problems Using Selection Hyper-Heuristics
SN - 978-989-758-680-4
AU - Anwar A.
AU - Liu G.
AU - Zhang X.
PY - 2024
SP - 194
EP - 201
DO - 10.5220/0012314400003636
PB - SciTePress