An evolutionary algorithm based hyper-heuristic framework for the set packing problem

Sachchida Nand Chaurasia, Joong Hoon Kim

Research output: Contribution to journalArticle

Abstract

In recent years, hyper-heuristics have received massive attention from the research community as an alternative of meta-heuristics. In a hyper-heuristic, generation or selection of an effective heuristic among a pool of heuristics is an important and challenging task in the search process. At each iteration, a suitable heuristic can take the search process toward the global optimal solution. Moreover, some additional factors such as quality and the number of heuristics also affect the performance. In this paper, we propose an evolutionary algorithm based hyper-heuristic framework that incorporates dynamic selection of parameters. To test its generality, effectiveness and robustness, we apply this approach on two different NP-hard problems - set packing problem (SPP) and minimum weight dominating set (MWDS) problem. The proposed approach for the SPP and the MWDS problem has been evaluated respectively on their respective set of benchmark instances. Computational results show that the proposed approach for the SPP and MWDS problem perform much better than their respective state-of-the-art approaches in terms of the solution quality and computational time.

Original languageEnglish
Pages (from-to)1-31
Number of pages31
JournalInformation Sciences
Volume505
DOIs
Publication statusPublished - 2019 Dec 1

Fingerprint

Set Packing
Hyper-heuristics
Packing Problem
Evolutionary algorithms
Evolutionary Algorithms
Dominating Set
Heuristics
Computational complexity
NP-hard Problems
Metaheuristics
Computational Results
Optimal Solution
Benchmark
Robustness
Iteration
Framework
Alternatives

Keywords

  • Estimation of distribution algorithm
  • Guided-mutation
  • Heuristic
  • Hyper-heuristic
  • Minimum weight dominating set problem
  • Set packing problem

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Cite this

An evolutionary algorithm based hyper-heuristic framework for the set packing problem. / Chaurasia, Sachchida Nand; Kim, Joong Hoon.

In: Information Sciences, Vol. 505, 01.12.2019, p. 1-31.

Research output: Contribution to journalArticle

@article{81d82cb708034cd5904594068dd20c59,
title = "An evolutionary algorithm based hyper-heuristic framework for the set packing problem",
abstract = "In recent years, hyper-heuristics have received massive attention from the research community as an alternative of meta-heuristics. In a hyper-heuristic, generation or selection of an effective heuristic among a pool of heuristics is an important and challenging task in the search process. At each iteration, a suitable heuristic can take the search process toward the global optimal solution. Moreover, some additional factors such as quality and the number of heuristics also affect the performance. In this paper, we propose an evolutionary algorithm based hyper-heuristic framework that incorporates dynamic selection of parameters. To test its generality, effectiveness and robustness, we apply this approach on two different NP-hard problems - set packing problem (SPP) and minimum weight dominating set (MWDS) problem. The proposed approach for the SPP and the MWDS problem has been evaluated respectively on their respective set of benchmark instances. Computational results show that the proposed approach for the SPP and MWDS problem perform much better than their respective state-of-the-art approaches in terms of the solution quality and computational time.",
keywords = "Estimation of distribution algorithm, Guided-mutation, Heuristic, Hyper-heuristic, Minimum weight dominating set problem, Set packing problem",
author = "Chaurasia, {Sachchida Nand} and Kim, {Joong Hoon}",
year = "2019",
month = "12",
day = "1",
doi = "10.1016/j.ins.2019.07.073",
language = "English",
volume = "505",
pages = "1--31",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier Inc.",

}

TY - JOUR

T1 - An evolutionary algorithm based hyper-heuristic framework for the set packing problem

AU - Chaurasia, Sachchida Nand

AU - Kim, Joong Hoon

PY - 2019/12/1

Y1 - 2019/12/1

N2 - In recent years, hyper-heuristics have received massive attention from the research community as an alternative of meta-heuristics. In a hyper-heuristic, generation or selection of an effective heuristic among a pool of heuristics is an important and challenging task in the search process. At each iteration, a suitable heuristic can take the search process toward the global optimal solution. Moreover, some additional factors such as quality and the number of heuristics also affect the performance. In this paper, we propose an evolutionary algorithm based hyper-heuristic framework that incorporates dynamic selection of parameters. To test its generality, effectiveness and robustness, we apply this approach on two different NP-hard problems - set packing problem (SPP) and minimum weight dominating set (MWDS) problem. The proposed approach for the SPP and the MWDS problem has been evaluated respectively on their respective set of benchmark instances. Computational results show that the proposed approach for the SPP and MWDS problem perform much better than their respective state-of-the-art approaches in terms of the solution quality and computational time.

AB - In recent years, hyper-heuristics have received massive attention from the research community as an alternative of meta-heuristics. In a hyper-heuristic, generation or selection of an effective heuristic among a pool of heuristics is an important and challenging task in the search process. At each iteration, a suitable heuristic can take the search process toward the global optimal solution. Moreover, some additional factors such as quality and the number of heuristics also affect the performance. In this paper, we propose an evolutionary algorithm based hyper-heuristic framework that incorporates dynamic selection of parameters. To test its generality, effectiveness and robustness, we apply this approach on two different NP-hard problems - set packing problem (SPP) and minimum weight dominating set (MWDS) problem. The proposed approach for the SPP and the MWDS problem has been evaluated respectively on their respective set of benchmark instances. Computational results show that the proposed approach for the SPP and MWDS problem perform much better than their respective state-of-the-art approaches in terms of the solution quality and computational time.

KW - Estimation of distribution algorithm

KW - Guided-mutation

KW - Heuristic

KW - Hyper-heuristic

KW - Minimum weight dominating set problem

KW - Set packing problem

UR - http://www.scopus.com/inward/record.url?scp=85069721292&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069721292&partnerID=8YFLogxK

U2 - 10.1016/j.ins.2019.07.073

DO - 10.1016/j.ins.2019.07.073

M3 - Article

VL - 505

SP - 1

EP - 31

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

ER -