Independent and cooperative parallel search methods for the generalized assignment problem

Yuichi Asahiro, Masahiro Ishibashi, Masafumi Yamashita

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

The generalized assignment problem is a representative NP-hard problem, for which many heuristic algorithms are known. In this article, two parallel heuristic algorithms are proposed, which are based on the ejection chain local search (EC) proposed by Yagiura et al. One is a simple parallelization called multistart parallel EC (MPEC) and the other is cooperative parallel EC (CPEC). In MPEC each search process independently explores search space while in CPEC search processes share partial information to cooperate with each other. The experimental results with 9 computers for large benchmark instances show that (1) MPEC and CPEC, respectively, run twice and 4 times faster than EC, and (2) compared to EC, the difference in quality between obtained solutions and theoretical lower bounds is reduced to 3/4 and 2/3 by MPEC and CPEC, respectively. It is said that these methods give us full benefit of parallelization, speedup and improvement for quality of solutions.

Original languageEnglish
Pages (from-to)129-141
Number of pages13
JournalOptimization Methods and Software
Volume18
Issue number2
DOIs
Publication statusPublished - Apr 1 2003

Fingerprint

Generalized Assignment Problem
Parallel Methods
Heuristic algorithms
Search Methods
Multistart
Parallel algorithms
Computational complexity
Parallelization
Heuristic algorithm
Assignment problem
Partial Information
NP-hard Problems
Parallel Algorithms
Local Search
Search Space
Speedup
Benchmark
Lower bound

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Optimization
  • Applied Mathematics

Cite this

Independent and cooperative parallel search methods for the generalized assignment problem. / Asahiro, Yuichi; Ishibashi, Masahiro; Yamashita, Masafumi.

In: Optimization Methods and Software, Vol. 18, No. 2, 01.04.2003, p. 129-141.

Research output: Contribution to journalArticle

Asahiro, Yuichi ; Ishibashi, Masahiro ; Yamashita, Masafumi. / Independent and cooperative parallel search methods for the generalized assignment problem. In: Optimization Methods and Software. 2003 ; Vol. 18, No. 2. pp. 129-141.
@article{8a4c77497a164e04bcd427ee43624ca5,
title = "Independent and cooperative parallel search methods for the generalized assignment problem",
abstract = "The generalized assignment problem is a representative NP-hard problem, for which many heuristic algorithms are known. In this article, two parallel heuristic algorithms are proposed, which are based on the ejection chain local search (EC) proposed by Yagiura et al. One is a simple parallelization called multistart parallel EC (MPEC) and the other is cooperative parallel EC (CPEC). In MPEC each search process independently explores search space while in CPEC search processes share partial information to cooperate with each other. The experimental results with 9 computers for large benchmark instances show that (1) MPEC and CPEC, respectively, run twice and 4 times faster than EC, and (2) compared to EC, the difference in quality between obtained solutions and theoretical lower bounds is reduced to 3/4 and 2/3 by MPEC and CPEC, respectively. It is said that these methods give us full benefit of parallelization, speedup and improvement for quality of solutions.",
author = "Yuichi Asahiro and Masahiro Ishibashi and Masafumi Yamashita",
year = "2003",
month = "4",
day = "1",
doi = "10.1080/1055678031000107105",
language = "English",
volume = "18",
pages = "129--141",
journal = "Optimization Methods and Software",
issn = "1055-6788",
publisher = "Taylor and Francis Ltd.",
number = "2",

}

TY - JOUR

T1 - Independent and cooperative parallel search methods for the generalized assignment problem

AU - Asahiro, Yuichi

AU - Ishibashi, Masahiro

AU - Yamashita, Masafumi

PY - 2003/4/1

Y1 - 2003/4/1

N2 - The generalized assignment problem is a representative NP-hard problem, for which many heuristic algorithms are known. In this article, two parallel heuristic algorithms are proposed, which are based on the ejection chain local search (EC) proposed by Yagiura et al. One is a simple parallelization called multistart parallel EC (MPEC) and the other is cooperative parallel EC (CPEC). In MPEC each search process independently explores search space while in CPEC search processes share partial information to cooperate with each other. The experimental results with 9 computers for large benchmark instances show that (1) MPEC and CPEC, respectively, run twice and 4 times faster than EC, and (2) compared to EC, the difference in quality between obtained solutions and theoretical lower bounds is reduced to 3/4 and 2/3 by MPEC and CPEC, respectively. It is said that these methods give us full benefit of parallelization, speedup and improvement for quality of solutions.

AB - The generalized assignment problem is a representative NP-hard problem, for which many heuristic algorithms are known. In this article, two parallel heuristic algorithms are proposed, which are based on the ejection chain local search (EC) proposed by Yagiura et al. One is a simple parallelization called multistart parallel EC (MPEC) and the other is cooperative parallel EC (CPEC). In MPEC each search process independently explores search space while in CPEC search processes share partial information to cooperate with each other. The experimental results with 9 computers for large benchmark instances show that (1) MPEC and CPEC, respectively, run twice and 4 times faster than EC, and (2) compared to EC, the difference in quality between obtained solutions and theoretical lower bounds is reduced to 3/4 and 2/3 by MPEC and CPEC, respectively. It is said that these methods give us full benefit of parallelization, speedup and improvement for quality of solutions.

UR - http://www.scopus.com/inward/record.url?scp=0037810924&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0037810924&partnerID=8YFLogxK

U2 - 10.1080/1055678031000107105

DO - 10.1080/1055678031000107105

M3 - Article

AN - SCOPUS:0037810924

VL - 18

SP - 129

EP - 141

JO - Optimization Methods and Software

JF - Optimization Methods and Software

SN - 1055-6788

IS - 2

ER -