About | Contact Us | Register | Login
ProceedingsSeriesJournalsSearchEAI
inis 20(25): e1

Research Article

Optimizing the Modified Lam Annealing Schedule

Download1166 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/eai.16-12-2020.167653,
        author={Vincent A. Cicirello},
        title={Optimizing the Modified Lam Annealing Schedule},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={7},
        number={25},
        publisher={EAI},
        journal_a={INIS},
        year={2020},
        month={12},
        keywords={simulated annealing, modified Lam, self-adaptive, parameter-free, combinatorial optimization},
        doi={10.4108/eai.16-12-2020.167653}
    }
    
  • Vincent A. Cicirello
    Year: 2020
    Optimizing the Modified Lam Annealing Schedule
    INIS
    EAI
    DOI: 10.4108/eai.16-12-2020.167653
Vincent A. Cicirello1,*
  • 1: Computer Science, Stockton University, 101 Vera King Farris Drive, Galloway, NJ 08205
*Contact email: vincent.cicirello@stockton.edu

Abstract

Simulated annealing is a metaheuristic commonly used for combinatorial optimization in many industrial applications. Its runtime behavior is controlled by an algorithmic component known as the annealing schedule. The classic annealing schedules have control parameters that must be set or tuned ahead of time. Adaptive annealing schedules, such as the Modified Lam, are parameter-free and self-adapt during runtime. However, they are also more complex than the classic alternatives, leading to more time per iteration. In this paper, we present an optimized variant of Modified Lam annealing, and experimentally demonstrate the potential significant impact on runtime performance of carefully optimizing the annealing schedule.

Keywords
simulated annealing, modified Lam, self-adaptive, parameter-free, combinatorial optimization
Received
2020-10-07
Accepted
2020-12-03
Published
2020-12-16
Publisher
EAI
http://dx.doi.org/10.4108/eai.16-12-2020.167653

Copyright © 2020 Vincent A. Cicirello, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

EBSCOProQuestDBLPDOAJPortico
EAI Logo

About EAI

  • Who We Are
  • Leadership
  • Research Areas
  • Partners
  • Media Center

Community

  • Membership
  • Conference
  • Recognition
  • Sponsor Us

Publish with EAI

  • Publishing
  • Journals
  • Proceedings
  • Books
  • EUDL