A multilevel ACO approach for solving forest transportation planning problems with environmental constraints

Pengpeng Lin, Marco A. Contreras, Ruxin Dai, Jun Zhang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

This paper presents a multilevel ant colony optimization (MLACO) approach to solve constrained forest transportation planning problems (CFTPPs). A graph coarsening technique is used to coarsen a network representing the problem into a set of increasingly coarser level problems. Then, a customized ant colony optimization (ACO) algorithm is designed to solve the CFTPP from coarser to finer level problems. The parameters of the ACO algorithm are automatically configured by evaluating a parameter combination domain through each level of the problem. The solution obtained by the ACO for the coarser level problems is projected into finer level problem components, which are used to help the ACO search for finer level solutions. The MLACO was tested on 20 CFTPPs and solutions were compared to those obtained from other approaches including a mixed integer programming (MIP) solver, a parameter iterative local search (ParamILS) method, and an exhaustive ACO parameter search method. Experimental results showed that the MLACO approach was able to match solution qualities and reduce computing time significantly compared to the tested approaches.

Original languageEnglish
Pages (from-to)78-87
Number of pages10
JournalSwarm and Evolutionary Computation
Volume28
DOIs
StatePublished - Jun 1 2016

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V.

Keywords

  • ACO
  • Graph-coarsening
  • Metaheuristics
  • Multilevel
  • Transportation

ASJC Scopus subject areas

  • Computer Science (all)
  • Mathematics (all)

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