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

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• 作者：Pengpeng Lin^a ; ^{linp@uwstout.edu" class="auth_mail" title="E-mail the corresponding author} ; Marco A. Contreras^b ; ^{marco.contreras@uky.edu" class="auth_mail" title="E-mail the corresponding author} ; Ruxin Dai^c ; ^{ruxin.dai@uwrf.edu" class="auth_mail" title="E-mail the corresponding author} ; Jun Zhang^d ; ^{jzhang@cs.uky.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Multilevel ; ACO ; Transportation ; Graph-coarsening ; Metaheuristics
• 刊名：Swarm and Evolutionary Computation
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：28
• 期：Complete
• 页码：78-87
• 全文大小：1822 K

文摘

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.