A Multi-agent Transgenetic Algorithm for the Bi-objective Spanning Tree Problem

Fernandes, Islame F. C.; Maia, Sílvia M. D. M.; Goldbarg, Elizabeth Ferreira Gouvêa; Goldbarg, Marco César

doi:10.1016/j.entcs.2019.08.040

Cited by 2 publications

(1 citation statement)

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“…In the context of bi-objective MST problems, Fernandes et al (2019) propose the application of a transgenetic algorithm, where so-called transgenetic agents are responsible for exchanging solution information and recombining them to new bi-objective MST solutions using weighting of objectives and single-objective MST approaches to construct new MSTs. Previous work by the authors of this paper (Bossek and Grimme, 2017) specifically focuses on mutation operators in standard meta-heuristics and introduces a heavily problem-tailored mutation operator -called the sub-graph mutation (SG) -for the bi-objective MST problem.…”

Section: Introductionmentioning

confidence: 99%

A pareto-beneficial sub-tree mutation for the multi-criteria minimum spanning tree problem

Bossek

Grimme

2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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We contribute to the efficient approximation of the Pareto-set for the classical N Phard multi-objective minimum spanning tree problem (moMST) adopting evolutionary computation. More precisely, by building upon preliminary work, we analyse the neighborhood structure of Pareto-optimal spanning trees and design several highly biased sub-graph-based mutation operators founded on the gained insights. In a nutshell, these operators replace (un)connected sub-trees of candidate solutions with locally optimal sub-trees. The latter (biased) step is realized by applying Kruskal's single-objective MST algorithm to a weighted sum scalarization of a sub-graph.We prove runtime complexity results for the introduced operators and investigate the desirable Pareto-beneficial property. This property states that mutants cannot be dominated by their parent. Moreover, we perform an extensive experimental benchmark study to showcase the operator's practical suitability. Our results confirm that the subgraph based operators beat baseline algorithms from the literature even with severely restricted computational budget in terms of function evaluations on four different classes of complete graphs with different shapes of the Pareto-front.

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Section: Introductionmentioning

confidence: 99%