Bio-Inspired Algorithms for Many-Objective Discrete Optimization

Bio-inspired approaches have been recently investigated as an alternative to solve intractable multi-objective problems with many objectives. In a recent paper, we proposed a many-objective algorithm named MACO/NDS which is based on ant colony optimization (ACO). Although MACO/NDS has shown to be a...

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Published in:Proceedings (Brazilian Conference on Intelligent Systems. Online) pp. 515 - 520
Main Authors: Martins, Luiz G.A., Franca, Tiago P., de Oliveira, Gina M.B.
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2019
Subjects:
Ant colony optimization
Approximation algorithms
discrete problems
Evolutionary algorithms
Evolutionary computation
Many objective algorithms
Materials requirements planning
Measurement
Optimization
Quality of service
Routing
ISSN:2643-6264
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Abstract Bio-inspired approaches have been recently investigated as an alternative to solve intractable multi-objective problems with many objectives. In a recent paper, we proposed a many-objective algorithm named MACO/NDS which is based on ant colony optimization (ACO). Although MACO/NDS has shown to be a competitive method against four multi-objective evolutionary algorithms (MOEAs), some open questions still remain. How would MACO/NDS behave when applied to highly complex problems where the Pareto front has a very large cardinality? Would it be well evaluated by a nondependent Pareto metric such as hypervolume? Furthermore, it should be clarified whether the good performance of MACO/NDS is due to its ACO subjacent framework or it is due to its underlying mechanisms to deal with many objectives. This last question is part of a broader investigation about the adequacy of ACOs and MOEAs to solve discrete optimization problems. Therefore, in the present paper, we use complex instances of multicast routing and multi-objective knapsack problems to compare the performance of three MOEAs (MOEA/D, NSGA-III and MEANDS) and three many-ACOs (MACO/NDS, MOACS and MOEA/D-ACO). MACO/NDS achieved good performance in both problems and it has a more balanced behaviour than the others.
AbstractList Bio-inspired approaches have been recently investigated as an alternative to solve intractable multi-objective problems with many objectives. In a recent paper, we proposed a many-objective algorithm named MACO/NDS which is based on ant colony optimization (ACO). Although MACO/NDS has shown to be a competitive method against four multi-objective evolutionary algorithms (MOEAs), some open questions still remain. How would MACO/NDS behave when applied to highly complex problems where the Pareto front has a very large cardinality? Would it be well evaluated by a nondependent Pareto metric such as hypervolume? Furthermore, it should be clarified whether the good performance of MACO/NDS is due to its ACO subjacent framework or it is due to its underlying mechanisms to deal with many objectives. This last question is part of a broader investigation about the adequacy of ACOs and MOEAs to solve discrete optimization problems. Therefore, in the present paper, we use complex instances of multicast routing and multi-objective knapsack problems to compare the performance of three MOEAs (MOEA/D, NSGA-III and MEANDS) and three many-ACOs (MACO/NDS, MOACS and MOEA/D-ACO). MACO/NDS achieved good performance in both problems and it has a more balanced behaviour than the others.
Author Franca, Tiago P.
de Oliveira, Gina M.B.
Martins, Luiz G.A.
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  givenname: Luiz G.A.
  surname: Martins
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  givenname: Tiago P.
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  givenname: Gina M.B.
  surname: de Oliveira
  fullname: de Oliveira, Gina M.B.
  email: gina@ufu.br
  organization: Faculty of Computing, Federal University of Uberlandia, Uberlandia, Brazil
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Snippet Bio-inspired approaches have been recently investigated as an alternative to solve intractable multi-objective problems with many objectives. In a recent...
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StartPage 515
SubjectTerms Ant colony optimization
Approximation algorithms
discrete problems
Evolutionary algorithms
Evolutionary computation
Many objective algorithms
Materials requirements planning
Measurement
Optimization
Quality of service
Routing
Title Bio-Inspired Algorithms for Many-Objective Discrete Optimization
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