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MOEA/D with a delaunay triangulation based weight adjustment

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JingXuan WeiAuthors Info & Claims

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 93 - 94

https://doi.org/10.1145/2598394.2598416

Published: 12 July 2014 Publication History

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Abstract

MOEA/D decomposes a multi-objective optimization problem (MOP) into a set of scalar sub-problems with evenly spread weight vectors. Recent studies have shown that the fixed weight vectors used in MOEA/D might not be able to cover the whole Pareto front (PF) very well. Due to this, we developed an adaptive weight adjustment method in our previous work by removing subproblems from the crowded parts of the PF and adding new ones into the sparse parts. Although it performs well, we found that the sparse measurement of a subproblem which is determined by the m-nearest (m is the dimensional of the object space) neighbors of its solution can be more appropriately defined. In this work, the neighborhood relationship between subproblems is defined by using Delaunay triangulation (DT) of the points in the population.

References

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K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182--197, 2002. DOI= http://dx.doi.org/10.1109/4235.996017.

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[2]

H. Liu, F. Gu, and Y. Cheung. T-MOEA/D: MOEA/D with objective transform in multi-objective problems. In Proceedings of the International Conference of Information Science and Management Engineering, pages 282--285, 2010. DOI= http://dx.doi.org/10.1109/isme.2010.274.

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Y. Qi, X. Ma, F. Liu, L. Jiao, and J. Wu. An adaptive weight vector adjustment based multi-objective evolutionary algorithm. MIT Evolutionary Computation, 2013. In Press, DOI= http://doi.acm.org/10.1162/EVCO a 00109.

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Q. Zhang and H. Li. MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6):712--731, 2007. DOI= http://dx.doi.org/10.1109/tevc.2007.892759.

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Liu DQi YYang RQuan YLi XMiao Q(2021)A tri-objective preference-based uniform weight design method using Delaunay triangulationSoft Computing10.1007/s00500-021-05868-125:15(9703-9729)Online publication date: 3-Jul-2021
https://doi.org/10.1007/s00500-021-05868-1
Xu QXu ZMa T(2020)A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future DirectionsIEEE Access10.1109/ACCESS.2020.29736708(41588-41614)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2973670
Pilat MNeruda R(2016)General tuning of weights in MOEA/D2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7743894(965-972)Online publication date: Jul-2016
https://doi.org/10.1109/CEC.2016.7743894

Index Terms

MOEA/D with a delaunay triangulation based weight adjustment
1. Computing methodologies

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Moea/d with adaptive weight adjustment

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MOEA/D, a representative multi-objective evolutionary algorithm, decomposes a multi-objective optimization problem into a number of single objective optimization problems and tries to approximate Pareto front by simultaneously optimizing each of these ...

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Published In

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1524 pages

ISBN:9781450328814

DOI:10.1145/2598394

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

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Published: 12 July 2014

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Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
Program for Cheung Kong Scholars and Innovative Research Team in University
Fund for Foreign Scholars in University Research and Teaching Programs

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GECCO '14

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SIGEVO

GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

BC, Vancouver, Canada

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GECCO Comp '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Liu DQi YYang RQuan YLi XMiao Q(2021)A tri-objective preference-based uniform weight design method using Delaunay triangulationSoft Computing10.1007/s00500-021-05868-125:15(9703-9729)Online publication date: 3-Jul-2021
https://doi.org/10.1007/s00500-021-05868-1
Xu QXu ZMa T(2020)A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future DirectionsIEEE Access10.1109/ACCESS.2020.29736708(41588-41614)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2973670
Pilat MNeruda R(2016)General tuning of weights in MOEA/D2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7743894(965-972)Online publication date: Jul-2016
https://doi.org/10.1109/CEC.2016.7743894

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Moea/d with adaptive weight adjustment

A tri-objective preference-based uniform weight design method using Delaunay triangulation

Adaptive Update Range of Solutions in MOEA/D for Multi and Many-Objective Optimization

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Moea/d with adaptive weight adjustment

A tri-objective preference-based uniform weight design method using Delaunay triangulation

Adaptive Update Range of Solutions in MOEA/D for Multi and Many-Objective Optimization

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