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A geometric structure based particle swarm optimization algorithm for multi-objective problems

Research output: Contribution to journalArticlepeer-review

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A geometric structure based particle swarm optimization algorithm for multi-objective problems. / Yuan, Wenqiang; Liu, Yusheng; Wang, Hongwei; Cao, Yanlong.

In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 47, No. 9, 01.09.2017, p. 2516-2537.

Research output: Contribution to journalArticlepeer-review

Harvard

Yuan, W, Liu, Y, Wang, H & Cao, Y 2017, 'A geometric structure based particle swarm optimization algorithm for multi-objective problems', IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 9, pp. 2516-2537. https://doi.org/10.1109/TSMC.2016.2523938

APA

Yuan, W., Liu, Y., Wang, H., & Cao, Y. (2017). A geometric structure based particle swarm optimization algorithm for multi-objective problems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(9), 2516-2537. https://doi.org/10.1109/TSMC.2016.2523938

Vancouver

Yuan W, Liu Y, Wang H, Cao Y. A geometric structure based particle swarm optimization algorithm for multi-objective problems. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017 Sep 1;47(9):2516-2537. https://doi.org/10.1109/TSMC.2016.2523938

Author

Yuan, Wenqiang ; Liu, Yusheng ; Wang, Hongwei ; Cao, Yanlong. / A geometric structure based particle swarm optimization algorithm for multi-objective problems. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2017 ; Vol. 47, No. 9. pp. 2516-2537.

Bibtex

@article{0b59be2eb6b348d7b4b12b768dd7ba34,
title = "A geometric structure based particle swarm optimization algorithm for multi-objective problems",
abstract = "This paper presents a novel evolutionary strategy for multiobjective optimization in which a population's evolution is guided by exploiting the geometric structure of its Pareto front. Specifically, the Pareto front of a particle population is regarded as a set of scattered points on which interpolation is performed using a geometric curve/surface model to construct a geometric parameter space. On this basis, the normal direction of this space can be obtained and the solutions located exactly in this direction are chosen as the guiding points. Then, the dominated solutions are processed by using a local optimization technique with the help of these guiding points. Particle populations can thus evolve toward optimal solutions with the guidance of such a geometric structure. The strategy is employed to develop a fast and robust algorithm based on correlation analysis for solving the optimization problems with more than three objectives. A number of computational experiments have been conducted to compare the algorithm to another three popular multiobjective algorithms. As demonstrated in the experiments, the proposed algorithm achieves remarkable performance in terms of the solutions obtained, robustness, and speed of convergence.",
keywords = "Splines (mathematics), surface topography, surface reconstruction, mathematical model, sociology, statistics",
author = "Wenqiang Yuan and Yusheng Liu and Hongwei Wang and Yanlong Cao",
year = "2017",
month = sep,
day = "1",
doi = "10.1109/TSMC.2016.2523938",
language = "English",
volume = "47",
pages = "2516--2537",
journal = "IEEE Transactions on Systems, Man, and Cybernetics: Systems",
issn = "2168-2216",
publisher = "IEEE Advancing Technology for Humanity",
number = "9",

}

RIS

TY - JOUR

T1 - A geometric structure based particle swarm optimization algorithm for multi-objective problems

AU - Yuan, Wenqiang

AU - Liu, Yusheng

AU - Wang, Hongwei

AU - Cao, Yanlong

PY - 2017/9/1

Y1 - 2017/9/1

N2 - This paper presents a novel evolutionary strategy for multiobjective optimization in which a population's evolution is guided by exploiting the geometric structure of its Pareto front. Specifically, the Pareto front of a particle population is regarded as a set of scattered points on which interpolation is performed using a geometric curve/surface model to construct a geometric parameter space. On this basis, the normal direction of this space can be obtained and the solutions located exactly in this direction are chosen as the guiding points. Then, the dominated solutions are processed by using a local optimization technique with the help of these guiding points. Particle populations can thus evolve toward optimal solutions with the guidance of such a geometric structure. The strategy is employed to develop a fast and robust algorithm based on correlation analysis for solving the optimization problems with more than three objectives. A number of computational experiments have been conducted to compare the algorithm to another three popular multiobjective algorithms. As demonstrated in the experiments, the proposed algorithm achieves remarkable performance in terms of the solutions obtained, robustness, and speed of convergence.

AB - This paper presents a novel evolutionary strategy for multiobjective optimization in which a population's evolution is guided by exploiting the geometric structure of its Pareto front. Specifically, the Pareto front of a particle population is regarded as a set of scattered points on which interpolation is performed using a geometric curve/surface model to construct a geometric parameter space. On this basis, the normal direction of this space can be obtained and the solutions located exactly in this direction are chosen as the guiding points. Then, the dominated solutions are processed by using a local optimization technique with the help of these guiding points. Particle populations can thus evolve toward optimal solutions with the guidance of such a geometric structure. The strategy is employed to develop a fast and robust algorithm based on correlation analysis for solving the optimization problems with more than three objectives. A number of computational experiments have been conducted to compare the algorithm to another three popular multiobjective algorithms. As demonstrated in the experiments, the proposed algorithm achieves remarkable performance in terms of the solutions obtained, robustness, and speed of convergence.

KW - Splines (mathematics)

KW - surface topography

KW - surface reconstruction

KW - mathematical model

KW - sociology

KW - statistics

U2 - 10.1109/TSMC.2016.2523938

DO - 10.1109/TSMC.2016.2523938

M3 - Article

VL - 47

SP - 2516

EP - 2537

JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems

JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems

SN - 2168-2216

IS - 9

ER -

ID: 3406667