Multiple-objective Optimization Based on a Two-time-scale Neurodynamic System

Shaofu Yang; Jun WANG; Qingshan Liu

doi:10.1109/ICACI.2016.7449825

Multiple-objective Optimization Based on a Two-time-scale Neurodynamic System

Shaofu Yang^*
, Jun WANG
, Qingshan Liu

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

In this paper, a framework of neurodynamic system consisting of two subsystems with different time-scale is firstly developed for solving multi-objective convex optimization problems. By designing a continuous-time dynamic for weight associated with each single objective function, the multi-objective optimization problem is turned to an optimization problem with a single time-varying objective function. Then the neurodynamic model can be formulated by combining the weight dynamic with existing neurodynamic models for single-objective optimization. By setting different time scale for the two subsystem, it is shown that the trajectory of the state of the neurodynamic system can approximate the whole pareto front well in bi-objective optimization problems. For the many-objective optimization problem, by designing proper dynamics for weight vectors, the whole Pareto-front can be well approximated by a curve generated from the neurodynamic system. Finally, numerical simulation is presented to illustrate the neurodynamic approaches.

Original language	English
Title of host publication	2016 EIGHTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI)
Publisher	IEEE
Pages	193-199
DOIs	https://doi.org/10.1109/ICACI.2016.7449825
Publication status	Published - Feb 2016
Event	8th International Conference on Advanced Computational Intelligence (ICACI) - Chiang Mai, Thailand Duration: 14 Feb 2016 → 16 Feb 2016

Conference

Conference	8th International Conference on Advanced Computational Intelligence (ICACI)
Place	Thailand
City	Chiang Mai
Period	14/02/16 → 16/02/16

Research Keywords

Multi-objective optimization
neurodynamic approach
weighted sum method
two-time scale
RECURRENT NEURAL-NETWORK
LIMITING ACTIVATION FUNCTION
WEIGHTED-SUM METHOD
MULTIOBJECTIVE OPTIMIZATION
CONSTRAINED OPTIMIZATION
VARIATIONAL-INEQUALITIES
PROGRAMMING PROBLEMS
GENERATION

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10.1109/ICACI.2016.7449825

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@inproceedings{37c919b2744544418b9a0314f774f1dd,

title = "Multiple-objective Optimization Based on a Two-time-scale Neurodynamic System",

abstract = "In this paper, a framework of neurodynamic system consisting of two subsystems with different time-scale is firstly developed for solving multi-objective convex optimization problems. By designing a continuous-time dynamic for weight associated with each single objective function, the multi-objective optimization problem is turned to an optimization problem with a single time-varying objective function. Then the neurodynamic model can be formulated by combining the weight dynamic with existing neurodynamic models for single-objective optimization. By setting different time scale for the two subsystem, it is shown that the trajectory of the state of the neurodynamic system can approximate the whole pareto front well in bi-objective optimization problems. For the many-objective optimization problem, by designing proper dynamics for weight vectors, the whole Pareto-front can be well approximated by a curve generated from the neurodynamic system. Finally, numerical simulation is presented to illustrate the neurodynamic approaches.",

keywords = "Multi-objective optimization, neurodynamic approach, weighted sum method, two-time scale, RECURRENT NEURAL-NETWORK, LIMITING ACTIVATION FUNCTION, WEIGHTED-SUM METHOD, MULTIOBJECTIVE OPTIMIZATION, CONSTRAINED OPTIMIZATION, VARIATIONAL-INEQUALITIES, PROGRAMMING PROBLEMS, GENERATION",

author = "Shaofu Yang and Jun WANG and Qingshan Liu",

year = "2016",

month = feb,

doi = "10.1109/ICACI.2016.7449825",

language = "English",

pages = "193--199",

booktitle = "2016 EIGHTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI)",

publisher = "IEEE",

address = "United States",

note = "8th International Conference on Advanced Computational Intelligence (ICACI) ; Conference date: 14-02-2016 Through 16-02-2016",

}

Multiple-objective Optimization Based on a Two-time-scale Neurodynamic System. / Yang, Shaofu; WANG, Jun; Liu, Qingshan.
2016 EIGHTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI). IEEE, 2016. p. 193-199.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Multiple-objective Optimization Based on a Two-time-scale Neurodynamic System

AU - Yang, Shaofu

AU - WANG, Jun

AU - Liu, Qingshan

PY - 2016/2

Y1 - 2016/2

N2 - In this paper, a framework of neurodynamic system consisting of two subsystems with different time-scale is firstly developed for solving multi-objective convex optimization problems. By designing a continuous-time dynamic for weight associated with each single objective function, the multi-objective optimization problem is turned to an optimization problem with a single time-varying objective function. Then the neurodynamic model can be formulated by combining the weight dynamic with existing neurodynamic models for single-objective optimization. By setting different time scale for the two subsystem, it is shown that the trajectory of the state of the neurodynamic system can approximate the whole pareto front well in bi-objective optimization problems. For the many-objective optimization problem, by designing proper dynamics for weight vectors, the whole Pareto-front can be well approximated by a curve generated from the neurodynamic system. Finally, numerical simulation is presented to illustrate the neurodynamic approaches.

AB - In this paper, a framework of neurodynamic system consisting of two subsystems with different time-scale is firstly developed for solving multi-objective convex optimization problems. By designing a continuous-time dynamic for weight associated with each single objective function, the multi-objective optimization problem is turned to an optimization problem with a single time-varying objective function. Then the neurodynamic model can be formulated by combining the weight dynamic with existing neurodynamic models for single-objective optimization. By setting different time scale for the two subsystem, it is shown that the trajectory of the state of the neurodynamic system can approximate the whole pareto front well in bi-objective optimization problems. For the many-objective optimization problem, by designing proper dynamics for weight vectors, the whole Pareto-front can be well approximated by a curve generated from the neurodynamic system. Finally, numerical simulation is presented to illustrate the neurodynamic approaches.

KW - Multi-objective optimization

KW - neurodynamic approach

KW - weighted sum method

KW - two-time scale

KW - RECURRENT NEURAL-NETWORK

KW - LIMITING ACTIVATION FUNCTION

KW - WEIGHTED-SUM METHOD

KW - MULTIOBJECTIVE OPTIMIZATION

KW - CONSTRAINED OPTIMIZATION

KW - VARIATIONAL-INEQUALITIES

KW - PROGRAMMING PROBLEMS

KW - GENERATION

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U2 - 10.1109/ICACI.2016.7449825

DO - 10.1109/ICACI.2016.7449825

M3 - RGC 32 - Refereed conference paper (with host publication)

SP - 193

EP - 199

BT - 2016 EIGHTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI)

PB - IEEE

T2 - 8th International Conference on Advanced Computational Intelligence (ICACI)

Y2 - 14 February 2016 through 16 February 2016

ER -

Multiple-objective Optimization Based on a Two-time-scale Neurodynamic System

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