A bi-objective hybrid constrained optimization (HyCon) method using a multi-objective and penalty function approach

Rituparna Datta, Kalyanmoy Deb, Aviv Segev

Research output: ResearchConference contribution

Abstract

Single objective evolutionary constrained optimization has been widely researched by plethora of researchers in the last two decades whereas multi-objective constraint handling using evolutionary algorithms has not been actively proposed. However, real-world multi-objective optimization problems consist of one or many non-linear and non-convex constraints. In the present work, we develop an evolutionary algorithm based on hybrid constraint handling methodology (HyCon) to deal with constraints in bi-objective optimization problems. HyCon is a combination of an Evolutionary Multi-objective Optimization (EMO) coupled with classical weighted sum approach and is an extended version of our previously developed constraint handling method for single objective optimization. A constrained bi-objective problem is converted into a tri-objective problem where the additional objective is formed using summation of constrained violation. The performance of HyCon is tested on four constrained bi-objective problems. The non-dominated solutions are compared with a standard evolutionary multi-objective optimization algorithm (NSGA-II) with respect to hypervolume and attainment surface. The simulation results illustrates the effectiveness of the HyCon method. The HyCon either outperformed or produced similar performance as compared to NSGA-II.

LanguageEnglish (US)
Title of host publication2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages317-324
Number of pages8
ISBN (Electronic)9781509046010
DOIs
StatePublished - Jul 5 2017
Event2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Donostia-San Sebastian, Spain
Duration: Jun 5 2017Jun 8 2017

Other

Other2017 IEEE Congress on Evolutionary Computation, CEC 2017
CountrySpain
CityDonostia-San Sebastian
Period6/5/176/8/17

Profile

Constrained optimization
Multiobjective optimization
Evolutionary algorithms

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing

Cite this

Datta, R., Deb, K., & Segev, A. (2017). A bi-objective hybrid constrained optimization (HyCon) method using a multi-objective and penalty function approach. In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings (pp. 317-324). [7969329] Institute of Electrical and Electronics Engineers Inc.. DOI: 10.1109/CEC.2017.7969329

A bi-objective hybrid constrained optimization (HyCon) method using a multi-objective and penalty function approach. / Datta, Rituparna; Deb, Kalyanmoy; Segev, Aviv.

2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 317-324 7969329.

Research output: ResearchConference contribution

Datta, R, Deb, K & Segev, A 2017, A bi-objective hybrid constrained optimization (HyCon) method using a multi-objective and penalty function approach. in 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings., 7969329, Institute of Electrical and Electronics Engineers Inc., pp. 317-324, 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia-San Sebastian, Spain, 6/5/17. DOI: 10.1109/CEC.2017.7969329
Datta R, Deb K, Segev A. A bi-objective hybrid constrained optimization (HyCon) method using a multi-objective and penalty function approach. In 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc.2017. p. 317-324. 7969329. Available from, DOI: 10.1109/CEC.2017.7969329
Datta, Rituparna ; Deb, Kalyanmoy ; Segev, Aviv. / A bi-objective hybrid constrained optimization (HyCon) method using a multi-objective and penalty function approach. 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 317-324
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