Evolutionary algorithm with parallel evaluation strategy using constrained penalty-based boundary intersection

Koji Shimoyama, Taiga Kato

研究成果: Conference contribution

抄録

Most of real-world optimization problems correspond to constrained multi-objective optimization problems (CMOPs). Multi-objective evolutionary algorithms (MOEAs) are useful to find out diverse Pareto-optimal solutions in CMOPs, and has been applied in various engineering fields. In order to expand the range of application of MOEAs further, we propose an improved evolutionary algorithm with parallel evaluation strategy (EAPES). In EAPES, feasible solutions and infeasible solution are separately stored in different populations, and infeasible solutions are evaluated in an unusual manner where not only feasible solutions but also useful infeasible solutions are used as parents to reproduce the populations for the next generation. Infeasible solutions are ranked based on a scalarizing function determined by objective function values and a total constraint violation value. This paper investigates the performance of the proposed EAPES to search for Pareto-optimal solutions compared to NSGA-II and the previous EAPES. The proposed EAPES with a well-tuned parameter is most capable to explore Pareto-optimal solutions with good diversity, spread, and convergence to the true Pareto front. The proposed EAPES assigns bad rank to the infeasible solutions that exist away from the true Pareto front, and does not store such solutions. Thus the proposed EAPES exhibits a higher searching capability than the previous EAPES by evaluating infeasible solutions in an appropriate balance between objective functions and total constraint violation. In addition, the results suggest that the proposed EAPES may exhibit high solution search performance even in more difficult CMOPs, which have more objective functions and/or more constraints.

本文言語English
ホスト出版物のタイトル2016 IEEE Congress on Evolutionary Computation, CEC 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3702-3709
ページ数8
ISBN(電子版)9781509006229
DOI
出版ステータスPublished - 2016 11 14
イベント2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada
継続期間: 2016 7 242016 7 29

出版物シリーズ

名前2016 IEEE Congress on Evolutionary Computation, CEC 2016

Other

Other2016 IEEE Congress on Evolutionary Computation, CEC 2016
CountryCanada
CityVancouver
Period16/7/2416/7/29

ASJC Scopus subject areas

  • Artificial Intelligence
  • Modelling and Simulation
  • Computer Science Applications
  • Control and Optimization

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