Hybrid real-code population-based incremental learning and differential evolution for many-objective optimisation of an automotive floor-frame

Pholdee N., Bureerat S., Yildiz A. R.

INTERNATIONAL JOURNAL OF VEHICLE DESIGN, vol.73, no.1-3, pp.20-53, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 73 Issue: 1-3
  • Publication Date: 2017
  • Doi Number: 10.1504/ijvd.2017.082578
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.20-53
  • Keywords: car floor-frame design, many-objective optimisation, population-based incremental learning, differential evolution, topology optimisation, STRUCTURAL DESIGN OPTIMIZATION, PARTICLE SWARM OPTIMIZATION, MULTIOBJECTIVE OPTIMIZATION, TOPOLOGY OPTIMIZATION, ALGORITHM, APPROXIMATE, REDUCTION, TRUSSES
  • Bursa Uludag University Affiliated: Yes


In this paper, a many-objective hybrid real-code population-based incremental learning and differential evolution algorithm (MnRPBILDE) is proposed based on the concept of objective function space reduction. The method is then implemented on real engineering design problems. The topology, shape and sizing design of a simplified automotive floor-frame structure are formulated and used as test problems. A variety of well-established multi-objective evolutionary algorithms (MOEAs) including the original version of MnRPBILDE are employed to solve the test problems while the results are compared based on hypervolume and C indicators. The results indicate that our proposed algorithm outperforms the other MOEAs. The proposed algorithm is effective and efficient for many-objective optimisations of a car floor-frame structure.