Soft Computing

, Volume 18, Issue 11, pp 2119–2134 | Cite as

DMEA-II: the direction-based multi-objective evolutionary algorithm-II

  • Long Nguyen
  • Lam T. BuiEmail author
  • Hussein A. Abbass
Methodologies and Application


This paper discusses the use of direction of improvement in guiding multi-objective evolutionary algorithms (MOEAs) during the search process towards the area of Pareto optimal set. We particularly propose a new version of the Direction based Multi-objective Evolutionary Algorithm (DMEA) and name it as DMEA-II. The new features of DMEA-II includes (1) an adaptation of the balance between convergence and spreading by using an adaptive ratio between the convergence and spreading directions being selected over time; (2) a new concept of ray-based density for niching; and (3) a new selection scheme based on the ray-based density for selecting solutions for the next generation. To validate the performance of DMEA-II, we carried out a case study on a wide range of test problems and comparison with other MOEAs. It obtained quite good results on primary performance metrics, namely the generation distance, inverse generation distance, hypervolume and the coverage set. Our analysis on the results indicates the better performance of DMEA-II in comparison with the most popular MOEAs.


Multi-objective evolutionary algorithms Direction-based MOEAs Performance measurement DMEA 



We acknowledge the financial support from Vietnam’s National Foundation for Science and Technology (NAFOSTED) Grant No. 102.01-2010.12.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Information TechnologyLe Quy Don Technical UniversityHa NoiVietnam
  2. 2.University of New South Wales at Australian Defence Force AcademyCanberraAustralia

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