An evolutionary many-objective optimization algorithm based on dominance and decomposition

Ke LI, Kalyanmoy DEB, Qingfu ZHANG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

929 Citations (Scopus)


Achieving balance between convergence and diversity is a key issue in evolutionary multiobjective optimization. Most existing methodologies, which have demonstrated their niche on various practical problems involving two and three objectives, face significant challenges in many-objective optimization. This paper suggests a unified paradigm, which combines dominance- and decomposition-based approaches, for many-objective optimization. Our major purpose is to exploit the merits of both dominance- and decomposition-based approaches to balance the convergence and diversity of the evolutionary process. The performance of our proposed method is validated and compared with four state-of-The-art algorithms on a number of unconstrained benchmark problems with up to 15 objectives. Empirical results fully demonstrate the superiority of our proposed method on all considered test instances. In addition, we extend this method to solve constrained problems having a large number of objectives. Compared to two other recently proposed constrained optimizers, our proposed method shows highly competitive performance on all the constrained optimization problems.
Original languageEnglish
Pages (from-to)694-716
JournalIEEE Transactions on Evolutionary Computation
Issue number5
Early online date21 Nov 2014
Publication statusPublished - Oct 2015
Externally publishedYes


  • constrained optimization
  • decomposition
  • evolutionary computation
  • Many-objective optimization
  • Pareto optimality
  • steady-state


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