August 4, 2015

Paper: DECMO2 - A Robust Hybrid and Adaptive Multi-Objective Evolutionary Algorithm

This journal article also stems from my PhD research project - enhancing currently available Evolutionary Computation methods employed for solving computationally-intensive multi-objective optimization problems. The presented algorithm - DECMO2 - is an improved version of a method presented in one of our earlier papersHere is the abstract of the article:
We describe a hybrid and adaptive coevolutionary optimization method that can efficiently solve a wide range of multi-objective optimization problems (MOOPs) as it successfully combines positive traits from three main classes of multi-objective evolutionary algorithms (MOEAs): classical approaches that use Pareto-based selection for survival criteria, approaches that rely on differential evolution, and decomposition-based strategies. A key part of our hybrid evolutionary approach lies in the proposed fitness sharing mechanism that is able to smoothly transfer information between the coevolved subpopulations without negatively impacting the specific evolutionary process behavior that characterizes each subpopulation. The proposed MOEA also features an adaptive allocation of fitness evaluations between the coevolved populations in order to increase robustness and favor the evolutionary search strategy that proves more successful for solving the MOOP at hand. Apart from the new evolutionary algorithm, this paper also contains the description of a new hypervolume and racing-based methodology aimed at providing practitioners from the field of multi-objective optimization with a simple means of analyzing/reporting the general comparative run-time performance of multi-objective optimization algorithms over large problem sets.
You can download the preprint version of the paper by clicking here or from my Downloads box (DECMO2 - A Robust Hybrid and Adaptive Multi-Objective Evolutionary Algorithm - SOCO 2014.pdf). The same preprint version can be previewed at the bottom of this post. The final publication is available at Springer via

For citations please use the following BibTeX reference:

  author = {Alexandru-Ciprian Z\u{a}voianu and Edwin Lughofer and Gerd Bramerdorfer and Wolfgang Amrhein and Erich Peter Klement},
  title = {{DECMO2}: a robust hybrid and adaptive multi-objective evolutionary algorithm},
  journal = {Soft Computing},
  year = {2014},
  volume = {19},
  number = {12},
  pages = {3551-3569},
  doi = {10.1007/s00500-014-1308-7}

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