Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2015.061204
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 12, 2015.
Abstract: We propose CEDA, a Copula-based Estimation of Distribution Algorithm, to increase the size, achieve high diversity and convergence of optimal solutions for a multiobjective optimization problem. The algorithm exploits the statistical properties of Copulas to produce new solutions from the existing ones through the estimation of their distribution. CEDA starts by taking initial solutions provided by any MOEA (Multi Objective Evolutionary Algorithm), construct Copulas to estimate their distribution, and uses the constructed Copulas to generate new solutions. This design saves CEDA the need of running an MOEA every time alternative solutions are requested by a Decision Maker when the found solutions are not satisfactory. CEDA was tested on a set of benchmark problems traditionally used by the community, namely UF1, UF2, ..., UF10 and CF1, CF2, ..., CF10. CEDA used along with SPEA2 and NSGA2 as two examples of MOEA thus resulting in two variants CEDA-SPEA2 and CEDA-NSGA2 and compare them with SPEA2 and NSGA2. The results of The experiments show that, with both variants of CEDA, new solutions can be generated in a significantly smaller without compromising quality compared to those found SPEA2 and NSGA2.
Abdelhakim Cheriet and Foudil Cherif, “A Posteriori Pareto Front Diversification Using a Copula-Based Estimation of Distribution Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 6(12), 2015. http://dx.doi.org/10.14569/IJACSA.2015.061204