  Under the condition of Karush-Kuhn-Tucker, the Pareto Set (PS) in the
decision area of an m-objective optimization problem is a piecewise continuous
(m-1)-D manifold. For illustrate the degree of convergence of the population,
we employed the ratio of the sum of the first (m-1) largest eigenvalue of the
population's covariance matrix of the sum of all eigenvalue. Based on this
property, this paper proposes a new algorithm, called DE/RM-MEDA, which mix
differential evolutionary (DE) and the estimation of distribution algorithm
(EDA) to generate and adaptively adjusts the number of new solutions by the
ratio. The proposed algorithm is experimented on nine tec09 problems. The
comparison results between DE/RM-MEDA and the others algorithms, called
NSGA-II-DE and RM-MEDA, show that the proposed algorithm perform better in
terms of convergence and diversity metric.
