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Under the condition of Karush-Kuhn-Tucker, the Pareto Set (PS) in thedecision 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 thepopulation's covariance matrix of the sum of all eigenvalue. Based on thisproperty, this paper proposes a new algorithm, called DE/RM-MEDA, which mixdifferential evolutionary (DE) and the estimation of distribution algorithm(EDA) to generate and adaptively adjusts the number of new solutions by theratio. The proposed algorithm is experimented on nine tec09 problems. Thecomparison results between DE/RM-MEDA and the others algorithms, calledNSGA-II-DE and RM-MEDA, show that the proposed algorithm perform better interms of convergence and diversity metric.

DE/RM-MEDA: A New Hybrid Multi-Objective Generator