DE/RM-MEDA: A New Hybrid Multi-Objective Generator
This is an incremental improvement for researchers in multi-objective optimization, addressing specific benchmark problems.
The paper tackles multi-objective optimization by proposing DE/RM-MEDA, a hybrid algorithm combining differential evolution and estimation of distribution, which outperforms NSGA-II-DE and RM-MEDA on nine TEC09 problems in convergence and diversity metrics.
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.