Many-objective optimization has posed a great challenge to the classical Pareto-dominance based multi-objective evolutionary algorithms. In this paper, an evolutionary algorithm based on a new dominance relation is proposed for manyobjective optimization. The proposed evolutionary algorithm aims to enhance the convergence of the recently suggested nondominated sorting genetic algorithm III by exploiting the fitness evaluation scheme in multi-objective evolutionary algorithm based on decomposition, but still inherit the strength of the former in diversity maintenance. In the proposed algorithm, the non-dominated sorting scheme based on the introduced new dominance relation is employed to rank solutions in the environmental selection phase, ensuring both convergence and diversity. The proposed algorithm is applied to a number of well-known benchmark problems having 3 to 15 objectives and compared against eight state-of-the-art algorithms. The extensive experimental results show that the proposed algorithm can work well on almost all the test instances considered in this study, and it is compared favourably with the other many-objective optimizers. Additionally, a parametric study is provided to investigate the influence of a key parameter in the proposed algorithm.

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D-S evidence theory is a useful method in dealing with uncertainty problems, but its application is limited because of the shortcomings of its combination rule. This paper presents an efficient combination rule, that is, the evidences' conflicting probability is distributed to every proposition according to its average supported degree. The new combination rule improves the reliability and rationality of combination results. Although evidences conflict one another highly, good combination results are also obtained.