Balancing the convergence and diversity of many-objective evolutionary algorithms is difficult and challenging. In this work, a multi-search strategy based on decomposition is proposed to generate good offspring and improve convergence, and an external population strategy is used to maintain the diversity of the obtained solutions. The multi-search strategy allows the selection of sparse and convergent nondominated solutions to carry out the exploration and exploitation steps. Experiments are conducted on 15 benchmark functions from the CEC 2018 with 5, 10, and 15 objectives. The results indicate that the proposed algorithm can obtain a set of solutions with better diversity and convergence than the five efficient state-of-the-art algorithms, i.e. NSGAIII, MOEA/D, MOEA/DD, KnEA, and RVEA.

中文翻译：

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一种基于多搜索策略和外部种群策略的多目标优化进化算法

平衡多目标进化算法的收敛性和多样性是困难且具有挑战性的。在这项工作中，提出了一种基于分解的多重搜索策略来生成良好的后代并提高收敛性，并使用外部种群策略来保持所获得解决方案的多样性。多重搜索策略允许选择稀疏和收敛的非支配解决方案来执行探索和利用步骤。实验是在 CEC 2018 的 15 个基准函数上进行的，具有 5、10 和 15 个目标。结果表明，与五种有效的最先进算法（即NSGAIII、MOEA/D、MOEA/DD、KnEA和RVEA）相比，所提出的算法可以获得一组具有更好的多样性和收敛性的解。