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考虑到支配解可能携带有利于算法搜索到最优解的信息,在克隆阶段选择一部分非支配解和支配解克隆以提高种群多样性和避免算法早熟收敛。在进化阶段,先采用自适应差分进化算子交叉变异,然后用多项式变异算子进行扰动以有效地平衡算法的全局搜索和局部搜索。基于个体强度建立外部文档储存一定数量的较好解,并让这些较好解在每次迭代中参与进化且被更新。对10个标准测试函数进行仿真实验,并与其他5种算法进行比较,结果表明所提算法在收敛性和解的分布性方面均表现出明显优势。
Given that the dominating solution may carry information that facilitates the search of the algorithm to the optimal solution, a portion of the non-dominated solution is dictated and dominanced in the cloning phase to improve population diversity and avoid premature convergence of the algorithm. In the evolutionary stage, the adaptive differential evolution operator is firstly used to cross-mutation, and then the polynomial mutation operator is used to perform the perturbation to effectively balance the global search and the local search of the algorithm. Create external documents based on individual intensities to store a certain number of better solutions, and make these better solutions evolve and updated at each iteration. Ten standard test functions are simulated and compared with the other five algorithms. The results show that the proposed algorithm shows obvious advantages in the convergence of convergence and solution.