基于新型非支配排序的多目标麻雀优化算法

针对麻雀搜索算法在求解多目标问题中的不足，并且在求解过程中易陷入局部最优与收敛性差的问题，提出了一种改进的多目标麻雀搜索算法。首先，引入了新型非支配排序，找到最优前沿面；其次，将多项式变异和正余弦算法融合到种群进化策略中，增强其搜索能力，通过竞争机制的种群选择方法，降低搜索过程中局部最优粒子和全局最优粒子导致的误差；最后，将改进算法与多种多目标算法在标准测试函数上进行对比，仿真结果表明，改进算法的收敛性与搜索能力均优于其他算法。由此说明该算法具有可靠的多目标寻优能力，能够有效解决多目标优化问题。

Novel multi-objective sparrow optimization algorithm with improved non-dominated sorting

Targeting to the deficiency of sparrow search algorithm solved the multi-objective problems, and the problems can easily enter the partial optimization and inferior convergence in the counting process, this paper came up with a kind of improved multi-objective sparrow search algorithm. First of all, this paper used a new-type non-dominated sorting to find the Pareto front. Next, it integrated the polynomial mutation and cosine algorithm into species evolution strategy to strengthen its searching ability. It used the species selection method of competition mechanism to decrease the differentiation caused by partial optimized particle and overall optimized particle in the searching process. Finally, this paper compared the improved algorithm and various kinds of multi-objective algorithm in standard test function. The simulation results show that, the convergence and searching ability of the improved algorithm are all superior than other algorithm. Therefore, this algorithm can effectively address the multiple target optimization problem.