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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. [ABSTRACT FROM AUTHOR]

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In order to solve the problem that the original sparrow search algorithm (Sparrow Search Algorithm, SSA) has reduced population diversity and the weak local development ability makes it difficult to jump out of the local optimum under the condition of approaching the global optimum. This paper proposes a sparrow search algorithm based on tangential flight (tangent flight Sparrow Search Algorithm, tanSSA). First of all, using adaptive t-distribution strategy to improve the finder location update formula can improve the individual sparrow’s optimization ability and prevent the algorithm from maturing. Then, the tangent flight strategy in the tangent search algorithm can enhance the search space ability of the algorithm, and can make the algorithm jump out of the advantage of the local optimal solution. In original sparrow search algorithm, using the tangent flight disturbance strategy to disturb the optimal solution. The combination of these two strategies can effectively improve the exploration and development performance of tanSSA algorithm. Finally, using 12 standard benchmark functions to test and verify the optimization performance of tanSSA algorithm combined with Wilcoxon rank sum test, and compared with the original SSA algorithm, whale optimization algorithm, particle swarm optimization algorithm and adaptive t-distribution SSA algorithm. The experimental results show that the optimization ability and convergence speed of the sparrow search algorithm based on tangent flight have a significant improvement. [ABSTRACT FROM AUTHOR]