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Aiming at the problems that the sparrow search algorithm (SSA) is prone to fall into local optimum and the optimization accuracy is low in the optimization process, this paper proposed a hybrid strategy improved sparrow search algorithm (MSSA). Firstly, in order to enable individual sparrows to fully search in the search space and increase the accuracy of algorithm optimization, it introduced an archiving stage in the algorithm optimization process to receive the residual position information that might be captured by sparrow producers when they moved to the safe area. Secondly, in the iterative process of the algorithm, it performed an adaptive neighborhood search operation on the current optimal individual, and fully explored the location information around the high-quality individual to enhance the algorithm's ability to jump out of the local optimum. This paper used 9 benchmark functions to evaluate the MSSA with SSA, and other four improved SSA, which were chaotic sparrow search optimization algorithm (CSSOA), mixed strategy improved sparrow search algorithm (MSSSA), improved sparrow search algorithm(ISSA), and enhanced sparrow search algorithm (ESSA). Experiment result illustrates that MSSA can achieve better convergence accuracy and stability on nearly 80% of the benchmark functions, and MSSA is also ranked first in the Friedman test. This paper also applied MSSA to optimize wireless sensor network( WSN) coverage in an obstacle environment. Compared with other five compared algorithms, MSSA can increase the coverage rates of WSN up 9. 77%, 4. 25%, 6. 62%, 3. 02%, and 7. 38%, respectively. [ABSTRACT FROM AUTHOR]

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]

To solve the shortcoming of the meta-heuristic sparrow search algorithm, such as early convergence, easy to fall into local optimum, poor global searchability, this paper proposed a multi-strategy sparrow search algorithm integrating golden sine and curve adaptative. Firstly, it used Chebyshev chaotic mapping to initialize the population, so that the initial solution position distribution was more homogeneous, produced high-quality initial solutions, and increased the richness of the population. Secondly, it introduced golden sine and curve adaptive weight to improve the location update method of the discoverer and the joiner, which effectively coordinated global search and local mining capabilities and accelerated the convergence speed. Finally, it selected the random walk or Cauchy-t disturbance strategy disturbed the optimal sparrow position dynamically, which improved the ability of the algorithm to jump out of the local optimum and improved the convergence accuracy. This paper selected 14 benchmark functions for testing, compared the simulation results of the proposed algorithm with other 9 metaheuristic algorithms, and used Wilcoxon rank-sum test and MAE ranking to verify the effectiveness of the proposed improvement strategy. The results prove that the proposed algorithm has a great improvement in global searchability, overcoming local optima, convergence speed, convergence accuracy, and stability [ABSTRACT FROM AUTHOR]

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]

This paper proposed an optimized mapping algorithm for service function chain based on improved sparrow search algorithm to realize the low-cost and efficient deployment of service chain in 5G network scenario. It coded the composition schemes and the mapping schemes of service requests together by a double-layer coding method in the mapping process, and then used an improved sparrow search algorithm to obtain the service function chain deployment scheme of the request and calculated the mapping weight. When there were multiple request types in the same time slice, it sorted all service function chains according to the mapping weight,and mapped the requests that had high weight as priority,so as to reduce the delay overhead and improve the utilization of node computing resources and link resources. Simulation results show that the algorithm can effectively reduce deployment overhead and improve resource utilization. [ABSTRACT FROM AUTHOR]