In this paper, a lens learning sparrow search algorithm (LLSSA) is proposed to improve the defects of the new sparrow search algorithm, which is random and easy to fall into local optimum. The algorithm has achieved good results in function optimization and has planned a safer and less costly path to the three-dimensional UAV path planning. In the discoverer stage, the algorithm introduces the reverse learning strategy based on the lens principle to improve the search range of sparrow individuals and then proposes a variable spiral search strategy to make the follower's search more detailed and flexible. Finally, it combines the simulated annealing algorithm to judge and obtain the optimal solution. Through 15 standard test functions, it is verified that the improved algorithm has strong search ability and mining ability. At the same time, the improved algorithm is applied to the path planning of 3D complex terrain, and a clear, simple, and safe route is found, which verifies the effectiveness and practicability of the improved algorithm.
The sparrow search algorithm has attracted much attention due to its excellent characteristics, but it still has shortcomings such as falling into the local optimum and relying on the initial population stage. In order to improve these shortcomings, the chaotic flying sparrow search algorithm is proposed. In the initialization, the chaotic mapping based on random variables is introduced to make the population distribution more uniform and speed up the optimization efficiency of the population. In the discoverer stage, the dynamic adaptive search strategy and levy flight mechanism are used to increase the search range and flexibility, and the random walk strategy is introduced to make the follower’s search more detailed and avoid premature phenomenon. The effectiveness of the improved algorithm is verified by six standard test functions, and the introduction of a variety of strategies greatly enhances the optimization ability of the algorithm.
The sparrow search algorithm is a new type of swarm intelligence optimization algorithm with better effect, but it still has shortcomings such as easy to fall into local optimality and large randomness. In order to solve these problems, this paper proposes an adaptive spiral flying sparrow search algorithm (ASFSSA), which reduces the probability of getting stuck into local optimum, has stronger optimization ability than other algorithms, and also finds the shortest and more stable path in robot path planning. First, the tent mapping based on random variables is used to initialize the population, which makes the individual position distribution more uniform, enlarges the workspace, and improves the diversity of the population. Then, in the discoverer stage, the adaptive weight strategy is integrated with Levy flight mechanism, and the fusion search method becomes extensive and flexible. Finally, in the follower stage, a variable spiral search strategy is used to make the search scope of the algorithm more detailed and increase the search accuracy. The effectiveness of the improved algorithm ASFSSA is verified by 18 standard test functions. At the same time, ASFSSA is applied to robot path planning. The feasibility and practicability of ASFSSA are verified by comparing the algorithms in the raster map planning routes of two models.
As a novel algorithm, the sparrow search algorithm has better optimization performance than other intelligent optimization algorithms. However, in complex problems, there is still the possibility of falling into a local optimum and relying on the initial population stage. In response to these shortcomings, a multi-strategy improved sparrow search algorithm (KLSSA) is proposed. First, in the initial population stage, K-means clustering method is used to cluster and differentiate the individual positions of sparrows, which speeds up the work efficiency of the population and gets rid of the influence of randomness. Then, the levy flight mechanism and adaptive local search strategy are respectively introduced in the calculation of the location update of the discoverer and the follower, so that the discoverer can conduct a wide range of searches more flexibly, and the follower has a more detailed search method. Through the 10 standard test functions, it can be seen that the multi-strategy improved sparrow search algorithm has stronger optimization ability and better optimization speed.
Compared with other algorithms, the performance of sparrow algorithm is better, but it also has shortcomings such as insufficient convergence and large randomness. For this reason, this paper proposes an improved sparrow search algorithm, which uses K-means to initialize the population to reduce the influence of randomness. Use sine and cosine search to improve the quality of the position of followers, and finally use adaptive local search to update the optimal solution, and apply it to concrete strength prediction. The results show that various improved sparrow search algorithms have certain advantages and high stability.
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