Abstract
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.
Highlights
With the continuous development of scientific research, more and more swarm intelligence optimization algorithms have been proposed. e swarm intelligence optimization algorithm basically abstracts a series of formulas based on the life characteristics or behavior rules of organisms or things and finds high-quality solutions in a certain solution space based on these formulas
The sparrow search algorithm (SSA) [1], which finds optimal solutions through the sparrow foraging process, is a novel swarm intelligence optimization algorithm proposed by two scholars, Xue and Shen in 2020. is algorithm has fewer population roles and simple principles, and it is easy to understand
These improved strategies can better improve the optimization ability of each algorithm, they do not change the search mechanism of each algorithm itself. They only improved the search ability of the algorithm in the neighborhood space, and the learning rate was not high, resulting in the algorithm still having drawbacks. is means that when faced with high-dimensional complex problems, these algorithms may be stuck into the local optimal situation. erefore, based on the research of many scholars, this paper proposes the adaptive spiral flying sparrow search algorithm (ASFSSA)
Summary
With the continuous development of scientific research, more and more swarm intelligence optimization algorithms have been proposed. e swarm intelligence optimization algorithm basically abstracts a series of formulas based on the life characteristics or behavior rules of organisms or things and finds high-quality solutions in a certain solution space based on these formulas. Xin et al [7] proposed an improved algorithm, adding the flying thoughts in the bird swarm algorithm to the location update of the discoverer and the follower, ensuring global convergence, increasing the diversity of the population, and being able to jump out of the local optimum. A labor cooperation structure is added to the discoverer stage and the early warning stage, allowing the discoverer and the early warning individual to share their locations to achieve cooperation, so as to converge to the global optimal solution faster and more stably These improved strategies can better improve the optimization ability of each algorithm, they do not change the search mechanism of each algorithm itself.
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