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Review
. 2022 May 16:2022:4925416.
doi: 10.1155/2022/4925416. eCollection 2022.

Improved Sparrow Algorithm Based on Game Predatory Mechanism and Suicide Mechanism

Ping Yang  1 , Shaoqiang Yan  1 , Donglin Zhu  2 , Jiangpeng Wang  1 , Fengxuan Wu  1 , Zhe Yan  1 , Song Yan  1
Affiliations
Review

Improved Sparrow Algorithm Based on Game Predatory Mechanism and Suicide Mechanism

Ping Yang et al. Comput Intell Neurosci. .

Abstract

In order to overcome the defect that sparrow search algorithm converges very fast but is easy to fall into the trap of local optimization, based on the original mechanism of sparrow algorithm, this paper proposes game predatory mechanism and suicide mechanism, which makes sparrow algorithm more in line with its biological characteristics and enhances the ability of the algorithm to get rid of the attraction of local optimization while retaining the advantages of fast convergence speed. By initializing the population with the good point set strategy, the quality of the initial population is guaranteed and the diversity of the population is enhanced. In view of the current situation that the diversity index evaluation does not consider the invalid search caused by individuals beyond the boundary in the search process, an index to measure the invalid search beyond the boundary in the search process is proposed, and the measurement of diversity index is further improved to make it more accurate. The improved algorithm is tested on six basic functions and CEC2017 test function to verify its effectiveness. Finally, the improved algorithm is applied to the three-dimensional path planning of UAV with threat area. The results show that the improved algorithm has stronger optimization performance, has strong competitiveness compared with other algorithms, and can quickly plan the effective and stable path of UAV, which improves an effective method for the application in this field and other fields.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Distribution of good point set.
Figure 2
Figure 2
Principle of game grabbing mechanism.
Figure 3
Figure 3
Parameter space of function.
Figure 4
Figure 4
Algorithm step diagram.
Figure 5
Figure 5
Strategy effect comparison.
Figure 6
Figure 6
Convergence diagram of each algorithm.
Figure 7
Figure 7
Invalid search of each algorithm.
Figure 8
Figure 8
Diversity of each algorithm.
Figure 9
Figure 9
Exploration-exploitation percentage of each algorithm.
Figure 10
Figure 10
Ranking radar chart.
Figure 11
Figure 11
Box diagram of each algorithm.
Figure 12
Figure 12
Threat principle.
Figure 13
Figure 13
Cubic B-spline principle.
Figure 14
Figure 14
Original map.
Figure 15
Figure 15
Path planning with threats.
Figure 16
Figure 16
Top view of path planning contour.
Figure 17
Figure 17
Objective function convergence graph.
Algorithm 1
Algorithm 1
The framework of the GPSSA.
See this image and copyright information in PMC

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