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基于博弈捕食机制和自杀机制的改进麻雀算法。

Improved Sparrow Algorithm Based on Game Predatory Mechanism and Suicide Mechanism.

机构信息

Xi'an Research Institute of High Technology, Xi'an, Shaanxi 710025, China.

School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China.

出版信息

Comput Intell Neurosci. 2022 May 16;2022:4925416. doi: 10.1155/2022/4925416. eCollection 2022.

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.

摘要

为了克服麻雀搜索算法收敛速度非常快但容易陷入局部优化陷阱的缺陷,本文基于麻雀算法的原始机制,提出了博弈捕食机制和自杀机制,使麻雀算法更符合其生物特性,增强了算法摆脱局部优化吸引力的能力,同时保留了快速收敛速度的优势。通过使用良好点集策略初始化种群,保证了初始种群的质量,增强了种群的多样性。针对目前多样性指数评价没有考虑个体在搜索过程中超出边界的无效搜索的情况,提出了一种在搜索过程中衡量超出边界的无效搜索的指标,进一步改进了多样性指数的度量方法,使其更加准确。改进的算法在六个基本函数和 CEC2017 测试函数上进行了测试,以验证其有效性。最后,将改进的算法应用于具有威胁区域的无人机三维路径规划中。结果表明,改进的算法具有更强的优化性能,与其他算法相比具有较强的竞争力,能够快速规划无人机的有效稳定路径,为该领域及其他领域的应用提供了一种有效的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bc3/9126709/3032fd2faa11/CIN2022-4925416.001.jpg

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