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Multi-sensor Detection Alliance Based on Improved Plant Cell Colony Algorithm

机译:基于改进植物细胞殖民群的多传感器检测联盟

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In order to acquire better multi-sensor detection alliance schemes, an improved plant cell swarm algorithm is proposed. The algorithm is proposed based on the natural phenomenon that the whole plant has the maximum contact area with the sunlight due to the sunward nature of the plant in the growth process, which includes five steps: initialization of plant cell population, determination of the location of the strongest light, distribution of auxin, calculation of growth rate and update of plant cell population. The simulation part compares the improved plant cell swarm algorithm (IPCCA) with the basic plant cell swarm algorithm (BPCCA), particle swarm algorithm (PSOA), bee colony algorithm (BCOA) and wolf colony algorithm (WCOA) to verify the effectiveness of the improved plant cell swarm algorithm. The simulation results show that the improved plant cell swarm algorithm has the strongest optimization ability, can effectively avoid premature and jump out of the local optimal solution, and has good global search ability in dealing with multi-sensor detection alliance problem.
机译:为了获得更好的多传感器检测联盟方案,提出了一种改进的植物单元群算法。基于自然现象提出了算法,即整个植物由于植物在生长过程中的阳光下具有阳光的最大接触面积,其中包括五个步骤:植物细胞群的初始化,确定位置最强烈的光,养猪分布,增长率和植物细胞群的更新。仿真部分将改进的植物细胞群算法(IPCCA)与基础植物细胞群(BPCCA),粒子群算法(PSOA),蜜蜂群算法(BCOA)和狼群算法(WCOA)进行了比较,以验证效果改进的植物细胞群算法。仿真结果表明,改进的植物细胞群算法具有最强的优化能力,可以有效地避免过早和跳出本地最佳解决方案,并具有良好的全球搜索能力,以处理多传感器检测联盟问题。

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