基于改进粒子群算法的海上遇险目标搜寻方法

A Maritime Distressed Target Search Method Based on Improved Particle Swarm Algorithm

摘要:

针对海上遇险目标搜寻范围动态化、影响因素众多导致搜救成功率较低的问题，提出了一种基于改进粒子群算法的海上遇险目标搜寻方法，该方法旨在寻找最佳搜寻方案，提高海上遇险目标的搜救成功率。基于遇险目标的位置信息和搜寻资源参数，构建海上遇险目标搜寻模型；为增强粒子群算法的初期全局搜索和后期局部搜索能力，采用余弦曲线自适应方法改进算法的惯性权重系数，为避免搜索效率下降或不稳定，采用自适应策略调整加速度并保持其总和不变；引入扰动粒子更新机制来保持种群的多样性，避免陷入局部最优，将改进算法应用于实际搜寻问题，验证了算法的有效性；利用先验概率分布图，将改进算法与传统的粒子群算法和遗传算法进行对比，结果表明，改进后算法的搜救成功率高于传统的粒子群算法和遗传算法。

Abstract:

In response to the problem of dynamicization of search scope and numerous influencing factors leading to lower success rates in rescuing maritime distress targets, a maritime distress target search method based on improved particle swarm algorithm is proposed. The method aims to find the optimal search scheme and enhance the success rate of rescuing maritime distress targets. Based on the location information of distressed targets and search resource parameters, a maritime distress target search model is constructed. To enhance the initial global search and later local search capabilities of the particle swarm algorithm, a cosine curve adaptive method is adopted to improve the algorithm's inertia weight coefficient. To avoid a decrease in search efficiency or instability, an adaptive strategy is employed to adjust the acceleration while maintaining its total sum unchanged. A perturbation particle update mechanism is introduced to maintain population diversity and prevent falling into local optima. The improved algorithm is applied to practical search problems to validate its effectiveness. By utilizing a prior probability distribution map, the improved algorithm is compared with traditional particle swarm and genetic algorithms. The results indicate that the success rate of the improved algorithm is higher than that of traditional particle swarm and genetic algorithms.