Python粒子群优化算法结合热力图TIFF文件案例
1. 项目概述
本项目使用粒子群优化算法（PSO）在热力图TIFF文件中寻找温度最高点。热力图通常以地理空间数据形式存储（TIFF格式），包含温度分布信息。PSO算法模拟鸟群觅食行为，通过粒子协作在搜索空间中寻找最优解。
import numpy as np
import rasterio
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from mpl_toolkits.axes_grid1 import make_axes_locatable
import time# 创建热力图数据（模拟真实场景）
def create_heatmap_tiff(filename):x = np.linspace(-5, 5, 500)y = np.linspace(-5, 5, 500)X, Y = np.meshgrid(x, y)# 创建多峰温度分布Z = (np.sin(X)*np.cos(Y)*2 + np.exp(-((X-2)**2 + (Y-2)**2)/2) * 3 +np.exp(-((X+3)**2 + (Y+1)**2)/3) * 4 +np.exp(-((X-1)**2 + (Y+3)**2)/4) * 5)# 标准化到0-100℃范围Z = (Z - Z.min()) / (Z.max() - Z.min()) * 100# 保存为TIFFwith rasterio.open(filename,'w',driver='GTiff',height=Z.shape[0],width=Z.shape[1],count=1,dtype=Z.dtype,crs='EPSG:4326',transform=rasterio.transform.from_origin(x.min(), y.max(), x[1]-x[0], y[1]-y[0])) as dst:dst.write(Z, 1)# 生成热力图文件
create_heatmap_tiff('heatmap.tif')
2. 粒子群优化算法实现
class ParticleSwarmOptimizer:def __init__(self, objective_func, bounds, num_particles=50, max_iter=100,inertia=0.5, cognitive_param=1.5, social_param=1.5):self.objective_func = objective_funcself.bounds = np.array(bounds)self.num_particles = num_particlesself.max_iter = max_iterself.inertia = inertiaself.cognitive_param = cognitive_paramself.social_param = social_param# 初始化粒子self.particles = np.random.uniform(low=self.bounds[:, 0], high=self.bounds[:, 1], size=(self.num_particles, len(self.bounds)))self.velocities = np.zeros((self.num_particles, len(self.bounds)))self.best_positions = self.particles.copy()self.best_scores = np.full(self.num_particles, np.inf)self.global_best_position = Noneself.global_best_score = np.infself.history = []def optimize(self):for iter_num in range(self.max_iter):for i in 
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