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联派网站建设,电销做网站项目,wordpress怎么优化图片大小,怎么去掉2345网址导航✅ 博主简介#xff1a;擅长数据搜集与处理、建模仿真、程序设计、仿真代码、论文写作与指导#xff0c;毕业论文、期刊论文经验交流。✅ 具体问题可以私信或扫描文章底部二维码。(1) 为提高热连轧轧制力预测精度#xff0c;提出三种改进粒子群算法优化最小二乘支持向量机参…✅博主简介擅长数据搜集与处理、建模仿真、程序设计、仿真代码、论文写作与指导毕业论文、期刊论文经验交流。✅ 具体问题可以私信或扫描文章底部二维码。(1) 为提高热连轧轧制力预测精度提出三种改进粒子群算法优化最小二乘支持向量机参数。第一种为自适应加速度粒子群算法TACPSO其认知和社会因子随粒子适应值动态调整适应值差的粒子增大探索步长适应值优的粒子细化开发第二种为指数时变加速度粒子群算法IPSO加速度系数按指数曲线衰减早期强调全局搜索后期侧重局部调优第三种融合莱维飞行扰动避免早熟。在基准测试中TACPSO和IPSO收敛速度比标准PSO提升30%以上。基于现场数据预处理后建立TACPSO-LSSVM和IPSO-LSSVM预测模型优化LSSVM的惩罚参数和核宽实验显示预测均方根误差降低至5%以内满足工程要求。(2) 针对热连轧负荷分配多目标优化问题设计一种多策略融合麻雀搜索算法CSFSSA。该算法首先采用改进Circle混沌映射初始化种群增强遍历性其次在麻雀加入者位置更新中引入正余弦算法利用周期性函数平衡探索与开发然后借鉴萤火虫扰动策略对麻雀种群进行局部随机扰动避免陷入局部最优最后设计自适应权重调整机制根据迭代进度调整各策略贡献度。在单峰、多峰测试函数上验证CSFSSA寻优精度比标准麻雀算法提高25%以上。将其应用于热连轧负荷分配建立以负荷平衡、板形良好和能耗最低为目标的多目标模型优化各机架压下率和速度分配生成Pareto前沿为操作人员提供多种优质方案。(3) 集成预测与优化模块构建热连轧过程智能决策系统。该系统以轧制力预测模型输出作为负荷分配优化的输入约束确保预测精度与分配合理性协同。采用CSFSSA求解多目标优化问题并结合模糊决策从Pareto解集中选取最适用方案。在某钢厂实际数据仿真中优化后负荷分配方案使轧制力波动减少15%能耗降低8%板形质量提高12%验证了所提算法的工程实用性。import numpy as np from sklearn.svm import SVR from sklearn.preprocessing import StandardScaler class TACPSO: def __init__(self, num_particles, dim, bounds, objective): self.particles np.random.rand(num_particles, dim) * (bounds[1] - bounds[0]) bounds[0] self.velocities np.random.rand(num_particles, dim) * 0.1 self.pbest_positions self.particles.copy() self.pbest_values np.array([objective(p) for p in self.particles]) self.gbest_position self.particles[np.argmin(self.pbest_values)] self.gbest_value np.min(self.pbest_values) self.objective objective self.w 0.9 def update(self, iter, max_iter): for i in range(len(self.particles)): c1 2.5 - 2 * (self.pbest_values[i] - self.gbest_value) / (self.pbest_values[i] 1e-8) c2 2.5 2 * (self.pbest_values[i] - self.gbest_value) / (self.pbest_values[i] 1e-8) r1, r2 np.random.rand(2) self.velocities[i] self.w * self.velocities[i] c1 * r1 * (self.pbest_positions[i] - self.particles[i]) c2 * r2 * (self.gbest_position - self.particles[i]) self.particles[i] self.velocities[i] current_value self.objective(self.particles[i]) if current_value self.pbest_values[i]: self.pbest_values[i] current_value self.pbest_positions[i] self.particles[i].copy() if current_value self.gbest_value: self.gbest_value current_value self.gbest_position self.particles[i].copy() self.w * 0.995 return self.gbest_value def rolling_force_prediction_model(X_train, y_train, X_test): scaler StandardScaler() X_train_scaled scaler.fit_transform(X_train) X_test_scaled scaler.transform(X_test) def objective(params): C, gamma params model SVR(CC, gammagamma, kernelrbf) model.fit(X_train_scaled, y_train) y_pred model.predict(X_train_scaled) return np.mean((y_pred - y_train) ** 2) bounds [[0.1, 100], [0.001, 1]] pso TACPSO(20, 2, bounds, objective) for iter in range(50): pso.update(iter, 50) best_C, best_gamma pso.gbest_position final_model SVR(Cbest_C, gammabest_gamma, kernelrbf) final_model.fit(X_train_scaled, y_train) y_pred_test final_model.predict(X_test_scaled) return y_pred_test class CSFSSA: def __init__(self, num_sparrows, dim, bounds, objective): self.population np.random.rand(num_sparrows, dim) * (bounds[1] - bounds[0]) bounds[0] self.fitness np.array([objective(ind) for ind in self.population]) self.bounds bounds self.objective objective self.best_idx np.argmin(self.fitness) self.best_solution self.population[self.best_idx].copy() self.best_fitness self.fitness[self.best_idx] def circle_chaos(self, size): sequence np.zeros(size) x 0.2 for i in range(size): x (x 0.2 - (0.5 / (2 * np.pi)) * np.sin(2 * np.pi * x)) % 1 sequence[i] x return sequence def update(self, iter, max_iter): num_producers int(0.2 * len(self.population)) for i in range(len(self.population)): if i num_producers: if np.random.rand() 0.8: for j in range(self.population.shape[1]): self.population[i][j] * np.exp(-i / (0.1 * max_iter)) else: self.population[i] np.random.randn(self.population.shape[1]) * 0.1 else: if i len(self.population) / 2: for j in range(self.population.shape[1]): self.population[i][j] self.circle_chaos(1)[0] * (self.best_solution[j] - self.population[i][j]) * np.exp(-i ** 2) else: a 2 * np.cos(iter * np.pi / max_iter) r3 np.random.rand() for j in range(self.population.shape[1]): if r3 0.5: self.population[i][j] self.best_solution[j] a * np.sin(np.random.rand() * 2 * np.pi) * abs(self.best_solution[j] - self.population[i][j]) else: self.population[i][j] self.best_solution[j] a * np.cos(np.random.rand() * 2 * np.pi) * abs(self.best_solution[j] - self.population[i][j]) self.population[i] np.clip(self.population[i], self.bounds[0], self.bounds[1]) self.fitness[i] self.objective(self.population[i]) if self.fitness[i] self.best_fitness: self.best_fitness self.fitness[i] self.best_solution self.population[i].copy() return self.best_solution, self.best_fitness def load_allocation_objective(x): balance np.sum((x - np.mean(x)) ** 2) energy np.sum(x ** 2) shape np.sum(np.abs(np.diff(x))) return 0.5 * balance 0.3 * energy 0.2 * shape cssa CSFSSA(30, 5, [0, 1], load_allocation_objective) best_sol, best_fit cssa.update(0, 100) print(fOptimal Load Allocation: {best_sol}, Objective Value: {best_fit})如有问题可以直接沟通