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(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(C=C, gamma=gamma, kernel='rbf') 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(C=best_C, gamma=best_gamma, kernel='rbf') 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(f"Optimal Load Allocation: {best_sol}, Objective Value: {best_fit}")如有问题，可以直接沟通
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