132 lines
5.8 KiB
Python
132 lines
5.8 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from chromosome_utils import ChromosomeUtils # 染色体工具类
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class ResultVisualizer:
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"""结果可视化工具类：绘制帕累托前沿、收敛曲线，打印最优解详情"""
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def __init__(self, utils: ChromosomeUtils):
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"""
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初始化可视化工具
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:param utils: 染色体工具类实例（用于解析染色体）
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"""
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self.utils = utils
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self.figsize = (12, 8) # 图表大小
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# 企业名称列表（风险企业+供应商）
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self.supplier_names = ["RiskEnterprise"] + self.utils.supplier.names
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def plot_pareto_front(self, all_objectives: list[tuple[float, float]],
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non_dominated_objectives: list[tuple[float, float]]):
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"""
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绘制帕累托前沿（所有解 vs 帕累托最优解）
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:param all_objectives: 所有解的目标值
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:param non_dominated_objectives: 帕累托最优解的目标值
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"""
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plt.figure(figsize=self.figsize)
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# 绘制所有解（蓝色点）
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if all_objectives:
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c_all = [obj[0] for obj in all_objectives] # 成本
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t_all = [obj[1] for obj in all_objectives] # 延期
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plt.scatter(c_all, t_all, color='blue', alpha=0.3, label='All Solutions', zorder=1)
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# 绘制帕累托前沿（红色点）
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if non_dominated_objectives:
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c_nd = [obj[0] for obj in non_dominated_objectives]
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t_nd = [obj[1] for obj in non_dominated_objectives]
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plt.scatter(c_nd, t_nd, color='red', s=50, label='Pareto Front', zorder=5)
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# 图表设置
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plt.title('Pareto Front: Change Cost vs Tardiness')
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plt.xlabel('Change Cost')
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plt.ylabel('Tardiness')
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plt.legend()
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plt.grid(True, alpha=0.5)
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plt.savefig('pareto_front.png', dpi=300, bbox_inches='tight') # 保存图片
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plt.show()
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def plot_convergence(self, convergence_history: list[tuple[float, float]]):
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"""
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绘制收敛趋势图（每代最优前沿的平均成本和延期）
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:param convergence_history: 收敛历史数据（(平均成本, 平均延期)）
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"""
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if len(convergence_history) == 0: # 无数据时不绘制
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return
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plt.figure(figsize=self.figsize)
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generations = list(range(len(convergence_history))) # 代数
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costs = [h[0] for h in convergence_history] # 平均成本
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tardiness = [h[1] for h in convergence_history] # 平均延期
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# 子图1：平均成本趋势
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plt.subplot(2, 1, 1)
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plt.plot(generations, costs, 'b-')
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plt.title('Convergence History')
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plt.ylabel('Average Change Cost')
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plt.grid(True, alpha=0.5)
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# 子图2：平均延期趋势
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plt.subplot(2, 1, 2)
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plt.plot(generations, tardiness, 'r-')
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plt.xlabel('Generation')
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plt.ylabel('Average Tardiness')
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plt.grid(True, alpha=0.5)
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plt.tight_layout() # 调整布局
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plt.savefig('convergence_history.png', dpi=300, bbox_inches='tight') # 保存图片
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plt.show()
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def print_solution_details(self, solution: np.ndarray, objectives: tuple[float, float]):
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"""
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打印单个解的详细信息（企业选择、产能、数量等）
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:param solution: 染色体（解）
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:param objectives: 该解的目标值（成本, 延期）
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"""
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# 拆分染色体为三层
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enterprise_layer, capacity_layer, quantity_layer = self.utils._split_chromosome(solution)
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split_points = np.cumsum(self.utils.material_enterprise_count) # 分割点
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print(f"\n变更成本: {objectives[0]:.2f}, 交付延期: {objectives[1]:.2f}")
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print("=" * 80)
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total_q_check = [] # 检查各物料的总数量是否满足需求
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start = 0
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for i in range(self.utils.I): # 遍历每种物料
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end = split_points[i]
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ents = self.utils.material_optional_enterprises[i] # 可选企业
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e_segment = enterprise_layer[start:end] # 企业选择状态
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c_segment = capacity_layer[start:end] # 产能
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q_segment = quantity_layer[start:end] # 数量
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demand_q = self.utils.order.Q[i] # 需求数量
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allocated_q = np.sum(q_segment[e_segment == 1]) # 分配的总数量
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print(f"物料 {i} - 需求数量: {demand_q}, 分配总量: {allocated_q:.2f}")
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total_q_check.append(abs(allocated_q - demand_q) < 1e-2) # 检查是否满足需求
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print(f" 选择的企业及其分配:")
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for idx, ent in enumerate(ents):
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if e_segment[idx] == 1: # 仅打印被选中的企业
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ent_name = self.supplier_names[ent] # 企业名称
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cap = c_segment[idx] # 产能
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qty = q_segment[idx] # 数量
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print(f" 企业: {ent_name}, 产能: {cap:.2f}, 分配数量: {qty:.2f}")
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start = end
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print("-" * 80)
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# 验证数量约束是否满足
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if all(total_q_check):
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print("✅ 所有物料数量满足需求约束")
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else:
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print("❌ 部分物料数量未满足需求约束")
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def print_pareto_solutions(self, population: np.ndarray, objectives: list[tuple[float, float]]):
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"""
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打印帕累托前沿解的详细信息（最多打印前5个）
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:param population: 帕累托前沿解的种群
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:param objectives: 对应的目标值列表
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"""
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print("\n" + "=" * 100)
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print("帕累托前沿解详细方案")
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print("=" * 100)
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# 打印前5个解（或所有解，取较少者）
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for i in range(min(5, len(population))):
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print(f"\n===== 最优解 {i + 1} =====")
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self.print_solution_details(population[i], objectives[i]) |