Implement genetic algorithm for collector layout optimization

ga.py

@@ -0,0 +1,193 @@
+import numpy as np
+import pandas as pd
+from scipy.spatial import distance_matrix
+from scipy.sparse.csgraph import minimum_spanning_tree
+from collections import defaultdict
+import random
+
+
+def design_with_ga(
+    turbines,
+    substation,
+    cable_specs=None,
+    voltage=66000,
+    power_factor=0.95,
+    system_params=None,
+    pop_size=50,
+    generations=50,
+    evaluate_func=None,
+    total_invest_func=None,
+    get_max_capacity_func=None,
+):
+    """
+    使用遗传算法优化集电线路布局
+    :param turbines: 风机DataFrame
+    :param substation: 升压站坐标
+    :param cable_specs: 电缆规格
+    :param system_params: 系统参数（用于NPV计算）
+    :param pop_size: 种群大小
+    :param generations: 迭代代数
+    :param evaluate_func: 评估函数
+    :param total_invest_func: 总投资计算函数
+    :param get_max_capacity_func: 获取最大容量函数
+    :return: 连接列表和带有簇信息的turbines
+    """
+    if get_max_capacity_func:
+        max_mw = get_max_capacity_func(cable_specs, voltage, power_factor)
+    else:
+        max_mw = 100.0  # 默认值
+    total_power = turbines["power"].sum()
+    max_clusters = int(np.ceil(total_power / max_mw))
+    n_turbines = len(turbines)
+
+    # 预计算距离矩阵
+    all_coords = np.vstack([substation, turbines[["x", "y"]].values])
+    dist_matrix_full = distance_matrix(all_coords, all_coords)
+
+    def fitness(chromosome):
+        cluster_assign = chromosome
+        clusters = defaultdict(list)
+        for i, c in enumerate(cluster_assign):
+            clusters[c].append(i)
+
+        connections = []
+        for c, members in clusters.items():
+            if len(members) == 0:
+                continue
+            coords = turbines.iloc[members][["x", "y"]].values
+            if len(members) > 1:
+                dm = distance_matrix(coords, coords)
+                mst = minimum_spanning_tree(dm).toarray()
+                for i in range(len(members)):
+                    for j in range(len(members)):
+                        if mst[i, j] > 0:
+                            connections.append(
+                                (
+                                    f"turbine_{members[i]}",
+                                    f"turbine_{members[j]}",
+                                    mst[i, j],
+                                )
+                            )
+            # 连接到升压站
+            dists = [dist_matrix_full[0, m + 1] for m in members]
+            closest = members[np.argmin(dists)]
+            connections.append((f"turbine_{closest}", "substation", min(dists)))
+
+        eval_res = evaluate_func(
+            turbines,
+            connections,
+            substation,
+            cable_specs,
+            is_offshore=False,
+            method_name="GA",
+            voltage=voltage,
+            power_factor=power_factor,
+        )
+        if system_params and total_invest_func:
+            res_list = total_invest_func(
+                [
+                    {
+                        "cost": eval_res["total_cost"],
+                        "loss": eval_res["total_loss"],
+                        "eval": eval_res,
+                    }
+                ],
+                system_params,
+            )
+            return res_list[0]["total_cost_npv"]
+        return eval_res["total_cost"]
+
+    def init_individual():
+        assign = np.zeros(n_turbines, dtype=int)
+        cluster_powers = np.zeros(max_clusters)
+        for i in range(n_turbines):
+            p = turbines.iloc[i]["power"]
+            possible = [
+                c for c in range(max_clusters) if cluster_powers[c] + p <= max_mw
+            ]
+            if possible:
+                c = random.choice(possible)
+            else:
+                c = random.randint(0, max_clusters - 1)
+            assign[i] = c
+            cluster_powers[c] += p
+        return assign.tolist()
+
+    population = [init_individual() for _ in range(pop_size)]
+    best = None
+    best_fitness = float("inf")
+
+    for gen in range(generations):
+        fitnesses = [fitness(ind) for ind in population]
+        min_fit = min(fitnesses)
+        if min_fit < best_fitness:
+            best_fitness = min_fit
+            best = population[fitnesses.index(min_fit)].copy()
+
+        def tournament(size=3):
+            candidates = random.sample(list(zip(population, fitnesses)), size)
+            return min(candidates, key=lambda x: x[1])[0]
+
+        selected = [tournament() for _ in range(pop_size)]
+
+        new_pop = []
+        for i in range(0, pop_size, 2):
+            p1 = selected[i]
+            p2 = selected[i + 1] if i + 1 < pop_size else selected[0]
+            if random.random() < 0.8:
+                point = random.randint(1, n_turbines - 1)
+                child1 = p1[:point] + p2[point:]
+                child2 = p2[:point] + p1[point:]
+            else:
+                child1, child2 = p1.copy(), p2.copy()
+            new_pop.extend([child1, child2])
+
+        for ind in new_pop:
+            if random.random() < 0.1:
+                idx = random.randint(0, n_turbines - 1)
+                old_c = ind[idx]
+                new_c = random.randint(0, max_clusters - 1)
+                ind[idx] = new_c
+                cluster_powers = defaultdict(float)
+                for j, c in enumerate(ind):
+                    cluster_powers[c] += turbines.iloc[j]["power"]
+                if max(cluster_powers.values()) > max_mw:
+                    ind[idx] = max_clusters
+                    max_clusters += 1
+
+        elites = sorted(zip(population, fitnesses), key=lambda x: x[1])[
+            : int(0.1 * pop_size)
+        ]
+        new_pop[: len(elites)] = [e[0] for e in elites]
+        population = new_pop[:pop_size]
+
+    # 解码最佳个体
+    cluster_assign = best
+    clusters = defaultdict(list)
+    for i, c in enumerate(cluster_assign):
+        clusters[c].append(i)
+
+    connections = []
+    for c, members in clusters.items():
+        if len(members) == 0:
+            continue
+        coords = turbines.iloc[members][["x", "y"]].values
+        if len(members) > 1:
+            dm = distance_matrix(coords, coords)
+            mst = minimum_spanning_tree(dm).toarray()
+            for i in range(len(members)):
+                for j in range(len(members)):
+                    if mst[i, j] > 0:
+                        connections.append(
+                            (
+                                f"turbine_{members[i]}",
+                                f"turbine_{members[j]}",
+                                mst[i, j],
+                            )
+                        )
+        dists = [dist_matrix_full[0, m + 1] for m in members]
+        closest = members[np.argmin(dists)]
+        connections.append((f"turbine_{closest}", "substation", min(dists)))
+
+    turbines["cluster"] = cluster_assign
+    return connections, turbines

gui.py

@@ -90,6 +90,7 @@ def index():
         "run_btn": None,
         "current_file_container": None,  # 替换 label 为 container
         "info_container": None,  # 新增信息展示容器
+        "ga_switch": None,  # 遗传算法开关
     }
 
     def update_info_panel():
@@ -347,7 +348,9 @@ def index():
         except Exception as ex:
             ui.notify(f"上传处理失败: {ex}", type="negative")
 
-    async def save_file_with_dialog(filename, callback, file_filter="All files (*.*)", sender=None):
+    async def save_file_with_dialog(
+        filename, callback, file_filter="All files (*.*)", sender=None
+    ):
         """
         跨平台文件保存助手。
         如果是原生模式，弹出系统保存对话框。
@@ -409,7 +412,7 @@ def index():
                         ["powershell", "-Command", ps_script],
                         capture_output=True,
                         text=True,
-                        startupinfo=startupinfo
+                        startupinfo=startupinfo,
                     )
                     save_path = result.stdout.strip()
                     if save_path:
@@ -477,7 +480,10 @@ def index():
 
             async def on_click_excel(e):
                 await save_file_with_dialog(
-                    default_excel_name, save_excel, "Excel Files (*.xlsx)", sender=e.sender
+                    default_excel_name,
+                    save_excel,
+                    "Excel Files (*.xlsx)",
+                    sender=e.sender,
                 )
 
             ui.button(
@@ -607,7 +613,9 @@ def index():
                             except:
                                 pass
 
-                await save_file_with_dialog(default_name, save_zip, "ZIP Files (*.zip)", sender=e.sender)
+                await save_file_with_dialog(
+                    default_name, save_zip, "ZIP Files (*.zip)", sender=e.sender
+                )
 
             ui.button("导出全部方案 DXF (ZIP)", on_click=on_click_all_dxf).props(
                 "icon=folder_zip color=secondary"
@@ -669,6 +677,9 @@ def index():
             refs["log_box"].clear()
         log_queue = queue.Queue()
 
+        # 获取遗传算法开关状态
+        use_ga = refs["ga_switch"].value if refs["ga_switch"] else False
+
         class QueueLogger(io.StringIO):
             def write(self, message):
                 if message and message.strip():
@@ -716,6 +727,7 @@ def index():
                         n_clusters_override=None,
                         interactive=False,
                         plot_results=False,
+                        use_ga=use_ga,
                     )
 
             # 在后台线程运行计算任务
@@ -782,7 +794,11 @@ def index():
                     total_length_km = total_length_m / 1000
 
                     # 获取回路数 (通过统计从升压站发出的连接)
-                    n_circuits = sum(1 for d in res["eval"]["details"] if d["source"] == "substation" or d["target"] == "substation")
+                    n_circuits = sum(
+                        1
+                        for d in res["eval"]["details"]
+                        if d["source"] == "substation" or d["target"] == "substation"
+                    )
 
                     row_dict = {
                         "name": name_display,
@@ -875,7 +891,10 @@ def index():
                             raise FileNotFoundError("无法生成模板文件")
 
                     await save_file_with_dialog(
-                        "coordinates.xlsx", save_template, "Excel Files (*.xlsx)", sender=e.sender
+                        "coordinates.xlsx",
+                        save_template,
+                        "Excel Files (*.xlsx)",
+                        sender=e.sender,
                     )
 
                 ui.button("导出 Excel 模板", on_click=export_template).classes(
@@ -901,6 +920,8 @@ def index():
                     # with refs["current_file_container"]:
                     #     ui.label("未选择文件").classes("text-xs text-gray-500 italic ml-1")
 
+                
+
                 # 3. 运行按钮
                 refs["run_btn"] = (
                     ui.button(
@@ -910,6 +931,11 @@ def index():
                     .classes("flex-1 py-4")
                     .props("icon=play_arrow color=secondary")
                 )
+                # 4. 遗传算法开关
+                with ui.column().classes("flex-1 gap-0 justify-center items-center"):
+                    refs["ga_switch"] = ui.switch("启用遗传算法", value=False).props(
+                        "color=orange"
+                    )
 
         with ui.column().classes("w-full gap-4"):
             # 新增：信息展示卡片

main.py

@@ -1,6 +1,7 @@
 import argparse
 import math
 import os
+import random
 from collections import defaultdict
 
 import matplotlib.pyplot as plt
@@ -12,6 +13,7 @@ from scipy.spatial import distance_matrix
 from sklearn.cluster import KMeans
 
 from esau_williams import design_with_esau_williams
+from ga import design_with_ga
 
 # 设置matplotlib支持中文显示
 plt.rcParams["font.sans-serif"] = ["Microsoft YaHei", "SimHei", "Arial"]
@@ -654,9 +656,163 @@ def design_with_rotational_sweep(
 
     return final_connections, turbines
 
+    # 预计算距离矩阵
+    all_coords = np.vstack([substation, turbines[["x", "y"]].values])
+    dist_matrix_full = distance_matrix(all_coords, all_coords)
+
+    def fitness(chromosome):
+        cluster_assign = chromosome
+        clusters = defaultdict(list)
+        for i, c in enumerate(cluster_assign):
+            clusters[c].append(i)
+
+        connections = []
+        for c, members in clusters.items():
+            if len(members) == 0:
+                continue
+            coords = turbines.iloc[members][["x", "y"]].values
+            if len(members) > 1:
+                dm = distance_matrix(coords, coords)
+                mst = minimum_spanning_tree(dm).toarray()
+                for i in range(len(members)):
+                    for j in range(len(members)):
+                        if mst[i, j] > 0:
+                            connections.append(
+                                (
+                                    f"turbine_{members[i]}",
+                                    f"turbine_{members[j]}",
+                                    mst[i, j],
+                                )
+                            )
+            # 连接到升压站
+            dists = [dist_matrix_full[0, m + 1] for m in members]
+            closest = members[np.argmin(dists)]
+            connections.append((f"turbine_{closest}", "substation", min(dists)))
+
+        eval_res = evaluate_design(
+            turbines,
+            connections,
+            substation,
+            cable_specs,
+            is_offshore=False,
+            method_name="GA",
+            voltage=voltage,
+            power_factor=power_factor,
+        )
+        if system_params:
+            res_list = total_investment(
+                [
+                    {
+                        "cost": eval_res["total_cost"],
+                        "loss": eval_res["total_loss"],
+                        "eval": eval_res,
+                    }
+                ],
+                system_params,
+            )
+            return res_list[0]["total_cost_npv"]
+        return eval_res["total_cost"]
+
+    def init_individual():
+        assign = np.zeros(n_turbines, dtype=int)
+        cluster_powers = np.zeros(max_clusters)
+        for i in range(n_turbines):
+            p = turbines.iloc[i]["power"]
+            possible = [
+                c for c in range(max_clusters) if cluster_powers[c] + p <= max_mw
+            ]
+            if possible:
+                c = random.choice(possible)
+            else:
+                c = random.randint(0, max_clusters - 1)
+            assign[i] = c
+            cluster_powers[c] += p
+        return assign.tolist()
+
+    population = [init_individual() for _ in range(pop_size)]
+    best = None
+    best_fitness = float("inf")
+
+    for gen in range(generations):
+        fitnesses = [fitness(ind) for ind in population]
+        min_fit = min(fitnesses)
+        if min_fit < best_fitness:
+            best_fitness = min_fit
+            best = population[fitnesses.index(min_fit)].copy()
+
+        def tournament(size=3):
+            candidates = random.sample(list(zip(population, fitnesses)), size)
+            return min(candidates, key=lambda x: x[1])[0]
+
+        selected = [tournament() for _ in range(pop_size)]
+
+        new_pop = []
+        for i in range(0, pop_size, 2):
+            p1 = selected[i]
+            p2 = selected[i + 1] if i + 1 < pop_size else selected[0]
+            if random.random() < 0.8:
+                point = random.randint(1, n_turbines - 1)
+                child1 = p1[:point] + p2[point:]
+                child2 = p2[:point] + p1[point:]
+            else:
+                child1, child2 = p1.copy(), p2.copy()
+            new_pop.extend([child1, child2])
+
+        for ind in new_pop:
+            if random.random() < 0.1:
+                idx = random.randint(0, n_turbines - 1)
+                old_c = ind[idx]
+                new_c = random.randint(0, max_clusters - 1)
+                ind[idx] = new_c
+                cluster_powers = defaultdict(float)
+                for j, c in enumerate(ind):
+                    cluster_powers[c] += turbines.iloc[j]["power"]
+                if max(cluster_powers.values()) > max_mw:
+                    ind[idx] = max_clusters
+                    max_clusters += 1
+
+        elites = sorted(zip(population, fitnesses), key=lambda x: x[1])[
+            : int(0.1 * pop_size)
+        ]
+        new_pop[: len(elites)] = [e[0] for e in elites]
+        population = new_pop[:pop_size]
+
+    # 解码最佳个体
+    cluster_assign = best
+    clusters = defaultdict(list)
+    for i, c in enumerate(cluster_assign):
+        clusters[c].append(i)
+
+    connections = []
+    for c, members in clusters.items():
+        if len(members) == 0:
+            continue
+        coords = turbines.iloc[members][["x", "y"]].values
+        if len(members) > 1:
+            dm = distance_matrix(coords, coords)
+            mst = minimum_spanning_tree(dm).toarray()
+            for i in range(len(members)):
+                for j in range(len(members)):
+                    if mst[i, j] > 0:
+                        connections.append(
+                            (
+                                f"turbine_{members[i]}",
+                                f"turbine_{members[j]}",
+                                mst[i, j],
+                            )
+                        )
+        dists = [dist_matrix_full[0, m + 1] for m in members]
+        closest = members[np.argmin(dists)]
+        connections.append((f"turbine_{closest}", "substation", min(dists)))
+
+    turbines["cluster"] = cluster_assign
+    return connections, turbines
+
 
 # 4. 获取电缆最大容量(MW)
-def get_max_cable_capacity_mw(cable_specs, voltage=VOLTAGE_LEVEL, power_factor=POWER_FACTOR):
+def get_max_cable_capacity_mw(
+    cable_specs, voltage=VOLTAGE_LEVEL, power_factor=POWER_FACTOR
+):
     """
     根据电缆规格计算最大承载功率
     :param cable_specs: 电缆规格列表 list of tuples，或者直接是最大功率数值(MW)
@@ -991,7 +1147,11 @@ def export_to_excel(connections_details, filename):
     df = pd.DataFrame(data)
 
     # 汇总统计
-    n_circuits = sum(1 for conn in connections_details if conn["source"] == "substation" or conn["target"] == "substation")
+    n_circuits = sum(
+        1
+        for conn in connections_details
+        if conn["source"] == "substation" or conn["target"] == "substation"
+    )
     summary = {
         "Total Cost (¥)": df["Cost (¥)"].sum(),
         "Total Effective Length (m)": df["Effective Length (m)"].sum(),
@@ -1023,7 +1183,11 @@ def export_all_scenarios_to_excel(results, filename):
             summary_data = []
             for res in results:
                 # 获取回路数 (通过统计从升压站发出的连接)
-                n_circuits = sum(1 for conn in res["eval"]["details"] if conn["source"] == "substation" or conn["target"] == "substation")
+                n_circuits = sum(
+                    1
+                    for conn in res["eval"]["details"]
+                    if conn["source"] == "substation" or conn["target"] == "substation"
+                )
 
                 summary_data.append(
                     {
@@ -1230,7 +1394,11 @@ def visualize_design(
 
 # 7. 主函数：比较两种设计方法
 def compare_design_methods(
-    excel_path=None, n_clusters_override=None, interactive=True, plot_results=True
+    excel_path=None,
+    n_clusters_override=None,
+    interactive=True,
+    plot_results=True,
+    use_ga=False,
 ):
     """
     比较MST和三种电缆方案下的K-means设计方法
@@ -1411,7 +1579,11 @@ def compare_design_methods(
             voltage=voltage,
             power_factor=power_factor,
         )
-        n_circuits_base = sum(1 for d in eval_base["details"] if d["source"] == "substation" or d["target"] == "substation")
+        n_circuits_base = sum(
+            1
+            for d in eval_base["details"]
+            if d["source"] == "substation" or d["target"] == "substation"
+        )
         comparison_results.append(
             {
                 "name": base_name,
@@ -1441,7 +1613,11 @@ def compare_design_methods(
             voltage=voltage,
             power_factor=power_factor,
         )
-        n_circuits_rot = sum(1 for d in eval_rot["details"] if d["source"] == "substation" or d["target"] == "substation")
+        n_circuits_rot = sum(
+            1
+            for d in eval_rot["details"]
+            if d["source"] == "substation" or d["target"] == "substation"
+        )
         comparison_results.append(
             {
                 "name": rot_name,
@@ -1471,7 +1647,11 @@ def compare_design_methods(
             voltage=voltage,
             power_factor=power_factor,
         )
-        n_circuits_ew = sum(1 for d in eval_ew["details"] if d["source"] == "substation" or d["target"] == "substation")
+        n_circuits_ew = sum(
+            1
+            for d in eval_ew["details"]
+            if d["source"] == "substation" or d["target"] == "substation"
+        )
         comparison_results.append(
             {
                 "name": ew_name,
@@ -1486,6 +1666,49 @@ def compare_design_methods(
             f"  [Esau-Williams] Cost: ¥{eval_ew['total_cost']:,.2f} | Loss: {eval_ew['total_loss']:.2f} kW | Circuits: {n_circuits_ew}"
         )
 
+        if use_ga:
+            # --- Run 4: Genetic Algorithm ---
+            ga_name = f"{name} (GA)"
+            conns_ga, turbines_ga = design_with_ga(
+                turbines.copy(),
+                substation,
+                current_specs,
+                voltage,
+                power_factor,
+                system_params,
+                evaluate_func=evaluate_design,
+                total_invest_func=total_investment,
+                get_max_capacity_func=get_max_cable_capacity_mw,
+            )
+            eval_ga = evaluate_design(
+                turbines,
+                conns_ga,
+                substation,
+                cable_specs=current_specs,
+                is_offshore=is_offshore,
+                method_name=ga_name,
+                voltage=voltage,
+                power_factor=power_factor,
+            )
+            n_circuits_ga = sum(
+                1
+                for d in eval_ga["details"]
+                if d["source"] == "substation" or d["target"] == "substation"
+            )
+            comparison_results.append(
+                {
+                    "name": ga_name,
+                    "cost": eval_ga["total_cost"],
+                    "loss": eval_ga["total_loss"],
+                    "eval": eval_ga,
+                    "turbines": turbines_ga,
+                    "specs": current_specs,
+                }
+            )
+            print(
+                f"  [GA] Cost: ¥{eval_ga['total_cost']:,.2f} | Loss: {eval_ga['total_loss']:.2f} kW | Circuits: {n_circuits_ga}"
+            )
+
         # 记录最佳
         if eval_rot["total_cost"] < best_cost:
             best_cost = eval_rot["total_cost"]
@@ -1500,7 +1723,11 @@ def compare_design_methods(
         # 可视化 (只画 Base 版本)
         ax_idx = i + 1
         if plot_results and ax_idx < 4:
-            n_circuits = sum(1 for d in eval_base["details"] if d["source"] == "substation" or d["target"] == "substation")
+            n_circuits = sum(
+                1
+                for d in eval_base["details"]
+                if d["source"] == "substation" or d["target"] == "substation"
+            )
             title = f"{base_name} ({n_circuits} circuits)\nCost: ¥{eval_base['total_cost'] / 10000:.2f}万"
             visualize_design(
                 turbines_base, substation, eval_base["details"], title, ax=axes[ax_idx]
@@ -1542,9 +1769,15 @@ def compare_design_methods(
                 best_idx = i
 
             # 获取回路数 (通过统计从升压站发出的连接)
-            n_circuits = sum(1 for conn in res["eval"]["details"] if conn["source"] == "substation" or conn["target"] == "substation")
+            n_circuits = sum(
+                1
+                for conn in res["eval"]["details"]
+                if conn["source"] == "substation" or conn["target"] == "substation"
+            )
 
-            print(f"  {i + 1}. {res['name']} - Cost: ¥{res['cost']:,.2f} | Circuits: {n_circuits}")
+            print(
+                f"  {i + 1}. {res['name']} - Cost: ¥{res['cost']:,.2f} | Circuits: {n_circuits}"
+            )
 
         print(f"推荐方案: {comparison_results[best_idx]['name']} (默认)")