淘客自己做网站,phpstudy怎么创建网站,怎么做关键词优化排名,wordpress+标签消失文章目录 一、问题描述二、思路分析三、解决方案3.1 贪心启发式算法3.2 群体智能算法#xff08;蜘蛛猴优化算法#xff09; 四、总结 一、问题描述
选址问题是指在规划区域里选择一个或多个设施的位置#xff0c;使得目标最优。
按照规划区域的结构划分#xff0c;可以将… 文章目录 一、问题描述二、思路分析三、解决方案3.1 贪心启发式算法3.2 群体智能算法蜘蛛猴优化算法 四、总结 一、问题描述
选址问题是指在规划区域里选择一个或多个设施的位置使得目标最优。
按照规划区域的结构划分可以将选址问题分为 1.连续选址问题设施可以在给定范围的任意位置选址设施的候选位置为无穷多。 2.离散选址问题设施的候选位置是有限且较少的实际中最常遇到这类问题。 3.网格选址问题规划区域被划分为许多的小单元每个设施占据其中有限个单元。
本博客将根据下面的例题介绍连续选址问题的求解思路 其中问题1为运输问题建议先阅读我的另一篇博客再来看本博客【运筹优化】运输问题建模 Java调用Cplex求解 二、思路分析
问题2属于连续选址问题它的数学模型和问题1的数学模型 Q 1 Q_1 Q1类似唯一不同之处在于问题2中 d i j d_{ij} dij也是一个决策变量。因此它的数学模型是非线性的无法直接通过Cplex直接求解。对于凸的无约束非线性模型可以使用梯度下降法或牛顿法求出其最优解对于其他非线性模型通常使用启发式算法或群体智能算法寻求其较优解。由于不确定问题2模型是否为凸因此本人采用了一个贪心启发式算法和一个群体智能算法(蜘蛛猴优化算法)对问题2进行求解。 三、解决方案
3.1 贪心启发式算法
首先介绍贪心启发式算法为了更好地描述给出下面的线性规划模型 该模型的含义为给定一个已知位置的料场和若干个工地的信息确保将料场的储量全部运输到各个工地并使得总运输成本最小。
然后介绍贪心启发式算法的基本思想每次为一个料场 i i i定位置基于每个工地的坐标和当前剩余需求量计算一个加权中心点作为当前料场 i i i的坐标在确定了料场 i i i的位置之后就可以将料场 i i i和当前工地的信息代入到线性的 Q 2 i Q^i_2 Q2i模型中调用Cplex进行求解得到料场 i i i的最小成本的运输方案后再更新每个工地的剩余需求量然后以同样的步骤为下一个料场确定位置并更新工地信息直到每个工地的位置都被确定。确定了每个工地的位置之后再求解 Q 1 Q_1 Q1模型即可得到当前料场坐标下最优的运输方案。
贪心启发式的Java代码实现如下
public class Answer2 {/*** 料场对象*/AllArgsConstructorstatic class Stockyard {/*** x,y坐标和储量c*/double x, y, c;}/*** 工地对象*/AllArgsConstructorstatic class ConstructionSite {/*** x,y坐标和需求d*/double x, y, d;}private static double calcDistance(double x1, double y1, double x2, double y2) {return Math.sqrt(Math.pow(x1 - x2, 2) Math.pow(y1 - y2, 2));}private static ListConstructionSite getInitConstructionSiteList() {ListConstructionSite constructionSiteList new ArrayList();constructionSiteList.add(new ConstructionSite(1.25, 1.25, 3));constructionSiteList.add(new ConstructionSite(8.75, 0.75, 5));constructionSiteList.add(new ConstructionSite(0.5, 4.75, 4));constructionSiteList.add(new ConstructionSite(5.75, 5, 7));constructionSiteList.add(new ConstructionSite(3, 6.5, 6));constructionSiteList.add(new ConstructionSite(7.25, 7.75, 11));return constructionSiteList;}public static void main(String[] args) throws Exception {// 浮点型精度误差double EPS 1e-06;// 料场列表ListStockyard stockyardList new ArrayList();stockyardList.add(new Stockyard(0, 0, 20));stockyardList.add(new Stockyard(0, 0, 20));stockyardList.sort(new ComparatorStockyard() {Overridepublic int compare(Stockyard o1, Stockyard o2) {return -Double.compare(o1.c, o2.c);}});// 工地列表ListConstructionSite constructionSiteList getInitConstructionSiteList();// 初始化距离矩阵double[][] distanceMatrix new double[stockyardList.size()][constructionSiteList.size()];// 开始循环for (int i 0; i stockyardList.size(); i) {// 计算总需求量double totalDemand constructionSiteList.stream().map(constructionSite - {return constructionSite.d;}).reduce(0d, Double::sum);// 计算加权中心点作为料场i的坐标stockyardList.get(i).x 0d;stockyardList.get(i).y 0d;for (ConstructionSite constructionSite : constructionSiteList) {stockyardList.get(i).x (constructionSite.x * constructionSite.d / totalDemand);stockyardList.get(i).y (constructionSite.y * constructionSite.d / totalDemand);}// 计算距离矩阵for (int j 0; j distanceMatrix[i].length; j) {distanceMatrix[i][j] calcDistance(stockyardList.get(i).x, stockyardList.get(i).y, constructionSiteList.get(j).x, constructionSiteList.get(j).y);}if (totalDemand stockyardList.get(i).c) {break;}// 然后强制料场i必须送够c_i的量并计算最小成本的运输方案// 开始建模IloCplex cplex new IloCplex();// 声明变量IloNumVar[] x new IloNumVar[constructionSiteList.size()];for (int j 0; j x.length; j) {x[j] cplex.numVar(0, constructionSiteList.get(j).d);}// 构造约束1不能超出每个工地的需求for (int j 0; j constructionSiteList.size(); j) {cplex.addLe(x[j], constructionSiteList.get(j).d);}// 构造约束2料场中的储量必须全部送出cplex.addEq(cplex.sum(x), stockyardList.get(i).c);// 声明目标函数cplex.addMinimize(cplex.scalProd(x, distanceMatrix[i]));// 配置cplexcplex.setOut(null);cplex.setWarning(null);cplex.setParam(IloCplex.DoubleParam.EpOpt, EPS);// 开始求解if (cplex.solve()) {// 更新每个工地的需求量for (int j 0; j x.length; j) {constructionSiteList.get(j).d - cplex.getValue(x[j]);}} else {System.err.println(此题无解);}// 结束模型cplex.end();}for (int i 0; i stockyardList.size(); i) {System.out.println(料场 (i 1) 的坐标为: ( stockyardList.get(i).x , stockyardList.get(i).y ));}// 重新获取初始化的工地列表constructionSiteList getInitConstructionSiteList();// 开始建模IloCplex cplex new IloCplex();// 声明变量IloNumVar[][] x new IloNumVar[stockyardList.size()][constructionSiteList.size()];for (int i 0; i x.length; i) {for (int j 0; j x[i].length; j) {x[i][j] cplex.numVar(0, Math.min(stockyardList.get(i).c, constructionSiteList.get(j).d));}}// 构造约束1必须满足每个工地的需求for (int j 0; j constructionSiteList.size(); j) {IloLinearNumExpr expr cplex.linearNumExpr();for (int i 0; i x.length; i) {expr.addTerm(1, x[i][j]);}cplex.addEq(expr, constructionSiteList.get(j).d);}// 构造约束2不能超过每个料场的储量for (int i 0; i stockyardList.size(); i) {cplex.addLe(cplex.sum(x[i]), stockyardList.get(i).c);}// 声明目标函数IloLinearNumExpr target cplex.linearNumExpr();for (int i 0; i x.length; i) {for (int j 0; j x[i].length; j) {target.addTerm(distanceMatrix[i][j], x[i][j]);}}cplex.addMinimize(target);// 配置cplexcplex.setOut(null);cplex.setWarning(null);cplex.setParam(IloCplex.DoubleParam.EpOpt, EPS);// 开始求解long s System.currentTimeMillis();if (cplex.solve()) {System.out.println(最小吨千米数为: cplex.getObjValue());for (int i 0; i stockyardList.size(); i) {for (int j 0; j x[i].length; j) {double xValue cplex.getValue(x[i][j]);if (xValue EPS) {System.out.println(料场 (i 1) 向工地 (j 1) 运输 xValue 吨水泥);}}}System.out.println(求解用时: (System.currentTimeMillis() - s) / 1000d s);} else {System.err.println(此题无解);}// 结束模型cplex.end();}
}运行结果如下
料场1的坐标为: ( 5.208333333333333 , 5.159722222222222 )
料场2的坐标为: ( 4.90625 , 3.59375 )
最小吨千米数为: 114.45936860248577
料场1向工地4运输7.0吨水泥
料场1向工地5运输2.0吨水泥
料场1向工地6运输11.0吨水泥
料场2向工地1运输3.0吨水泥
料场2向工地2运输5.0吨水泥
料场2向工地3运输4.0吨水泥
料场2向工地5运输4.0吨水泥
求解用时: 0.0 s3.2 群体智能算法蜘蛛猴优化算法
然后是使用了蜘蛛猴优化算法。每个蜘蛛猴的变量数组表示了每个料场的坐标。设置蜘蛛猴数量为50迭代次数为60局部领导者决策阶段的更新几率为0.1局部领导者阶段的更新几率为0.8最大组数为5料场的坐标上下界分别为10和-10。
Java代码实现如下
public class SMO_Solver {/*** 料场对象*/AllArgsConstructorstatic class Stockyard {/*** x,y坐标和储量c*/double x, y, c;}/*** 工地对象*/AllArgsConstructorstatic class ConstructionSite {/*** x,y坐标和需求d*/double x, y, d;}private double calcDistance(double x1, double y1, double x2, double y2) {return Math.sqrt(Math.pow(x1 - x2, 2) Math.pow(y1 - y2, 2));}private static ListConstructionSite getInitConstructionSiteList() {ListConstructionSite constructionSiteList new ArrayList();constructionSiteList.add(new ConstructionSite(1.25, 1.25, 3));constructionSiteList.add(new ConstructionSite(8.75, 0.75, 5));constructionSiteList.add(new ConstructionSite(0.5, 4.75, 4));constructionSiteList.add(new ConstructionSite(5.75, 5, 7));constructionSiteList.add(new ConstructionSite(3, 6.5, 6));constructionSiteList.add(new ConstructionSite(7.25, 7.75, 11));return constructionSiteList;}ListConstructionSite constructionSiteList;ListStockyard stockyardList;double EPS 1e-06;public SMO_Solver(ListStockyard stockyardList, ListConstructionSite constructionSiteList) throws IloException {this.constructionSiteList constructionSiteList;this.stockyardList stockyardList;}// 蜘蛛猴对象class SpiderMonkey {// 当前蜘蛛猴坐标自变量数组double[] curVars;// 当前自变量对应的目标函数值double curObjValue;// 适应度解决最小化问题所以适应度为目标函数值的倒数double fit;// 全参数构造public SpiderMonkey(double[] curVars, double curObjValue, double fit) {this.curVars curVars;this.curObjValue curObjValue;this.fit fit;}}// 算法参数// 蜘蛛猴群ListSpiderMonkey[] spiderMonkeyList new ArrayList();// 局部领导者ListSpiderMonkey localLeaderList new ArrayList();// 最好的蜘蛛猴(全局领导者)SpiderMonkey bestSpiderMonkey;// 随机数对象Random random new Random();// 最大迭代次数int maxGen 60;// 蜘蛛猴数量int spiderMonkeyNum 50;// 局部搜索次数(一般等于蜘蛛猴数量)int localSearchCount spiderMonkeyNum;// 局部领导者决策阶段的更新几率double LLDP_PR 0.1;// 局部领导者阶段的更新几率double LLP_PR 0.8;// 变量维度数int varNum 4;// 最大组数(一般要至少保证每组里有10个蜘蛛猴)int maxgroupNum spiderMonkeyNum / 10;// 变量的上下界double[] ub new double[]{10, 10, 10, 10};double[] lb new double[]{-10, -10, -10, -10};// 局部计数器int[] localLimitCount new int[]{0};// 停止条件int limitCnt 30;// 全局计数器int globalLimitCount;// 记录迭代过程public double[][][] positionArr;// 记录迭代器的行数int curC 0;// 是否开启贪心机制只接受比当前解好的解boolean greedy true;// 求解主函数public void solve() {// 初始化蜘蛛猴种群initSpiderMonkeys();// 开始迭代for (int t 0; t maxGen; t) {System.out.println(t : bestSpiderMonkey.curObjValue);// 局部领导者阶段LLP所有的蜘蛛猴都有机会更新自己LLP();// 全局领导者阶段GLP轮盘赌随机选取偏向于对fit值大的蜘蛛猴进行更新GLP();// 全局领导者学习阶段如果全局领导者有更新则globalLimitCount0否则globalLimitCountGLLP();// 局部领导者学习阶段如果局部领导者有更新则localLimitCount0否则localLimitCountLLLP();// 局部领导者决策阶段LLDP();// 全局领导者决策阶段GLDP();}// 输出最好的结果System.out.println(变量取值为 Arrays.toString(bestSpiderMonkey.curVars));System.out.println(最优解为 bestSpiderMonkey.curObjValue);}// 全局领导者决策阶段private void GLDP() {if (globalLimitCount limitCnt) {globalLimitCount 0;if (spiderMonkeyList.size() maxgroupNum) {// 分裂ListSpiderMonkey tempList new ArrayList();for (SpiderMonkey[] spiderMonkeys : spiderMonkeyList) {tempList.addAll(Arrays.asList(spiderMonkeys));}tempList.sort(new ComparatorSpiderMonkey() {Overridepublic int compare(SpiderMonkey o1, SpiderMonkey o2) {return Double.compare(o2.fit, o1.fit);}});//int groupNum spiderMonkeyList.size() 1;spiderMonkeyList new ArrayList();int avgNum spiderMonkeyNum / groupNum;for (int i 0; i groupNum - 1; i) {SpiderMonkey[] spiderMonkeys new SpiderMonkey[avgNum];for (int j 0; j avgNum; j) {spiderMonkeys[j] copySpiderMonkey(tempList.remove(0));}spiderMonkeyList.add(spiderMonkeys);}spiderMonkeyList.add(tempList.toArray(new SpiderMonkey[0]));localLimitCount new int[groupNum];} else {// 融合SpiderMonkey[] spiderMonkeys new SpiderMonkey[spiderMonkeyNum];int i 0;for (SpiderMonkey[] monkeys : spiderMonkeyList) {for (SpiderMonkey monkey : monkeys) {spiderMonkeys[i] copySpiderMonkey(monkey);}}spiderMonkeyList new ArrayList();spiderMonkeyList.add(spiderMonkeys);localLimitCount new int[]{0};}// 更新局部领导者localLeaderList new ArrayList();for (SpiderMonkey[] spiderMonkeys : spiderMonkeyList) {localLeaderList.add(copySpiderMonkey(spiderMonkeys[0]));int index localLeaderList.size() - 1;for (int i 1; i spiderMonkeys.length; i) {if (localLeaderList.get(index).fit spiderMonkeys[i].fit) {localLeaderList.set(index, copySpiderMonkey(spiderMonkeys[i]));}}}}}// 局部领导者决策阶段private void LLDP() {int c 0;for (int i 0; i spiderMonkeyList.size(); i) {SpiderMonkey[] spiderMonkeys spiderMonkeyList.get(i);if (localLimitCount[i] limitCnt) {for (int j 0; j spiderMonkeys.length; j) {SpiderMonkey tempSpiderMonkey copySpiderMonkey(spiderMonkeys[j]);for (int m 0; m varNum; m) {if (random.nextDouble() LLDP_PR) {tempSpiderMonkey.curVars[m] lb[m] random.nextDouble() * (ub[m] - lb[m]);} else {double moveDist random.nextDouble() * (bestSpiderMonkey.curVars[m] - tempSpiderMonkey.curVars[m]) random.nextDouble() * (spiderMonkeys[random.nextInt(spiderMonkeys.length)].curVars[m] - tempSpiderMonkey.curVars[m]);moveSpiderMonkey(tempSpiderMonkey, m, moveDist);}}tempSpiderMonkey.curObjValue getObjValue(tempSpiderMonkey.curVars);tempSpiderMonkey.fit 1 / tempSpiderMonkey.curObjValue;if (greedy) {if (tempSpiderMonkey.fit spiderMonkeys[j].fit) {spiderMonkeys[j] tempSpiderMonkey;}} else {spiderMonkeys[j] tempSpiderMonkey;}}}for (int j 0; j spiderMonkeys.length; j) {for (int m 0; m spiderMonkeys[j].curVars.length; m) {positionArr[curC][c][m] spiderMonkeys[j].curVars[m];}c;}}curC;}// 局部领导者学习阶段如果局部领导者有更新则localLimitCount0否则localLimitCountprivate void LLLP() {for (int i 0; i spiderMonkeyList.size(); i) {boolean isUpdate false;for (SpiderMonkey spiderMonkey : spiderMonkeyList.get(i)) {if (localLeaderList.get(i).fit spiderMonkey.fit) {localLeaderList.set(i, copySpiderMonkey(spiderMonkey));isUpdate true;}}if (isUpdate) {localLimitCount[i] 0;} else {localLimitCount[i];}}}// 全局领导者学习阶段如果全局领导者有更新则globalLimitCount0否则globalLimitCountprivate void GLLP() {boolean isUpdate false;for (int i 0; i spiderMonkeyList.size(); i) {for (SpiderMonkey spiderMonkey : spiderMonkeyList.get(i)) {if (spiderMonkey.fit bestSpiderMonkey.fit) {bestSpiderMonkey copySpiderMonkey(spiderMonkey);isUpdate true;}}}if (isUpdate) {globalLimitCount 0;} else {globalLimitCount;}}// 全局领导者阶段GLP轮盘赌随机选取偏向于对fit值大的蜘蛛猴进行更新private void GLP() {int c 0;for (int i 0; i spiderMonkeyList.size(); i) {SpiderMonkey[] spiderMonkeys spiderMonkeyList.get(i);// 计算fit总和double totalFit 0;for (SpiderMonkey spiderMonkey : spiderMonkeys) {totalFit spiderMonkey.fit;}// 轮盘赌的累计概率数组double[] p new double[spiderMonkeys.length];for (int j 0; j p.length; j) {p[j] (spiderMonkeys[j].fit / totalFit) (j 0 ? 0 : p[j - 1]);}// 局部搜索for (int j 0; j localSearchCount; j) {double r random.nextDouble();for (int k 0; k p.length; k) {if (r p[k]) {for (int m 0; m varNum; m) {double moveDist random.nextDouble() * (bestSpiderMonkey.curVars[m] - spiderMonkeys[k].curVars[m]) (random.nextDouble() - 0.5) * 2 * (spiderMonkeys[random.nextInt(spiderMonkeys.length)].curVars[m] - spiderMonkeys[k].curVars[m]);moveSpiderMonkey(spiderMonkeys[k], m, moveDist);}spiderMonkeys[k].curObjValue getObjValue(spiderMonkeys[k].curVars);spiderMonkeys[k].fit 1 / spiderMonkeys[k].curObjValue;break;}}}for (int j 0; j spiderMonkeys.length; j) {for (int m 0; m spiderMonkeys[j].curVars.length; m) {positionArr[curC][c][m] spiderMonkeys[j].curVars[m];}c;}spiderMonkeyList.set(i, spiderMonkeys);}curC;}// 局部领导者阶段LLP所有的蜘蛛猴都有机会更新自己private void LLP() {int c 0;for (int i 0; i spiderMonkeyList.size(); i) {SpiderMonkey[] spiderMonkeys spiderMonkeyList.get(i);SpiderMonkey localLeader localLeaderList.get(i);for (int j 0; j spiderMonkeys.length; j) {// 以一定几率更新自己if (random.nextDouble() LLP_PR) {SpiderMonkey tempSpiderMonkey copySpiderMonkey(spiderMonkeys[j]);for (int m 0; m varNum; m) {double moveDist random.nextDouble() * (localLeader.curVars[m] - tempSpiderMonkey.curVars[m]) (random.nextDouble() - 0.5) * 2 * (spiderMonkeys[random.nextInt(spiderMonkeys.length)].curVars[m] - tempSpiderMonkey.curVars[m]);moveSpiderMonkey(tempSpiderMonkey, m, moveDist);}tempSpiderMonkey.curObjValue getObjValue(tempSpiderMonkey.curVars);tempSpiderMonkey.fit 1 / tempSpiderMonkey.curObjValue;if (greedy) {if (tempSpiderMonkey.fit spiderMonkeys[j].fit) {spiderMonkeys[j] tempSpiderMonkey;}} else {spiderMonkeys[j] tempSpiderMonkey;}}for (int m 0; m spiderMonkeys[j].curVars.length; m) {positionArr[curC][c][m] spiderMonkeys[j].curVars[m];}c;}spiderMonkeyList.set(i, spiderMonkeys);}curC;}// 初始化蜘蛛猴种群private void initSpiderMonkeys() {positionArr new double[3 * maxGen][spiderMonkeyNum][varNum];SpiderMonkey[] spiderMonkeys new SpiderMonkey[spiderMonkeyNum];SpiderMonkey localLeader null;for (int i 0; i spiderMonkeyNum; i) {spiderMonkeys[i] getRandomSpiderMonkey();if (i 0) {bestSpiderMonkey copySpiderMonkey(spiderMonkeys[0]);localLeader copySpiderMonkey(spiderMonkeys[0]);} else {if (bestSpiderMonkey.fit spiderMonkeys[i].fit) {bestSpiderMonkey copySpiderMonkey(spiderMonkeys[i]);localLeader copySpiderMonkey(spiderMonkeys[0]);}}}spiderMonkeyList.add(spiderMonkeys);localLeaderList.add(localLeader);}// 获取一个随机生成的蜘蛛猴SpiderMonkey getRandomSpiderMonkey() {double[] vars new double[varNum];for (int j 0; j vars.length; j) {vars[j] lb[j] random.nextDouble() * (ub[j] - lb[j]);}double objValue getObjValue(vars);return new SpiderMonkey(vars.clone(), objValue, 1 / objValue);}// 控制spiderMonkey在第m个维度上移动n个距离public void moveSpiderMonkey(SpiderMonkey spiderMonkey, int m, double n) {// 移动spiderMonkey.curVars[m] n;// 超出定义域的判断if (spiderMonkey.curVars[m] lb[m]) {spiderMonkey.curVars[m] lb[m];}if (spiderMonkey.curVars[m] ub[m]) {spiderMonkey.curVars[m] ub[m];}}/*** param vars 自变量数组* return 返回目标函数值*/IloCplex cplex new IloCplex();public double getObjValue(double[] vars) {try {// 更新距离for (int i 0; i vars.length; i) {if (i % 2 0) {stockyardList.get(i / 2).x vars[i];} else {stockyardList.get(i / 2).y vars[i];}}// 计算距离矩阵double[][] distanceMatrix new double[stockyardList.size()][constructionSiteList.size()];for (int i 0; i distanceMatrix.length; i) {Stockyard stockyard stockyardList.get(i);for (int j 0; j distanceMatrix[i].length; j) {ConstructionSite constructionSite constructionSiteList.get(j);distanceMatrix[i][j] calcDistance(stockyard.x, stockyard.y, constructionSite.x, constructionSite.y);}}// 开始建模
// cplex new IloCplex();// 声明变量IloNumVar[][] x new IloNumVar[stockyardList.size()][constructionSiteList.size()];for (int i 0; i x.length; i) {for (int j 0; j x[i].length; j) {x[i][j] cplex.numVar(0, Math.min(stockyardList.get(i).c, constructionSiteList.get(j).d));}}// 构造约束1必须满足每个工地的需求for (int j 0; j constructionSiteList.size(); j) {IloLinearNumExpr expr cplex.linearNumExpr();for (int i 0; i x.length; i) {expr.addTerm(1, x[i][j]);}cplex.addEq(expr, constructionSiteList.get(j).d);}// 构造约束2不能超过每个料场的储量for (int i 0; i stockyardList.size(); i) {cplex.addLe(cplex.sum(x[i]), stockyardList.get(i).c);}// 声明目标函数IloLinearNumExpr target cplex.linearNumExpr();for (int i 0; i x.length; i) {for (int j 0; j x[i].length; j) {target.addTerm(distanceMatrix[i][j], x[i][j]);}}cplex.addMinimize(target);// 配置cplexcplex.setOut(null);cplex.setWarning(null);cplex.setParam(IloCplex.DoubleParam.EpOpt, EPS);// 开始求解double obj;if (cplex.solve()) {obj cplex.getObjValue();} else {throw new RuntimeException(此题无解);}cplex.clearModel();return obj;} catch (Exception e) {throw new RuntimeException(e);}}// 复制蜘蛛猴SpiderMonkey copySpiderMonkey(SpiderMonkey old) {return new SpiderMonkey(old.curVars.clone(), old.curObjValue, old.fit);}public static void main(String[] args) throws IloException {// 料场列表ListStockyard stockyardList new ArrayList();stockyardList.add(new Stockyard(0, 0, 20));stockyardList.add(new Stockyard(0, 0, 20));// 工地列表ListConstructionSite constructionSiteList getInitConstructionSiteList();long s System.currentTimeMillis();new SMO_Solver(stockyardList, constructionSiteList).solve();System.out.println(总用时: (System.currentTimeMillis() - s) / 1000d s);}}输出结果如下
0 : 122.18394756340149
1 : 115.22137772493119
2 : 107.64015378143884
3 : 91.25562099863063
4 : 87.27034345689911
5 : 86.08239574989028
6 : 85.5990696901402
7 : 85.5675162336327
8 : 85.40967721594052
9 : 85.33079736839548
10 : 85.29533933219953
11 : 85.27770206804239
12 : 85.27194687104382
13 : 85.26919031512125
14 : 85.26862219676372
15 : 85.26750239234426
16 : 85.26663764799034
17 : 85.26636259651076
18 : 85.26628507350429
19 : 85.26618997863784
20 : 85.26605221867857
21 : 85.26605221867857
22 : 85.26604976682619
23 : 85.26604439739287
24 : 85.26604351700756
25 : 85.26604156579967
26 : 85.26604141563301
27 : 85.26604110466047
28 : 85.2660410153342
29 : 85.26604098426941
30 : 85.26604096946092
31 : 85.26604090826108
32 : 85.26604089012692
33 : 85.26604088253666
34 : 85.26604087662045
35 : 85.26604087515656
36 : 85.26604087344376
37 : 85.26604087292048
38 : 85.26604087263298
39 : 85.26604087239937
40 : 85.26604087223953
41 : 85.26604087214378
42 : 85.26604087212847
43 : 85.2660408721172
44 : 85.26604087211567
45 : 85.2660408721116
46 : 85.26604087211126
47 : 85.26604087211126
48 : 85.26604087211103
49 : 85.26604087211096
50 : 85.26604087211076
51 : 85.2660408721107
52 : 85.2660408721107
53 : 85.26604087211066
54 : 85.26604087211066
55 : 85.26604087211066
56 : 85.26604087211064
57 : 85.26604087211064
58 : 85.26604087211064
59 : 85.26604087211064
变量取值为[7.25, 7.75, 3.2548825928531566, 5.652332445501583]
最优解为85.26604087211064
总用时: 0.997 s四、总结
本博客分别介绍了一种贪心启发式算法和一种群体智能算法求解选址问题结果表明贪心启发式算法可以在极短的时间内得到较优的解而蜘蛛猴优化算法则可以在迭代多次后得到更好的解。
