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src/main/java/org/dromara/easyai/pso/PSO.java

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+package org.dromara.easyai.pso;
+
+import org.dromara.easyai.i.PsoFunction;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Random;
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description 粒子群
+ */
+public class PSO {
+    private float globalValue = -1;//当前全局最优值
+    private int times;//迭代次数
+    private List<Particle> allPar = new ArrayList<>();//全部粒子集合
+    private PsoFunction psoFunction;//粒子群执行函数
+    private float inertialFactor = 0.5f;//惯性因子
+    private float selfStudyFactor = 2;//个体学习因子
+    private float socialStudyFactor = 2;//社会学习因子
+    private boolean isMax;//取最大值还是最小值
+    private float[] allBest;//全局最佳位置
+    private Random random = new Random();
+    private float[] minBorder, maxBorder;
+    private float maxSpeed;
+    private float initSpeed;//初始速度
+    /**
+     * 初始化
+     *
+     * @param dimensionNub 维度
+     * @param minBorder  最小边界
+     * @param maxBorder 最大边界
+     * @param times 迭代次数
+     * @param particleNub 粒子数量
+     * @param psoFunction 适应函数
+     * @param inertialFactor 惯性因子
+     * @param selfStudyFactor 个体学习因子
+     * @param socialStudyFactor 社会学习因子
+     * @param isMax 最大值是否为最优
+     * @param maxSpeed 最大速度
+     * @param initSpeed 初始速度
+     * @throws Exception
+     */
+    public PSO(int dimensionNub, float[] minBorder, float[] maxBorder,
+               int times, int particleNub, PsoFunction psoFunction,
+               float inertialFactor, float selfStudyFactor, float socialStudyFactor
+            , boolean isMax, float maxSpeed, float initSpeed) {
+        this.initSpeed = initSpeed;
+        this.times = times;
+        this.psoFunction = psoFunction;
+        this.isMax = isMax;
+        allBest = new float[dimensionNub];
+        this.minBorder = minBorder;
+        this.maxBorder = maxBorder;
+        this.maxSpeed = maxSpeed;
+        if (inertialFactor > 0) {
+            this.inertialFactor = inertialFactor;
+        }
+        if (selfStudyFactor >= 0 && selfStudyFactor <= 4) {
+            this.selfStudyFactor = selfStudyFactor;
+        }
+        if (socialStudyFactor >= 0 && socialStudyFactor <= 4) {
+            this.socialStudyFactor = socialStudyFactor;
+        }
+        for (int i = 0; i < particleNub; i++) {//初始化生成粒子群
+            Particle particle = new Particle(dimensionNub);
+            allPar.add(particle);
+        }
+
+    }
+
+    public float[] getAllBest() {
+        return allBest;
+    }
+
+    public void setAllPar(List<Particle> allPar) {//外置粒子群注入
+        this.allPar = allPar;
+    }
+
+    public void start() throws Exception {//开始进行迭代
+        int size = allPar.size();
+        for (int i = 0; i < times; i++) {
+            for (int j = 0; j < size; j++) {
+                move(allPar.get(j), j);
+            }
+        }
+        //粒子群移动结束
+        // draw("/Users/lidapeng/Desktop/test/testOne/e2.jpg", fatherX, fatherY);
+    }
+
+    private void move(Particle particle, int id) throws Exception {//粒子群开始移动
+        float[] parameter = particle.getParameter();//当前粒子的位置
+        BestData[] bestData = particle.bestDataArray;//该粒子的信息
+        float value = psoFunction.getResult(parameter, id);
+        float selfValue = particle.selfBestValue;//局部最佳值
+        if (isMax) {//取最大值
+            if (value > globalValue) {//更新全局最大值
+                globalValue = value;
+                //更新全局最佳位置
+                for (int i = 0; i < allBest.length; i++) {
+                    allBest[i] = parameter[i];
+                }
+            }
+            if (value > selfValue) {//更新局部最大值
+                particle.selfBestValue = value;
+                //更新局部最佳位置
+                for (int i = 0; i < bestData.length; i++) {
+                    bestData[i].selfBestPosition = parameter[i];
+                }
+            }
+        } else {//取最小值
+            if (globalValue < 0 || value < globalValue) {//更新全局最小值
+                globalValue = value;
+                //更新全局最佳位置
+                for (int i = 0; i < allBest.length; i++) {
+                    allBest[i] = parameter[i];
+                }
+            }
+            if (selfValue < 0 || value < selfValue) {//更新全局最小值
+                particle.selfBestValue = value;
+                //更新局部最佳位置
+                for (int i = 0; i < bestData.length; i++) {
+                    bestData[i].selfBestPosition = parameter[i];
+                }
+            }
+        }
+        //先更新粒子每个维度的速度
+        for (int i = 0; i < bestData.length; i++) {
+            float speed = bestData[i].speed;//当前维度的速度
+            float pid = bestData[i].selfBestPosition;//当前自己的最佳位置
+            float selfPosition = parameter[i];//当前自己的位置
+            float pgd = allBest[i];//当前维度的全局最佳位置
+            //当前维度更新后的速度
+            speed = inertialFactor * speed + selfStudyFactor * random.nextFloat() * (pid - selfPosition)
+                    + socialStudyFactor * random.nextFloat() * (pgd - selfPosition);
+            if ((float)Math.abs(speed) > maxSpeed) {
+                if (speed > 0) {
+                    speed = maxSpeed;
+                } else {
+                    speed = -maxSpeed;
+                }
+            }
+            bestData[i].speed = speed;
+            //更新该粒子该维度新的位置
+            float position = selfPosition + speed;
+            if (minBorder != null) {
+                if (position < minBorder[i]) {
+                    position = minBorder[i];
+                }
+                if (position > maxBorder[i]) {
+                    position = maxBorder[i];
+                }
+            }
+            bestData[i].selfPosition = position;
+        }
+    }
+
+    class Particle {//粒子
+        private BestData[] bestDataArray;
+        private float selfBestValue = -1;//自身最优的值
+
+        private float[] getParameter() {//获取粒子位置信息
+            float[] parameter = new float[bestDataArray.length];
+            for (int i = 0; i < parameter.length; i++) {
+                parameter[i] = bestDataArray[i].selfPosition;
+            }
+            return parameter;
+        }
+
+        protected Particle(int dimensionNub) {//初始化随机位置
+            bestDataArray = new BestData[dimensionNub];
+            for (int i = 0; i < dimensionNub; i++) {
+                float position;
+                if (minBorder != null && maxBorder != null) {
+                    float min = minBorder[i];
+                    float max = maxBorder[i];
+                    float region = max - min + 1;
+                    position = random.nextInt((int) region) + min;//初始化该维度的位置
+                } else {
+                    position = random.nextFloat();
+                }
+                bestDataArray[i] = new BestData(position, initSpeed);
+            }
+        }
+    }
+
+    class BestData {//数据保存
+
+        private BestData(float selfPosition, float initSpeed) {
+            this.selfBestPosition = selfPosition;
+            this.selfPosition = selfPosition;
+            speed = initSpeed;
+        }
+
+        private float speed;//该粒子当前维度的速度
+        private float selfBestPosition;//当前维度自身最优的历史位置/自己最优位置的值
+        private float selfPosition;//当前维度自己现在的位置/也就是当前维度自己的值
+    }
+}