问题，一个rv减速器,由一个针齿销N，两个摆线轮B和两个曲轴P组成，一个针齿销有三个参数，一个摆线轮有5个参数，一个曲轴有2个参数，只有参数满足一定条件，才能成功装配，现在有50个针齿销，100个摆线轮B和100个曲轴P，基于粒子群算法，提高rv减速器的产量，Java代码

由于缺乏具体的参数和装配条件，以下代码仅为示例，需要根据实际情况进行修改和调整。

import java.util.ArrayList;
import java.util.List;
import java.util.Random;

public class PSO {

private static final int PARTICLE_NUM = 30;// 粒子数量
private static final int ITERATE_NUM = 100;// 迭代次数
private static final int DIMENSION = 3 + 5 * 2;// 维度，包括针齿销、摆线轮和曲轴的参数
private static final double C1 = 2.0;// 学习因子
private static final double C2 = 2.0;// 学习因子
private static final double W = 0.8;// 惯性因子
private static final int MAX_V = 5;// 最大速度

private static final Random random = new Random();

public static void main(String[] args) {
List<Particle> particles = new ArrayList<>();
for (int i = 0; i < PARTICLE_NUM; i++) {
particles.add(new Particle(DIMENSION));
}
Particle gBest = getGlobalBest(particles);
for (int i = 0; i < ITERATE_NUM; i++) {
for (Particle particle : particles) {
double[] v = particle.getV();
double[] x = particle.getX();
double[] pBest = particle.getPBest();
for (int j = 0; j < DIMENSION; j++) {
double r1 = random.nextDouble();
double r2 = random.nextDouble();
v[j] = W * v[j] + C1 * r1 * (pBest[j] - x[j]) + C2 * r2 * (gBest.getX()[j] - x[j]);
if (v[j] > MAX_V) {
v[j] = MAX_V;
}
if (v[j] < -MAX_V) {
v[j] = -MAX_V;
}
x[j] += v[j];
}
double fitness = calculateFitness(x);// 计算当前粒子的适应度
if (fitness > particle.getFitness()) {// 判断是否更新个体最优解
particle.setPBest(x);
particle.setFitness(fitness);
}
if (fitness > gBest.getFitness()) {// 判断是否更新全局最优解
gBest = new Particle(x, fitness);
}
}
System.out.println("Iteration " + (i + 1) + ": " + gBest.getFitness());
}
}

private static double calculateFitness(double[] x) {
// 根据参数计算适应度
return 0;
}

private static Particle getGlobalBest(List<Particle> particles) {
Particle gBest = particles.get(0);
for (Particle particle : particles) {
if (particle.getFitness() > gBest.getFitness()) {
gBest = particle;
}
}
return gBest;
}

static class Particle {
private double[] x;// 当前位置
private double[] v;// 当前速度
private double[] pBest;// 个体最优解
private double fitness;// 适应度

public Particle(int dimension) {
x = new double[dimension];
v = new double[dimension];
pBest = new double[dimension];
for (int i = 0; i < dimension; i++) {
x[i] = random.nextDouble();// 初始位置随机生成
v[i] = random.nextDouble() * MAX_V;// 初始速度随机生成
pBest[i] = x[i];
}
fitness = calculateFitness(x);
}

public Particle(double[] x, double fitness) {
this.x = x;
this.v = new double[x.length];
this.pBest = x;
this.fitness = fitness;
}

public double[] getX() {
return x;
}

public double[] getV() {
return v;
}

public double[] getPBest() {
return pBest;
}

public double getFitness() {
return fitness;
}

public void setPBest(double[] pBest) {
this.pBest = pBest;
}

public void setFitness(double fitness) {
this.fitness = fitness;
}
}
}