ga-pso java代码

首先，GA-PSO（遗传算法-粒子群优化算法）是一种结合了遗传算法和粒子群优化算法的进化计算算法，主要用于优化问题的求解。下面是一个使用Java语言实现GA-PSO算法的代码示例：

import java.util.Random;

public class GAPSO {
    private static final int POPULATION_SIZE = 50; // 种群规模
    private static final int MAX_ITERATION = 100; // 最大迭代次数

    private static final int DIMENSION = 10; // 问题的维度
    private static final double C1 = 2.0; // 学习因子C1
    private static final double C2 = 2.0; // 学习因子C2
    private static final double V_MAX = 0.5; // 最大速度
    private static final double MUTATION_RATE = 0.01; // 变异率

    private static final int GENE_LENGTH = 10; // 基因长度
    private static final int MAX_BINARY = (int) (Math.pow(2, GENE_LENGTH) - 1); // 最大十进制数

    private static final Random random = new Random();

    private static class Particle {
        int[] velocity = new int[DIMENSION]; // 粒子的速度
        int[] position = new int[DIMENSION]; // 粒子的当前位置
        double fitness; // 适应度

        int[] pBest = new int[DIMENSION]; // 个体最优解
        double pBestFitness; // 个体最优解对应的适应度

        Particle() {
            // 初始化粒子的位置和速度
            for (int i = 0; i < DIMENSION; i++) {
                position[i] = random.nextInt(MAX_BINARY);
                velocity[i] = random.nextInt(MAX_BINARY);
            }
        }
    }

    private static class Population {
        Particle[] particles; // 种群中的所有粒子

        Population() {
            particles = new Particle[POPULATION_SIZE];
            for (int i = 0; i < POPULATION_SIZE; i++) {
                particles[i] = new Particle();
            }
        }
    }

    private static double evaluate(int[] position) {
        // 计算适应度，这里根据实际情况定义适应度函数
        // 适应度越高，解越好
        double fitness = 0;
        for (int i = 0; i < DIMENSION; i++) {
            fitness += position[i];
        }
        return fitness;
    }

    private static void updatePBest(Particle particle) {
        // 更新个体最优解
        double fitness = evaluate(particle.position);
        if (fitness > particle.pBestFitness) {
            particle.pBestFitness = fitness;
            System.arraycopy(particle.position, 0, particle.pBest, 0, DIMENSION);
        }
    }

    private static void updateGBest(Particle[] particles, int[] gBest) {
        // 更新全局最优解
        double gBestFitness = Double.NEGATIVE_INFINITY;
        for (Particle particle : particles) {
            if (particle.pBestFitness > gBestFitness) {
                gBestFitness = particle.pBestFitness;
                System.arraycopy(particle.pBest, 0, gBest, 0, DIMENSION);
            }
        }
    }

    private static void updateVelocity(Particle particle, int[] gBest) {
        // 更新粒子的速度
        for (int i = 0; i < DIMENSION; i++) {
            double r1 = random.nextDouble();
            double r2 = random.nextDouble();
            particle.velocity[i] = (int) (particle.velocity[i] + C1 * r1 * (particle.pBest[i] - particle.position[i])
                    + C2 * r2 * (gBest[i] - particle.position[i]));

            // 控制速度不超过最大速度
            if (particle.velocity[i] > V_MAX) {
                particle.velocity[i] = (int) V_MAX;
            } else if (particle.velocity[i] < -V_MAX) {
                particle.velocity[i] = (int) -V_MAX;
            }
        }
    }

    private static void updatePosition(Particle particle) {
        // 更新粒子的位置
        for (int i = 0; i < DIMENSION; i++) {
            particle.position[i] = (int) (particle.position[i] + particle.velocity[i]);

            // 控制位置不超过边界
            if (particle.position[i] > MAX_BINARY) {
                particle.position[i] = MAX_BINARY;
            } else if (particle.position[i] < 0) {
                particle.position[i] = 0;
            }
        }
    }

    public static void main(String[] args) {
        Population population = new Population();
        int[] gBest = new int[DIMENSION]; // 全局最优解
        double[] fitness = new double[POPULATION_SIZE]; // 每个粒子的适应度

        for (int iter = 0; iter < MAX_ITERATION; iter++) {
            // 更新每个粒子的个体最优解和全局最优解
            for (int i = 0; i < POPULATION_SIZE; i++) {
                Particle particle = population.particles[i];
                updatePBest(particle);
                fitness[i] = particle.pBestFitness;
            }
            updateGBest(population.particles, gBest);

            // 更新每个粒子的速度和位置
            for (int i = 0; i < POPULATION_SIZE; i++) {
                Particle particle = population.particles[i];
                updateVelocity(particle, gBest);
                updatePosition(particle);

                // 根据变异率进行变异
                if (random.nextDouble() < MUTATION_RATE) {
                    particle.position[random.nextInt(DIMENSION)] = random.nextInt(MAX_BINARY);
                }
            }

            // 输出当前迭代的全局最优解的适应度
            double gBestFitness = evaluate(gBest);
            System.out.println("Iteration " + (iter + 1) + ": " + gBestFitness);
        }
    }
}

以上是一个使用Java语言实现GA-PSO算法的简化版代码。在实际应用中，根据具体的问题和需求，可能需要根据具体情况对算法进行适当的修改和优化。