使用粒子群优化（PSO）算法在满足生产需求和约束条件的同时，尽量减少能源消耗 matlab代码.rar

clear; clc; numParticles = 30; maxIterations = 100; numJobs = 10; numMachines = 5; w = 0.6; c1 = 1.8; c2 = 1.8; maxSetupTime = 5; processingRates = randi([5, 15], numJobs, numMachines); energyProfiles = rand(numJobs, numMachines); particles = randi([1, numMachines], numParticles, numJobs); velocities = zeros(numParticles, numJobs); globalBestParticle = particles(1, :); globalBestFitness = calculateFitness(globalBestParticle, processingRates, energyProfiles); localBestParticles = particles; localBestFitnesses = arrayfun(@(p) calculateFitness(p, processingRates, energyProfiles), particles); for iteration = 1:maxIterations for p = 1:numParticles particle = particles(p, :); velocities(p, :) = w * velocities(p, :) + c1 * rand(1, numJobs) .* (localBestParticles(p, :) - particle) + c2 * rand(1, numJobs) .* (globalBestParticle - particle); particle = particle + round(velocities(p, :)); particle(particle > numMachines) = numMachines; particle(particle < 1) = 1; fitness = calculateFitness(particle, processingRates, energyProfiles); if fitness < localBestFitnesses(p) localBestParticles(p, :) = particle; localBestFitnesses(p) = fitness; end if fitness < globalBestFitness globalBestParticle = particle; globalBestFitness = fitness; end end end disp('Optimal Schedule:'); disp(globalBestParticle); disp('Energy Consumption:'); disp(globalBestFitness); figure; bar(1:numJobs, energyProfiles(globalBestParticle, :)); xlabel('Jobs'); ylabel('Energy Consumption'); title('Optimal Job Scheduling for Energy Efficiency'); function fitness = calculateFitness(schedule, processingRates, energyProfiles) makespan = max(sum(processingRates(:, schedule))); energyConsumption = sum(energyProfiles(sub2ind(size(energyProfiles), 1:length(schedule), schedule))); fitness = makespan + energyConsumption; end