matlab-(含教程)基于惯性加权粒子群优化算法PSO函数最小值matlab仿真

clc; clear; close all; warning off; addpath(genpath(pwd)); %constants while initializing alpha = 1; dT = 1; xMin = -5; xMax = 5; %constants while updating particle velocity c1 = 2; c2 = 2; w = 1.4; beta = 0.95; wMin = 0.35; %constants while restricitng velocity vMax = (xMax-xMin)/dT; %creating vector for best global value and position bestGlobalPosition = zeros(1,2); bestGlobalValue = inf; numberOfParticles = 50; numberOfIterations = 10000; %creating vector for best local value and position bestLocalPosition = zeros(numberOfParticles,2); bestLocalValue (1:numberOfParticles,1) = inf; %Step1 : Initializing velocity and position [positionParticle,velocityParticle] = initializing(xMin,xMax,numberOfParticles,alpha,dT); iter=1; while iter<numberOfIterations functionValue = zeros(numberOfParticles,1); for i=1:numberOfParticles functionValue(i) = functionEvaluate(positionParticle(i,1),positionParticle(i,2)); if functionValue(i)<bestLocalValue(i) bestLocalValue(i) = functionValue(i); bestLocalPosition(i,:) = positionParticle(i,:); end if functionValue(i)<bestGlobalValue bestGlobalValue = functionValue(i); bestGlobalPosition = positionParticle(i,:); end end %Step 4: Updating particle velocity velocityParticle = updateVelocities(w,velocityParticle,c1,bestGlobalPosition,dT,positionParticle,c2,bestLocalPosition); %Step 5: Restricting Velocity velocityParticle = restrictVelocity(positionParticle,velocityParticle,vMax); %Step 6: Updating Position positionParticle = updatePositions(positionParticle,velocityParticle,dT); %Step 7: Reducing influence of w if w>wMin w = w*beta; end iter = iter+1; end plotContour()