First, the way to obtain the code

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Differential swarm optimization algorithm (DEABC) differential swarm optimization algorithm (DEABC)

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Two, some source code

Clear CLC close all %% objfun=@f1; ub=100; lb=- 100.; %sphere global minimum value is0objfun=@f2; lb=- 600.; ub=600; %griewank global minimum value is0objfun=@f3; lb=- 100.; ub=100; %rastrigin The global minimum value is0objfun=@f4; lb=- 10; ub=10; %rosenbrock The global minimum value is0
%%
d=2;
max_iter=2500;
sizepop=20; % % %1Particle swarm [pso_position, pso_fitness pso_trace] = pso (objfun, d, max_iter sizepop, ub, lb). %2Genetic algorithm [GA_position, GA_FITNESS, GA_trace]= GA (objFun, D,max_iter, sizePOP,ub,lb); %2Differential evolution algorithm [DE_position, DE_FITNESS, DE_trace]= DE1 (objFun, D,max_iter, sizePOP, UB,lb); %3Swarm algorithm [ABc_position, ABC_FITNESS, ABC_trace]= ABC (ObjFun, D,max_iter, sizePOP, UB,lb); % differential bee colony [DEABc_position, DEABC_FITNESS, DEABC_trace]=DEABC(ObjFun, D,max_iter, sizePOP, UB,lb); % %figure 
plot(pso_trace(:,1))
hold on
plot(ga_trace(:,1))
plot(de_trace(:,1))

plot(abc_trace(:,1))
plot(deabc_trace(:,1))

legend('PSO'.'GA'.'DE'.'ABC'.'DEABC')
xlabel('Number of iterations')
ylabel('Fitness value/function value')
%%

figure 
plot(log10(pso_trace(:,1)),'b-')
hold on
plot(log10(ga_trace(:,1)),'r-')
plot(log10(de_trace(:,1)),'g-')

plot(log10(abc_trace(:,1)),'k-')
plot(log10(deabc_trace(:,1)),'m-')

legend('PSO'.'GA'.'DE'.'ABC'.'DEABC')
xlabel('Number of iterations')
ylabel('Fitness value /log10(function value)')
img =gcf; Print (img,'-dpng'.'-r600'.'./img.png') % to get the corresponding format and expected DPI imageCopy the code

3. Operation results

Matlab version and references

1 matlab version 2014A

[1] Yang Baoyang, YU Jizhou, Yang Shan. Intelligent Optimization Algorithm and Its MATLAB Example (2nd Edition) [M]. Publishing House of Electronics Industry, 2016. [2] ZHANG Yan, WU Shuigen. MATLAB Optimization Algorithm source code [M]. Tsinghua University Press, 2017.