gamain.m

遗传算法实例

function x = gamain(bus,index,nornalized_reald,relationlist_final)

%规划周期内的时段数目(13周)
Nperiod=13*3+1;  %(2,2,3)
%系统内节点总数
Nnode=length(bus(:,1));

%调整前系统内各机组检修时段序列;(1-35)
% Tpbefore=ones(1,Nnode);  
% Tpbefore(1:30)=[1 0 0 21 0 4 0 0 0 23 0 19 0 0 0 0 0 24 0 0 0 0 0 25 13 0 0 0 0 0]';
% Tpbefore(31:60)=[0 19 0 25 0 0 0 0 0 0 0 10 0 0 0 27 0 0 22 0 0 0 0 0 25 0 0 0 0 0]';
% Tpbefore(61:90)=[0 29 0 0 10 28 0 0 0 31 0 7 32 0 0 4 16 0 0 0 0 0 0 0 13 0 32 0 16 33]';   
% Tpbefore(91:118)=[19 33 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 35 0 10 0 0 0 25 0 0 ]'; 
% 
% best_rptime(1,1:32)=[1 21 4 23 19 24 25 13 19 25 10 27 22 25 29 10 28 31 7  32 4 16 13 32 16 33 19 33 1 35 10 25];
% best_rptime(2,1:32)=[2 22 5 24 20 25 26 14 20 26 11 28 23 26 30 11 29 32 8  33 5 17 14 33 17 34 20 34 2 36 11 26];
% best_rptime(3,1:32)=[3 23 6 25 21 26 27 15 21 27 12 29 24 27 31 12 30 33 9  34 6 18 15 34 18 35 21 35 3 37 12 27];
% best_rptime(4,1:32)=[4 24 7 26 22 27 28 16 22 28 13 30 25 28 32 13 31 34 10 35 7 19 16 35 19 36 22 36 4 38 13 28];
% best_rptime(5,1:32)=[5 25 8 27 23 28 29 17 23 29 14 31 26 29 33 14 32 35 11 36 8 20 17 36 20 37 23 37 5 39 14 29];
% best_rptime(6,1:32)=[6 26 9 28 24 29 30 18 24 30 15 32 27 30 34 15 33 36 12 37 9 21 18 37 21 38 24 38 6 40 15 30];

Tpbefore=ones(1,Nnode);  
Tpbefore(1:30)=[1 0 0 21 0 4 0 0 0 23 0 19 0 0 0 0 0 24 0 0 0 0 0 25 13 0 0 0 0 0]';
Tpbefore(31:60)=[0 19 0 25 0 0 0 0 0 0 0 10 0 0 0 27 0 0 22 0 0 0 0 0 25 0 0 0 0 0]';
Tpbefore(61:90)=[0 2 0 0 10 3 0 0 0 10 0 7 1 0 0 4 16 0 0 0 0 0 0 0 13 0 32 0 16 33]';   
Tpbefore(91:118)=[19 12 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 35 0 10 0 0 0 25 0 0 ]'; 

best_rptime(1,1:32)=[1 21 4 23 19 24 25 13 19 25 10 27 22 25 2 10 3 10 7  1 4 16 13 32 16 33 19 12 1 35 10 25];
best_rptime(2,1:32)=[2 22 5 24 20 25 26 14 20 26 11 28 23 26 3 11 4 11 8  2 5 17 14 33 17 34 20 13 2 36 11 26];
best_rptime(3,1:32)=[3 23 6 25 21 26 27 15 21 27 12 29 24 27 4 12 5 12 9  3 6 18 15 34 18 35 21 14 3 37 12 27];
best_rptime(4,1:32)=[4 24 7 26 22 27 28 16 22 28 13 30 25 28 5 13 6 13 10 4 7 19 16 35 19 36 22 15 4 38 13 28];
best_rptime(5,1:32)=[5 25 8 27 23 28 29 17 23 29 14 31 26 29 6 14 7 14 11 5 8 20 17 36 20 37 23 16 5 39 14 29];
best_rptime(6,1:32)=[6 26 9 28 24 29 30 18 24 30 15 32 27 30 7 15 8 15 12 6 9 21 18 37 21 38 24 17 6 40 15 30];

xFns = 'simpleXover';
xOpts = [.4];
% Mutation Operators
mFns = 'binaryMutation';
mOpts = [0.005];
% Termination Operators
termFns = 'maxGenTerm';
termOps = [100]; % 200 Generations
% Selection Function
selectFn = 'roulette'
selectOps = [];
% Evaluation Function
evalFn = 'garepairEval';
evalOps = [];
% Bounds on the variables
bounds = [0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7;0 7; 0 7; 0 7; 0 7; 0 7; 0 7; 0 7;];
gaOpts=[1 0 1];

% Generate an intialize population of size 20
startPop = initializega(80,bounds,'garepairEval',bus,[1 0],nornalized_reald,relationlist_final,index,Tpbefore,best_rptime)
[x endPop bestPop trace]=ga(bounds,evalFn,bus,startPop,gaOpts,...
    termFns,termOps,selectFn,selectOps,nornalized_reald,relationlist_final,index,Tpbefore,best_rptime,xFns,xOpts,mFns,mOpts);

termOps=[200];
[x endPop bestPop trace]=ga(bounds,evalFn,bus,[],gaOpts,termFns,termOps,...
    selectFn,selectOps,nornalized_reald,relationlist_final,index,Tpbefore,best_rptime);