Example 20.4 Optimization with Linear Constraints Using Repair Strategy

This problem seeks a minimum within a convex domain specified by a convex hull, a set of points such that all points in the search space are normalized linear combinations of those points. Each solution is represented by a set of weights such that there is one for each point in the convex hull, $0 \leq w_ i \leq 1$ , and $\Sigma w_ i=1$ . In this example the feasible region is the convex hull defined by the set of points (-3 -2), (3 -2), (-3 2), and (3 2). The objective function is a six-hump camel-back function (see Michalewicz (1996), Appendix B), with a known global minimum value of -1.0316 at two different points, (-0.0898,0.7126) and (0.0898,-0.7126). A user mutation module is specified, and the simple crossover operator is used. Both the mutation operator and the crossover operator will produce solutions that violate the constraints, so in the objective function each solution will be checked and renormalized to bring it back within the convex hull.

proc iml;

/* Test case using user modules for the mutation operator and
 * for initialization
 */

start sixhump(w) global(cvxhull);
/* Function has global minimum value of -1.0316
 * at x = {-0.0898  0.7126} and 
 *    x = { 0.0898 -0.7126}
 */
  sum = w[1,+];

  /* guard against the remote possibility of all-0 weights */
  if sum=0 then do;
     nc = ncol(w);
     w = j(1, nc, 1/nc );
         sum = 1;
  end;

  /* re-normalize weights */
  w = w/sum;

  /* convert to x-coordinate form */
  x = (w * cvxhull)[+,];
  x1 = x[1];
  x2 = x[2];
  
  /* compute objective value */
  r = (4 - 2.1*x1##2 + x1##4/3)*x1##2 + x1*x2 +
       (-4 + 4*x2*x2)*x2##2;
  return(r);
finish;

/* each row is one point on the boundary of
 * the convex hull */
cvxhull = {-3 -2, 
            3 -2, 
            -3 2,
            3 2};

/* initialization module */
start cvxinit( w ) global(cvxhull);
sum = 0;
a = j(1, nrow(cvxhull), 1234);
do while(sum = 0);
  r = uniform(a);
  sum = r[1,+];
end;
w = r / sum;
finish;

/* mutation module */
start cvxmut(w)global(cvxhull);
  row = int(uniform(1234) * nrow(cvxhull)) + 1;
  r = uniform(1234);
  w[1,row] = r;
finish;

id = gasetup(1, /* real fixed-length vector encoding */
             nrow(cvxhull), /* vector size = number of points
                             * specifying convex hull
                             */
             1234);
call gasetobj(id,
               0, /* minimize a user-specified objective function */
              "sixhump"
              );
call gasetsel( id,
                5, /* carry over the best 5 from each generation */
                1, /* dual tournament */
              0.95  /* best-player-wins probability */
             );
call gasetcro(id,
              0.8, /* crossover probability */
              1    /* simple crossover operator */
              );
call gasetmut(id,0.05,0,"cvxmut");


call gainit( id,
            100,         /* population size */
               ,         /* not using constant bounds */
       "cvxinit"         /* initialization module */
           );

niter = 35; /* number of iterations */
summary = j(niter,2);
mattrib summary [c = {"bestValue", "avgValue"}];
call gagetval(value, id);
summary[1,1] = value[1];
summary[1,2] = value[:];

do i = 1 to niter;
  call garegen(id);
  call gagetval(value, id); 
  summary[i,1] = value[1];
  summary[i,2] = value[:];
end;
call gagetmem(mem, value, id, 1);
bestX = (mem * cvxhull)[+,];
print  "best X " bestX[l = ""],
       "best value " value[l = ""];
iteration = t(1:niter);
print iteration summary;
call gaend(id);

The output results are

                 best X   0.089842 -0.712658
                     best value  -1.031628

                          SUMMARY
              ITERATION bestValue avgValue

                      1 -0.082301 0.9235856
                      2 -0.948434 0.1262678
                      3 -0.956136 0.2745601
                      4 -1.017636 0.1367912
                      5 -1.028457 -0.241069
                      6 -1.028457 -0.353218
                      7 -1.028457  -0.56789
                      8 -1.028457  -0.73044
                      9 -1.028457 -0.854496
                     10 -1.028509 -0.941693
                     11 -1.031334 -0.936541
                     12 -1.031334  -0.90363
                     13 -1.031373 -0.774917
                     14 -1.031614 -0.873418
                     15 -1.031614 -0.886818
                     16 -1.031618  -0.95678
                     17 -1.031619 -0.933061
                     18 -1.031626 -0.885132
                     19 -1.031628 -0.936944
                     20 -1.031628 -0.906637
                     21 -1.031628 -0.925809
                     22 -1.031628 -0.860156
                     23 -1.031628 -0.946146
                     24 -1.031628 -0.817196
                     25 -1.031628 -0.883284
                     26 -1.031628 -0.904361
                     27 -1.031628 -0.974893
                     28 -1.031628 -0.975647
                     29 -1.031628 -0.872004
                     30 -1.031628 -1.031628
                     31 -1.031628 -0.897558
                     32 -1.031628 -0.922121
                     33 -1.031628 -0.855045
                     34 -1.031628 -0.922061
                     35 -1.031628 -0.958257

Any problem with linear constraints could be formulated in this way, by determining the convex hull that corresponds to the constraints. The genetic operators and the repair strategy are straightforward to apply, and as this case shows, can give reasonable convergence to a global optimum.