Global Optimization Toolbox User's Guide - MathWorks

Vectorized is 'off' — Vectorized determines whether ga evaluates
an entire population with one function call in a vectorized fashion. If
Vectorized is 'on', ga does not distribute the evaluation of the function,
so does not use parfor.
ga can have a hybrid function that runs after it finishes; see “Using a Hybrid
Function” on page 5-104. If you want the hybrid function to take advantage
of parallel computation, set its options separately so that UseParallel is
'always'. If the hybrid function is patternsearch, setCompletePoll to 'on'
so that patternsearch runs in parallel.
If the hybrid function is fmincon, set the following options with optimset to
have parallel gradient estimation:
GradObj must not be 'on' — it can be 'off' or [].
Or, if there is a nonlinear constraint function, GradConstr must not be
'on' — it can be 'off' or [].
To find out how to write options for the hybrid function, see “Parallel Hybrid
Functions” on page 8-16.
Parallel Computing with gamultiobj
Parallel computing with gamultiobj works almost the same as with ga. For
detailed information, see “Genetic Algorithm” on page 8-8.
The difference between parallel computing with gamultiobj and ga has to
do with the hybrid function. gamultiobj allows only one hybrid function,
fgoalattain. This function optionally runs after gamultiobj finishes its run.
Each individual in the calculated Pareto frontier, that is, the final population
found by gamultiobj, becomes the starting point for an optimization using
fgoalattain. These optimizations run in parallel. The number of processors
performing these optimizations is the smaller of the number of individuals
and the size of your matlabpool.
For fgoalattain toruninparallel,setits options correctly:
fgoalopts = optimset('UseParallel','always')
gaoptions = gaoptimset('HybridFcn',{@fgoalattain,fgoalopts});
Background
8-9