## Global Optimization Toolbox |

This example shows how to create and manage options for the multiobjective genetic algorithm function `gamultiobj`

using `gaoptimset`

in Global Optimization Toolbox.

`gamultiobj`

`gamultiobj`

finds a local Pareto front for multiple objective functions using the genetic algorithm. For this example, we will use `gamultiobj`

to obtain a Pareto front for two objective functions described in the MATLAB file `kur_multiobjective.m`

. It is a real-valued function that consists of two objectives, each of three decision variables. We also impose bound constraints on the decision variables -5 <= x(i) <= 5, i = 1,2,3.

```
type kur_multiobjective.m
```

function y = kur_multiobjective(x) %KUR_MULTIOBJECTIVE Objective function for a multiobjective problem. % The Pareto-optimal set for this two-objective problem is nonconvex as % well as disconnected. The function KUR_MULTIOBJECTIVE computes two % objectives and returns a vector y of size 2-by-1. % % Reference: Kalyanmoy Deb, "Multi-Objective Optimization using % Evolutionary Algorithms", John Wiley & Sons ISBN 047187339 % Copyright 2007 The MathWorks, Inc. % Initialize for two objectives y = zeros(2,1); % Compute first objective for i = 1:2 y(1) = y(1) - 10*exp(-0.2*sqrt(x(i)^2 + x(i+1)^2)); end % Compute second objective for i = 1:3 y(2) = y(2) + abs(x(i))^0.8 + 5*sin(x(i)^3); end

We need to provide a fitness function, the number of variables, and bound constraints in the problem to `gamultiobj`

function. Refer to the help for the `gamultiobj`

function for the syntax. Here we also want to plot the Pareto front in every generation using the plot function @GAPLOTPARETO. We use `gaoptimset`

function to specify this plot function.

FitnessFunction = @kur_multiobjective; % Function handle to the fitness function numberOfVariables = 3; % Number of decision variables lb = [-5 -5 -5]; % Lower bound ub = [5 5 5]; % Upper bound A = []; % No linear inequality constraints b = []; % No linear inequality constraints Aeq = []; % No linear equality constraints beq = []; % No linear equality constraints options = gaoptimset('PlotFcns',@gaplotpareto);

Run the `gamultiobj`

solver and display the number of solutions found on the Pareto front and the number of generations.

[x,Fval,exitFlag,Output] = gamultiobj(FitnessFunction,numberOfVariables,A, ... b,Aeq,beq,lb,ub,options); fprintf('The number of points on the Pareto front was: %d\n', size(x,1)); fprintf('The number of generations was : %d\n', Output.generations);

Optimization terminated: average change in the spread of Pareto solutions less than options.TolFun. The number of points on the Pareto front was: 18 The number of generations was : 238

The Pareto plot displays two competing objectives. For this problem, the Pareto front is known to be disconnected. The solution from `gamultiobj`

can capture the Pareto front even if it is disconnected. Note that when you run this example, your result may be different from the results shown because `gamultiobj`

uses random number generators.

The multiobjective genetic algorithm (`gamultiobj`

) works on a population using a set of operators that are applied to the population. A population is a set of points in the design space. The initial population is generated randomly by default. The next generation of the population is computed using the non-dominated rank and a distance measure of the individuals in the current generation.

A non-dominated rank is assigned to each individual using the relative fitness. Individual 'p' dominates 'q' ('p' has a lower rank than 'q') if 'p' is strictly better than 'q' in at least one objective and 'p' is no worse than 'q' in all objectives. This is same as saying 'q' is dominated by 'p' or 'p' is non-inferior to 'q'. Two individuals 'p' and 'q' are considered to have equal ranks if neither dominates the other. The distance measure of an individual is used to compare individuals with equal rank. It is a measure of how far an individual is from the other individuals with the same rank.

The multiobjective GA function `gamultiobj`

uses a controlled elitist genetic algorithm (a variant of NSGA-II [1]). An elitist GA always favors individuals with better fitness value (rank) whereas, a controlled elitist GA also favors individuals that can help increase the diversity of the population even if they have a lower fitness value. It is very important to maintain the diversity of population for convergence to an optimal Pareto front. This is done by controlling the elite members of the population as the algorithm progresses. Two options 'ParetoFraction' and 'DistanceFcn' are used to control the elitism. The Pareto fraction option limits the number of individuals on the Pareto front (elite members) and the distance function helps to maintain diversity on a front by favoring individuals that are relatively far away on the front.

We can chose the default distance measure function, @distancecrowding, that is provided in the toolbox or write our own function to calculate the distance measure of an individual. The crowding distance measure function in the toolbox takes an optional argument to calculate distance either in function space (phenotype) or design space (genotype). If 'genotype' is chosen, then the diversity on a Pareto front is based on the design space. The default choice is 'phenotype' and, in that case, the diversity is based on the function space. Here we choose 'genotype' for our distance function. We will pass our options structure 'options', created above, to `gaoptimset`

to modify the value of the parameter 'DistanceMeasureFcn'.

options = gaoptimset(options,'DistanceMeasureFcn',{@distancecrowding,'genotype'});

The Pareto fraction has a default value of 0.35 i.e., the solver will try to limit the number of individuals in the current population that are on the Pareto front to 35 percent of the population size. Here we set the Pareto fraction to 0.5.

```
options = gaoptimset(options,'ParetoFraction',0.5);
```

Run the `gamultiobj`

solver and display the number of solutions found on the Pareto front and the average distance measure of solutions.

[x,Fval,exitFlag,Output] = gamultiobj(FitnessFunction,numberOfVariables,A, ... b,Aeq,beq,lb,ub,options); fprintf('The number of points on the Pareto front was: %d\n', size(x,1)); fprintf('The average distance measure of the solutions on the Pareto front was: %g\n', Output.averagedistance); fprintf('The spread measure of the Pareto front was: %g\n', Output.spread);

Optimization terminated: average change in the spread of Pareto solutions less than options.TolFun. The number of points on the Pareto front was: 25 The average distance measure of the solutions on the Pareto front was: 0.038025 The spread measure of the Pareto front was: 0.165495

A smaller average distance measure indicates that the solutions on the Pareto front are evenly distributed. However, if the Pareto front is disconnected, then the distance measure will not indicate the true spread of solutions.

`gamultiobj`

uses three different criteria to determine when to stop the solver. The solver stops when any one of the stopping criteria is met. It stops when the maximum number of generations is reached; by default this number is '200*numberOfVariables'. `gamultiobj`

also stops if the average change in the spread of the Pareto front over the 'StallGenLimit' generations (default is 100) is less than tolerance specified in options.TolFun. The third criterion is the maximum time limit in seconds (default is Inf). Here we modify the stopping criteria to change the function tolerance (TolFun) from 1e-4 to 1e-3 and increase the stall generations (StallGenLimit) to 150.

options = gaoptimset(options,'TolFun',1e-3,'StallGenLimit',150);

Run the `gamultiobj`

solver and display the number of solutions found on the Pareto front and the number of generations.

[x,Fval,exitFlag,Output] = gamultiobj(FitnessFunction,numberOfVariables,A, ... b,Aeq,beq,lb,ub,options); fprintf('The number of points on the Pareto front was: %d\n', size(x,1)); fprintf('The number of generations was : %d\n', Output.generations);

Optimization terminated: average change in the spread of Pareto solutions less than options.TolFun. The number of points on the Pareto front was: 25 The number of generations was : 152

We will use a hybrid scheme to find an optimal Pareto front for our multiobjective problem. `gamultiobj`

can reach the region near an optimal Pareto front relatively quickly, but it can take many function evaluations to achieve convergence. A commonly used technique is to run `gamultiobj`

for a small number of generations to get near an optimum front. Then the solution from `gamultiobj`

is used as an initial point for another optimization solver that is faster and more efficient for a local search. We use `fgoalattain`

as the hybrid solver with `gamultiobj`

. `fgoalattain`

solves the goal attainment problem, which is one formulation for minimizing a multiobjective optimization problem.

The hybrid functionality in multiobjective function `gamultiobj`

is slightly different from that of the single objective function GA. In single objective GA the hybrid function starts at the best point returned by GA. However, in `gamultiobj`

the hybrid solver will start at all the points on the Pareto front returned by `gamultiobj`

. The new individuals returned by the hybrid solver are combined with the existing population and a new Pareto front is obtained. It may be useful to see the syntax of `fgoalattain`

function to better understand how the output from `gamultiobj`

is internally converted to the input of `fgoalattain`

function. `gamultiobj`

estimates the pseudo weights (required input for `fgoalattain`

) for each point on the Pareto front and runs the hybrid solver starting from each point on the Pareto front. Another required input, goal, is a vector specifying the goal for each objective. `gamultiobj`

provides this input as the extreme points from the Pareto front found so far.

Here we run `gamultiobj`

without the hybrid function.

[x,Fval,exitFlag,Output] = gamultiobj(FitnessFunction,numberOfVariables,A, ... b,Aeq,beq,lb,ub,options); fprintf('The number of points on the Pareto front was: %d\n', size(x,1)); fprintf('The average distance measure of the solutions on the Pareto front was: %g\n', Output.averagedistance); fprintf('The spread measure of the Pareto front was: %g\n', Output.spread);

Optimization terminated: average change in the spread of Pareto solutions less than options.TolFun. The number of points on the Pareto front was: 25 The average distance measure of the solutions on the Pareto front was: 0.0392839 The spread measure of the Pareto front was: 0.170654

Here we use @fgoalattain as the hybrid function. We also reset the random number generators so that we can compare the results with the previous run (without the hybrid function).

options = gaoptimset(options,'HybridFcn',@fgoalattain); % Reset the random state (only to compare with previous run) RandStream.getGlobalStream.State = Output.rngstate.state; % Run the GAMULTIOBJ solver with hybrid function. [x,Fval,exitFlag,Output,Population,Score] = gamultiobj(FitnessFunction,numberOfVariables,A, ... b,Aeq,beq,lb,ub,options); fprintf('The number of points on the Pareto front was: %d\n', size(x,1)); fprintf('The average distance measure of the solutions on the Pareto front was: %g\n', Output.averagedistance); fprintf('The spread measure of the Pareto front was: %g\n', Output.spread);

Optimization terminated: average change in the spread of Pareto solutions less than options.TolFun. The number of points on the Pareto front was: 25 The average distance measure of the solutions on the Pareto front was: 0.106791 The spread measure of the Pareto front was: 0.369945

If the Pareto fronts obtained by `gamultiobj`

alone and by using the hybrid function are close, we can compare them using the spread and the average distance measures. The average distance of the solutions on the Pareto front can be improved by using a hybrid function. The spread is a measure of the change in two fronts and that can be higher when hybrid function is used. This indicates that the front has changed considerably from that obtained by `gamultiobj`

with no hybrid function.

It is certain that using the hybrid function will result in a optimal Pareto front but we may lose the diversity of the solution (because `fgoalattain`

does not try to preserve the diversity). This can be indicated by a higher value of the average distance measure and the spread of the front. We can further improve the average distance measure of the solutions and the spread of the Pareto front by running `gamultiobj`

again with the final population returned in the last run. Here, we should remove the hybrid function.

options = gaoptimset(options,'HybridFcn',[]); % No hybrid function % Provide initial population and scores options = gaoptimset(options,'InitialPopulation',Population,'InitialScore',Score); % Run the GAMULTIOBJ solver with hybrid function. [x,Fval,exitFlag,Output,Population,Score] = gamultiobj(FitnessFunction,numberOfVariables,A, ... b,Aeq,beq,lb,ub,options); fprintf('The number of points on the Pareto front was: %d\n', size(x,1)); fprintf('The average distance measure of the solutions on the Pareto front was: %g\n', Output.averagedistance); fprintf('The spread measure of the Pareto front was: %g\n', Output.spread);

Optimization terminated: average change in the spread of Pareto solutions less than options.TolFun. The number of points on the Pareto front was: 25 The average distance measure of the solutions on the Pareto front was: 0.0339164 The spread measure of the Pareto front was: 0.136365

[1] Kalyanmoy Deb, "Multi-Objective Optimization using Evolutionary Algorithms", John Wiley & Sons ISBN 047187339.