Computational Intelligence - November 2017 - 80

of INDIVIDUAL objects) from the
GLOBAL object by invoking its method Initialization(). After that, the algor ithm starts the evolution until the
termination criterion is fulfilled, where
the maximum number of fitness evaluations is used as the termination criterion
for all the MOEAs in PlatEMO. In each
generation of a general MOEA, it first
performs mating pool selection for
selecting a number of parents from the
current population, and the parents are
used to generate offsprings by invoking
the method Variation() of GLOBAL
object. Variation() then passes the parents
to the operator function (e.g., DE.m),
which is used to calculate the decision
variables of the offsprings according to

variables of the individual, which is
assigned when the object is being
instantiated. obj and con store the objective values and the constraint values of
the individual, respectively, which are
calculated after dec has been assigned.
The property add is used to store additional properties of the individual for
some special operators, such as the
'speed' property in PSO operator [101].
In order to better understand the
mechanism of PlatEMO, Fig. 4 illustrates the sequence diagram of running
an MOEA by PlatEMO without GUI.
To begin with, the interface main.m first
invokes the algorithm function (e.g.,
NSGAII.m), then the algorithm obtains an initial population (i.e., an array

main.m

Algorithm
Invoke

GLOBAL.m

Operator

the parents. Next, the operator function
invokes the INDIVIDUAL class to instantiate the offspring objects, where the
objective values of offsprings are calculated by invoking the problem function
(e.g., DTLZ1.m). After obtaining the
offsprings, the algorithm performs environmental selection on the current population and the offsprings to select the
population for next generation. When
the number of instantiated INDIVIDUAL objects exceeds the maximum number of fitness evaluations, the algorithm
will be terminated and the final population will be output.
As presented by the above procedure,
the algorithm function, the problem
function and the operator function do

INDIVIDUAL.m

Problem

Initialization
Initial
Population
When Termination Criterion Not Fulfilled
Mating Pool
Selection

Parents
Parents

Generate
Variables of
Offsprings

Variables of
Offsprings

Offsprings

Offsprings

Variables of
Offsprings

Objective
Values of
Offsprings

Offsprings
Environmental
Selection

Final
Population
FIGURE 4 Sequence diagram of running a general multi-objective optimization algorithm by PlatEMO without GUI.

80

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2017

Calculate
Objective
Values of
Offsprings



Table of Contents for the Digital Edition of Computational Intelligence - November 2017

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