Evolutionary Algorithm Essay

1215 words - 5 pages

There are a few papers that use multivariate EDAs on
DOPs. In some papers like [28] are use univariate EDAs in
continuous environments and [38] is another paper that uses
EDAs in discrete environments. Besides, variant Particle
Swarm Optimization (PSO) algorithms proposed on the DOPs
provide good results. Therefore, to compare the results of MAMEDA
we use [29] and [62]. PSO-CP algorithm [29] are
utilized a new PSO model, called PSO with composite
particles to address DOPs. In [62] is proposed a MA which
hybridizes PSO with a fuzzy cognition local search technique
on DOPs.
The experiments are divided into four groups. In the first
group, we try to produce different dynamic environments to
evaluate the performance of MA-MEDA. With combination of
the following parameters, different conditions are produced.
Dimensions are set to 2,5,10. Number of peaks and change
severity of environment are in set 1,10,100 and 1.0,2.0,5.0 respectively. The experimental results of MAMEDA
are discussed in second group. Hence, the ability and
weakness of algorithm are investigated. Therefore, we can
evaluate the flexibility and performance rate of MA-MEDA. If
the algorithm proposed is sensitive to some parameters, we
discuss variant methods to improve the performance of it. We
can also discuss the influence of many parameters like
diversity rate, mutation rate, number of peaks and other
parameters on our algorithm. So that, it is decided which the
MA-MEDA is needed to tune parameters. The results of MAMEDA
are compared with PSO-CP algorithm.
The third group includes sets of experiments on the effect of
correlation parameter ? on the performance of MA-MEDA. In
final group, we have comparing with MA is proposed in [62].
C. Comparing MA-MEDA in Dynamic Environments
A set of experiments with different conditions are carried
out to test performance rate of MA-MEDA. The results are
compared with the PSO-CP algorithm [29], in different
environment complexities, dimensions g h2,5,10i, peaks
g h1,10,100i, different environment change
severities c g h1.0,2.0,5.0i and number of evaluation between
two environment change is 5000. The results are shown
in Table I. In this paper all MA-MEDA experiment results
which are compared with PSO-CP, have similar experimental
D. Effects of Different Parameters on MA-MEDA
In this section, the effect of vary parameter on MA-MEDA
are evaluated. The methods can also be used to increase the
ability of MA-MEDA are discussed.
Effect of Diversity Rate on the Algorithm
The One of the most important aims on DOPs is to maintain
the diversity of algorithm in a desirable level in two situations,
primarily when algorithm is running, between two
environment changes, and the other after the change is
detected. We can divide the MA-MEDA into three parts as
follows: EA for exploration of the function landscape,
clustering methods, and the multivariate EDA presented as
LS. If the global search method can cover the...

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