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

number

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

conditions.

D. Effects of Different Parameters on MA-MEDA

performance

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...

Get inspired and start your paper now!