Nowadays, the optimization of parameters according to a set of activities performed in order to reduce time, effort and resources is gaining more attention. That is why the use of optimization algorithms has been made necessary in everyday life. This paper presents a comparison study between micro-algorithms: Genetic Algorithm (GA), Artificial Immune System (AIS), Estimation Distribution Algorithm (EDA), Particle Swarm Optimization(PSO), Bee Algorithm (BA) and Bee SwarmOptimization (BSO). To perform an activity in a confined space, this is affected by the light from the outside, which can be blocked by shutters and doors (of crystal), and lighting of lamps obtained within this space. It is then necessary to obtain the best combination using any optimization algorithm on the condition that it should be each of these objects to have a lighting comfort.

Topics
Evolutionary computation, Optimization algorithms, Immune system
1.
W.
 
Romero
,
V.
 
Zamudio
,
R.
 
Baltazar
,
M.
 
Sotelo
,
J.
 
Soria
. “
Comparative Study of BSO and GA for the Optimizing Energy in Ambiente Intelligence
,”
Advances in Soft Computing
,
MICAI 2011, Part II, LNAI 7095
,
177
–
188
,
2011
.
Google Scholar
 
2.
L. A.
 
Romero
,
V.
 
Zamudio
,
M.
 
Sotelo
,
R.
 
Baltazar
, and
E.
 
Mezura
, “
A Comparison between Meta-heuristics as Strategies for Minimizing Cyclic Instability in Ambient Intelligence
,”
Sensors 2012
,
2012
.
Google Scholar
 
3.
Herrera-Lozada
 
J.C.
,
Calvo
 
H.
,
Taud
 
H.
, “
A Micro artificial immune system
”.
Polibits
.
43
,
107
–
111
(
2011
).
Google Scholar
Crossref
Search ADS
 
4.
Y. S.
 
Kim
,
A. S.
 
Choi
, and
J. W.
 
Jeong
, “
Applying microgenetic algorithm to numerical model for luminous intensity distribution of planar prism LED luminaire
,”
Opt. Commun.
 
293
,
22
–
30
(
2013
).
https://doi.org/10.1016/j.optcom.2012.11.017
Google Scholar
Crossref
Search ADS
 
5.
Lourdes
Araujo
,
Cervigón
Carlos
.
Evolutionary Algorithms, APractical Approach
. FirstEdition.
Mexico
,
SAAlfaomegaGrupo Editorde C.V.
2009
.
Google Scholar
 
6.
Leandro N.
 
de Castro
 y
Jonathan
 
Timmis
.
2002
.
Artificial Immune System: A New Computational Intelligence Approach
. First Edition.
United Kingdom
:
Springer Editorial
.
Google Scholar
 
7.
P.
Larrañagaand
JA
Lozano
, “
Estimation of DistributionAlgorithms
,”
2003
[cited10.4.2014]. Available inhttp://www.redheur.org/sites/default/files/metodos/EDA01.pdf.
8.
J.
 
Kennedy
 and
R.
 
Eberhart
, “
Particle swarm optimization
.”
Proc. IEEE International Conf. on Neural Networks
(
Perth, Australia
),
IEEE Service Center
,
Piscataway, NJ
,
1995
(in press).
Google Scholar
Crossref
Search ADS
 
9.
D.T.
 
Pham
,
A.
 
Ghanbarzadeh
,
E.
 
Koc
,
S.
 
Otri
,
S.
 
Rahim
,
M.
 
Zaidi
. “
The Bee Algorithm – A Novel Tool for Complex Optimisation Problems
,” In
Intelligence Production Machines and Systems 2
,
2006
.
Google Scholar
 
10.
Sotelo-Figueroa
,
M.A.
;
del Rosario Baltazar-Flores
,
M.
;
Carpio
,
J.M.
;
Zamudio
,
V.
, “
A Comparation between Bee Swarm Optimization and Greedy Algorithm for the Knapsack Problem with Bee Reallocation
,”
Articial Intelligence (MICAI)
,
2010 Ninth Mexican International Conference
on , vol., no., pp.
22
,
27
,
8-13 Nov
.
2010
.
Google Scholar
Crossref
Search ADS
 
11.
Vicente
 
Tam
,
King-Yip
 
Cheng
, and
King-Shan
 
Lui
. Using Micro-Genetic Algorithms to Improve Localization in Wireless Sensor Networks.
Journal of Communications
,
Academy Publisher
.
2006
. Vol.
1
, No.
4
.
12.
Derrac
 
Joaquín
,
García
 
Salvador
,
Molina
 
Daniel
 y
Herrera
 
Francisco
. “A Practical Tutorial on the Use of Nonparametric Statistical Test as a Methodology for Comparing Evolutionary and Swarm Intelligence Algorithms,”
Swarm and Evolutionary Computation
.
ELSEVIER
,
2011
.
Google Scholar
 
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