Red Deer Algorithm (RDA)

Jan , 2016
www.iiec2016.com
Mostafa Hajiaghaei-Keshteli
Amir Mohammad Fathollahi Fard
Red Deer Algorithm (RDA)
January 2016
Reza Tavakkoli-Moghaddam

Content
 Meta-heuristics
 Evolutionary Algorithms
 Scottish Red Deer
 Red Deer’s Characteristics
 Red Deer Algorithm (RDA)
 Experiments
 Conclusion
 Questions
2
Content
A. M. Fathollahi Fard Red Deer Algorithm (RDA)

Evolutionary Algorithms
3
Meta-heuristics
Metaheuristics
Population-based
metaheuristics
Local search-based
metaheuristics
Iterated local search (ILS)
Variable neighborhood search (VNS)
Tabu search (TS)
Simulated annealing (SA)

4
Meta-heuristics
Metaheuristics
Population-based
metaheuristics
Local-search-based
metaheuristics
Scatter search (SS)
Genetic algorithm (GA)
Ant colony optimization (ACO)
Particle swarm optimization (PSO)

 Optimization techniques can be divided in two
groups, mathematical programming and Meta-
heuristic algorithms (Gandomi, 2014).
 In general, the existing Meta-heuristic
algorithms may be divided into two main
categories as follows:
1) Evolutionary Algorithms
2) Swarm Algorithms (Yang, 2013).
5
Meta-heuristics

‌‫‌های‬‫م‬‫یت‬‫ر‬‫الگو‬‌‫ی‬‫فراابتکار‬
No. Year Algorithm
1 1975 Holland introduced the Genetic Algorithm (GA).
2 1977 Glover proposed Scatter Search (SS).
3 1980 Smith elucidated genetic programming.
4 1983 Kirkpatrick et al. proposed Simulated Annealing (SA).
5 1986 Glover and McMillan offeredTabuSearch(TS).
6 1986 Farmer et al. suggested the Artificial immune system (AIS).
7 1988 Koza registered his first patent on genetic programming.
8 1989 Moscato presented Memetic Algorithm.
9 1992 Dorigo proposed the Ant Colony Algorithm (ACO).
10 1993 Fonseca and Fleming provided Multi-Objective GA (MOGA).
11 1994 Battiti and Tecchiolli introduced Reactive Search Optimization (RSO).
12 1995 Kennedy and Eberhart proposed Particle Swarm Optimization (PSO).
13 1997 Storn and Price suggested Differential Evolution (DE).
14 1997 Rubinstein presented the Cross Entropy Method (CEM).
15 2001 Geemetal.providedHarmonySearch(HS).
16 2001 Hanseth and Aanestad offered Bootstrap Algorithm (BA).
17 2004 Nakrani and Tovey presented Bees Optimization (BO).
18 2005 Krishnanand and Ghose introduced Glowworm Swarm Optimization(GSO).
19 2005 Karaboga proposed Artificial Bee Colony Algorithm (ABC).
Meta-heuristics
List of Meta-heuristics (1975-2016) as follow:
6

20 2006 Haddad et al. suggested Honey-bee Mating Optimization (HMO).
21 2007 Hamed Shah-Hosseini offered Intelligent Water Drops (IWD).
22 2007 Atashpaz-Gargari and Lucas introduced Imperialist CompetitiveAlgorithm (ICA).
23 2008 Yang presented Firefly Algorithm (FA).
24 2008 Mucherino and Seref suggested Monkey Search (MS).
25 2009 Husseinzadeh- Kashan provided League Championship Algorithm (LCA).
26 2009 Rashedi et al. introduced Gravitational Search Algorithm (GSA).
27 2009 Yang and Deb offered Cuckoo Search (CS).
28 2010 Yang developed Bat Algorithm (BA).
29 2011 Shah-Hosseini introduced the Galaxy-based Search Algorithm (GbSA).
30 2011 Tamura and Yasuda designed Spiral Optimization (SO).
31 2011 Rajabioun developed Cuckoo Optimization Algorithm (COA)
32 2011 Rao et al. presented Teaching-Learning-Based Optimization (TLBO) algorithm.
33 2012 Gandomi and Alavi proposed the Krill Herd (KH) Algorithm.
34 2012 Çivicioglu introduced Differential Search Algorithm (DSA).
35 2012 Hajiaghaei-Keshteli and Aminnayeri proposed the Keshtel Algorithm (KA).
36 2013 Husseinzadeh- Kashan provided Optics Inspired Optimization(OIO).
37 2013 Husseinzadeh- Kashan proposed Grouping Evolution Strategies (GES).
38 2014 Gandomi presented Interior Search Algorithm (ISA).
39 2014 Salimi proposed Stochastic Factal Search (SFS).
40 2015 Zheng developed Water Wave Optimization (WWO).
41 2016 Fathollahi-Fard and Hajiaghaei-Keshteli proposed Red Deer Algorithm (RDA).
Meta-heuristics
7

Iran
Other
Summary
Other 0.66
Iran 0.34
Meta-heuristics
8A. M. Fathollahi Fard Red Deer Algorithm (RDA)

 The Evolutionary Algorithms (EAs) are
generally inspired biological evolution and use
in iterative progress to solve optimization
problems (Husseinzadeh Kashan, 2014).
 In 1975, John Holland has developed the
Genetic Algorithm (GA) to solve huge and
complex problems, for the first time (Holland,
1975).
 The Red Deer Algorithm (RDA) is a one of
Evolutionary Algorithms.
9

Scottish Red Deer
 The Scottish Red Deer (Cervus Elaphus
Scoticus).
 Hinds and male Red Deers.
 A harem is a group of female RDs, which mate
with the head of harem (male commander).
 The competition of male RDs to get the harem
with more hinds via roaring and fighting.
10
Scottish Red Deer

Red Deer’s Characteristics
 We studied the unusual RD’s behavior.
 We can divided main of RD’s behavior in 3
sections as follow:
1) Roaring
2) Mating
3) Fighting
11
Red Deer’s Characteristics

Red Deer’s Roaring
 Roaring male RDs causes to attract hinds.
Females prefer a high to a low roaring rate
(McComb, 1991).
12
Red Deer’s Roaring

Red Deer’s Mating
 Red Deer mating patterns usually involve a
dozen or more mating attempts before the first
successful one.
 There may be several matings before the stag
(the mature male) will seek out another mate in
his harem.
 The commander occupies the territory and
protects the other hinds in his harem.
13
Red Deer’s Mating

Red Deer’s Fighting
 Fighting between male commanders and stags
(or the other males).
 Winner can seize the harem and territory of
loser the match (Clutton-Brock et al. 1979).
14
Red Deer’s Fighting

 A new optimization algorithm based on Red
Deer’s mating is developed.
 The main steps of proposed RDA as follow:
1. Generating initial Red Deers
Select some random points on the
functions and initialize Red Deers. And initial
population of size Npop. We select the best
Red Deers to Nmale and the rest of to Nhind.
15

2. Roar male Red Deers
In this step, the male Red Deers are trying
to increase their grace by roaring. its
counterpart of solution in local searches near
them. In fact, we permit every male Red
Deers to change his position.
16

3. Select γ percent of the best male Red Deers as
male commanders
17

4. Fight between the male commanders and the
stags
We let for each commander males fight
with stags randomly. And select them after
fighting if the objective function is better than
the prior ones.
18

5. Form harems
19

6. Mate male commander of harem with α
percent hinds in his harem.
7. Mate male commander of harem with β
percent hinds in another harem.
20

8. Mate stag with the nearest hind
In this step, for each stags, they are mating with
closest hind. In breeding season, the male Red Deers
prefer to follow the handy hind, this hind may be his
favorite hind among all hinds. This hind maybe in
his harem or habituates in another harem.
21

9. Select the next generation
We select the next generation of male RDs as
the best solutions and for choose hinds for the
next generation with tournament selection or
roulette wheel selection or each evolutionary
mechanism for selection with fitness.
10. Convergence
The stopping condition may be the number of
iteration, the quality of the best solution ever
found, or a time interval.
22

27
Escape
from
Local
Optimum
A. M. Fathollahi Fard

Experiments
28
Experiments

Experiments
29
Experiments

Experiments
30
Experiments
 In all algorithms, the stopping criterion is a maximum
number of iteration and equals to 200. The behavior
of the RDA in finding the best solution is better than
the GA and PSO. The results shown in Table. 2 for
each algorithm.

Conclusion
 The competition among male Red Deers to take
possession of the hinds is the core of this algorithm
and hopefully results in the convergence of Red
Deers to find the global minimum of the problem
 The algorithm is tested by 4 benchmark functions and
the comparison of RDA with standard versions of GA
and PSO showed the superiority of RDA in these
problems.
 Alongside the RDA is very simple to code and use in
different problem types.
31
Conclusion

References
 M. Melanie, An Introduction to Genetic Algorithms, MIT Press, Massachusetts,1999.
 J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications
to Biology, Control, and Artificial Intelligence, University of Michigan Press, Michigan, Ann Arbor, 1975.
 M. Dorigo, V. Maniezzo, A. Colorni, The ant system: optimization by a colony of cooperating agents,
IEEE Trans. Syst. Man Cybern. B 26 (1) (1996) 29–41.
 J. Kennedy, and R. Eberhart,: Particle swarm optimization, Proc. IEEE Int. Conf. Neural Networks.
Perth, Australia, (1995) 1942-1945 .
 K. N. Krishnand, D. Ghose, Detection of multiple source locations using a glow-worms metaphor with
applications to collective robotics, in: Proceeding of the IEEE Swarm Intelligence Symposium, (2005) 84–
91.
 E. Atashpaz-Gargari, C. Lucas, Imperialist competitive algorithm: an algorithm for optimization inspired
by imperialistic competition, in: IEEE Congress on Evolutionary Computation, Singapore (2007) 4661–
4667.
 M. Hajiaghaei-Keshteli, M. Aminnayeri, Keshtel Algorithm (KA); a new optimization algorithm inspired
by Keshtels’ feeding, Proceeding in IEEE Conference on Industrial Engineering and Management Systems
(2013) 2249–2253.
 T. H. Clutton-Brock, S. D. Albon, R. M. Gibson, the logical stag: Adaptive Aspects of fighting in Red
Deer, Animal Behavior 27 (1979) 21 I-225.
 K. E. McComb, Female choice for high roaring rates in Red Deer, Cervus elaphus, Animal Behavior 41
(1991) 79-88.
 C. R. Thouless and F. E. Guiness, Conflict between Red Deers hinds: the winner always wins, Animal
Behavior 34 (1986) 1166-1171.
32
References

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Red Deer Algorithm (RDA)

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