Modified position update in spider monkey optimization algorithm

International Association of Scientific Innovation and Research (IASIR)
(An Association Unifying the Sciences, Engineering, and Applied Research)
International Journal of Emerging Technologies in Computational
and Applied Sciences (IJETCAS)
www.iasir.net
IJETCAS 14-141 © 2014, IJETCAS All Rights Reserved Page 198
ISSN (Print): 2279-0047
ISSN (Online): 2279-0055
Modified Position Update in Spider Monkey Optimization Algorithm
1
Sandeep Kumar, 2
Vivek Kumar Sharma, 3
Rajani Kumari
Faculty of Engineering and Technology
Jagannath University, Chaksu, Jaipur-303901
INDIA
Abstract: Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO
is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission
fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper
proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a
position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy
is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed
algorithm tested over benchmark problems and results show that it gives better results for considered unbiased
problems.
Keywords: Spider Monkey Optimization, Swarm Intelligence, Population based Metaheuristics, Modified
Position Update in Spider Monkey Optimization
I. Introduction
Nature inspired meta-heuristics has turn out to be an intimidating and out of the ordinary field of research in the
midst of researchers who are trying to solve complex optimization problems. More or less all meta-heuristics
make use of both randomization and local search. Due to randomization it can be in motion away from local
search to global search space. That’s why meta-heuristics are unsurpassed more suitable for global optimization
problems. Meta heuristic algorithms have two major components: diversification and intensification.
Diversification is the process of exploration of the large search space and ensures that solution does not ensnare
in local optima at the same time as intensification concentrates on best solution for convergence to optimality
[1]. Population based meta-heuristics do not give assurance for the optimal solution but they provide near-
optimal solution for most difficult optimization problems. Researchers have evaluated this type of behaviors and
developed strategies with the intention of can be used to solve nonlinear and discrete optimization problems.
Preceding research [2, 3, 4, and 5] in last decade has shown that strategies based on swarm intelligence have
enormous prospective to come across solutions of real world optimization problems. The algorithms that have
emerged in topical years consist of ant colony optimization (ACO) [2], particle swarm optimization (PSO) [3],
bacterial foraging optimization (BFO) [6], Artificial bee colony (ABC) optimization algorithm established by D.
Karaboga [7] and most recently developed Spider Monkey Optimization (SMO) algorithm [10]is new entry in
class of swarm intelligence. This SMO algorithm is inspired by fission fusion social structure (FFSS) based
foraging behavior of spider monkeys when searching for quality food source and for mating. Similar to any
other population based optimization techniques, ABC consists of a population of inherent solutions. The
inherent solutions are food sources of honey bees. The fitness is decided in terms of the quality of the food
source that is nectar amount. ABC is relatively a straightforward, speedy and population based stochastic search
technique in the field of nature inspired algorithms. SMO is also similar to ABC in nature.
There are two fundamental processes which drive the swarm to update in ABC: the deviation process, which
enables exploring different fields of the search space, and the selection process, which ensures the exploitation of
the previous experience. However, it has been shown that the ABC may occasionally stop moving toward the
global optimum even though the population has not encounter to a local optimum [8]. It can be observed that the
solution search equation of ABC algorithm is good at exploration but poor at exploitation [9]. Therefore, to
maintain the proper balance between exploration and exploitation behavior of ABC, it is highly expected to
develop a local search approach in the basic ABC to intensify the search region.
II. Spider Monkey Optimozation (SMO) Algorithm
Social activities of spider monkeys encouraged JC Bansal et al. [10] to develop a stochastic optimization modus
operandi that impersonate fission-fusion social structure (FFSS) based intelligent foraging behavior of spider
monkeys. JC Bansal et al. [10] identified following four key features of the FFSS.
 The fission-fusion social organization based animals are societal and survive in groups of 40-50 individuals.
The FFSS of swarm may diminish the foraging antagonism among group members by separating them into
sub-groups in order to search food [10].
 A most senior female in general leads the group and is conscientious for searching food sources. It is
denoted as global leader. If she is not competent to search out an adequate amount of food for the group,

Sandeep Kumar et al., International Journal of Emerging Technologies in Computational and Applied Sciences, 7(2), December 2013-
February 2014, pp. 198-204
she divides the group into minor subgroups (size may vary from 3 to 8 individuals) that forage
autonomously.
 Sub-groups are also believed to be leaded by a female who becomes decision-maker for planning a well-
organized foraging route each day. Here this leader is known as local leader [10].
 The group members exchange a few words among themselves and with other group members, to uphold
social bonds and protective boundaries [10].
In the SMO algorithm, foraging behavior of FFSS based flora and fauna (like spider monkeys) is alienated into
four stages.
Step 1. The group starts food foraging and appraises their distance from the food.
Step 2. Group members bring up to date their positions based on the distance from the foods source and all
over again appraise distance from the food sources.
Step 3. Moreover, in this step, the local leader updates its best location within the group and if the location is
not updated for a predefined number of times then all members of that group start searching of the food
sources in different directions.
Step 4. Subsequently, in the last step, the global leader keep informed its eternally best position and in case of
stagnation, it divides the group into smaller size subgroups.
Above described four steps mentioned aforesaid, are constantly executed until the preferred output is achieved.
It is observed that in SMO algorithm there are two most important control parameters are Global Leader Limit
(GLlimit) and Local Leader Limit (LLlimit) which provide appropriate direction to global and local leaders
respectively. In SMO stagnation can be avoided by using LLlimit. If a local group leader does not keep informed
her-self after a predefined number of times then that group is re-directed to another direction for in order to
search food. Here, the term predefined number of times is referred as LLlimit. An additional control parameter,
that is to say Global Leader Limit (GLlimit) is used for the identical intention by global leader. The global leader
divides the group into smaller sub-groups if she does not update in a predefined number of times that is GLlimit
[10].
A. Analogy between SMO and Swarm intelligence Behaviour
The SMO algorithm also follows self-organization and division of labor properties for obtaining intelligent
swarming behaviors of natural world.
Self Organization: Self organization includes positive feedback, negative feedback, fluctuations and multiple
interactions [11].
Positive Feedback: As monkeys keep updating their locations by learning from local leader, global leader and
self experience in the first and subsequent steps of SMO algorithm, it shows encouraging criticism mechanisms
of self-organization.
Negative Feedback: Local leader limit and Global leader limit provides negative feedback to help local and
global leader’s for their decisions.
Fluctuations: The 3rd
step in which the languished group members are redirected to different directions for
searching food sources, shows fluctuations characteristic.
Multiple Interactions: As each and every monkey in both global and local leader phase communicates with
others it shows multiple interaction property.
Division of Labor: In the 4th
step, at what time the global leader is gets trapped, it splits the groups into minor
subgroups for the purpose of food foraging. This phenomenon mimics division of labour property of spider
monkeys [10].
B. Major steps of Spider Monkey Optimization (SMO) Algorithm
Analogous to the other population-based algorithms, SMO is also a hit and trial based mutual iterative strategy.
The SMO progression consists of seven major phases. The detailed description of each step of SMO
accomplishment is outlined below:
1) Initialization of the Population
At the outset, SMO engenders an unvaryingly disseminated early population of N spider monkeys where each
monkey SMi (i = 1, 2, ..., N) is a vector of dimension D. At this point D is the number of variables in the
optimization problem and SMi represent the position of ith
Spider Monkey (SM) in the population. Each spider
monkey SM corresponds to the potential solution of the problem under consideration. Each SMi is initialized as
follows:
min max min( ) (0,1)ij j j jSM SM SM SM where      (1)
Here SMminj and SMmaxj are lower and upper bounds of SMi in jth
direction respectively.
2) Local Leader Phase (LLP)
The second phase in SMO is Local Leader phase. In this phase SM update its existing location based on the
information from the local leader understanding as well as local group members understanding. The fitness
value of so obtained new location is calculated. If the fitness value of the new location is higher than that of the

February 2014, pp. 198-204
previous location, subsequently the SM updates his location with the new one. The location modernizes
equation for ith
SM (which is a member of kth
local group) in this phase is as follow:
1 2 1 2( ) ( ) (0,1) ( 1,1)newij ij kj ij rj ijSM SM LL SM SM SM Here and             (2)
Where SMij is the jth
dimension of the ith
SM, LLkj represents the jth
dimension of the kth
local group leader
position. SMrj is the jth
dimension of the rth
SM which is chosen randomly within kth
group such that r≠ i.
3) Global Leader Phase (GLP)
After achievement of the Local Leader phase next phase is Global Leader phase (GLP). During GLP phase, all
the SM’s bring up to date their location by means of understanding of Global Leader and local group member’s
understanding. The location modernizes equation for this phase is as follows:
1 2 1 2( ) ( ) (0,1) ( 1,1)newij ij j ij rj ijSM SM GL SM SM SM Where             (3)
Where GLj stands for the jth
dimension of the global leader location and j ∈ {1, 2, ...,D} is the haphazardly
preferred index.
In GLP phase, the locations of spider monkeys (SMi) are updated based on probabilities pi’s which are
considered using their fitness. In this way a better candidate will have more chance to make itself better. The
probability pi may be calculated using following expression (there may be some other but must be a function of
fitness):
max
0.9 0.1i
i
fitness
p
fitness
   (4)
Here fitnessi is the fitness value of the ith
SM and fitnessmax is the maximum fitness in the group. Further, the
fitness of the newly generated position of the SM’s is calculated and compared with the old one and adopted the
better position.
4) Global Leader Learning (GLL) phase
In GLL phase, the location of the global leader is modernized by applying the voracious selection approach in
the population i.e., the location of the SM having most excellent fitness in the population is selected as the
updated location of the global leader. Additional, it is checked that the location of global leader is updating or
not and if not then the Global Limit Count is incremented by 1.
5) Local Leader Learning (LLL) phase
In LLL phase, the location of the local leader is updated by applying the greedy selection in that group i.e., the
location of the SM having unsurpassed fitness in that group is preferred as the modernized location of the local
leader. Next, the updated location of the local leader is compared with the older one and if the local leader is not
updated then the Local Limit Count is incremented by 1.
6) Local Leader Decision (LLD) phase
If any Local Leader location is not updated up to a predefined threshold called Local Leader Limit (LLlimit), then
all the members of that group modernize their locations either by arbitrary initialization or by using mutual
information from Global Leader and Local Leader through equation (5), based on the pr (perturbation rate).
( ) ( ) (0,1)newij ij j ij ij kjSM SM GL SM SM LL Where          (5)
It is understandable from the equation (5) that the updated measurement of this SM is attracted towards global
leader and fends off from the local leader.
7) Global Leader Decision (GLD) phase
In GLD phase, the location of global leader is monitored and if it is not updated up to a predefined number of
iterations thatv is known as Global Leader Limit (GLlimit), then the global leader divides the population into
smaller groups. Firstly, the population is divided into two groups and then three groups and so on till the
maximum number of groups (MG) are formed. Each time in GLD phase, LLL process is initiated to decide on
the local leader in the recently fashioned groups. The case in which maximum number of groups is formed and
even then the position of global leader is not updated then the global leader combines all the groups to form a
single group. As a consequence the anticipated algorithm impersonates fusion-fission structure of SMs. The
absolute pseudo-code of the SMO algorithm is outlined as follow [10]:

February 2014, pp. 198-204
III. Modified Position Update in Spider Monkey Optimization Algorithm
Exploration of the complete search space and exploitation of the best solutions found in its proximity may be
balanced by maintaining the diversity in local leader and global leader phase of SMO. In order to balance
exploration and exploitation of local search space the proposed algorithm modify both local leader phase and
global leader phase using modified golden section search GSS [15] method inspired by memetic search in ABC
[12], Randomized memetic search in ABC [13] and memetic search in DE [14]. The golden section search first
time incorporated in population algorithm by JC Bansal et al [12] in memetic search in ABC (MeABC).
MeABC introduced a new search phase in ABC inspired by Golden Section Search (GSS) [15]. In MeABC only
the best particle of the current swarm updates itself in its proximity. Original GSS method does not use any
gradient information of the function to finds the optima of a uni-modal continuous function. GSS processes the
interval [a = −1.2, b = 1.2] and initiates two intermediate points:
F1 = b − (b − a) × ψ, (6)
F2 = a + (b − a) × ψ, (7)
Here ψ = 0.618 is the golden ratio.
The detailed GSS process [12] is described in Algorithm 2 as follow:
The proposed strategy modify equation (2) and (3) in the following manner. Here f is determined by GSS process
as outlined in above algorithm. Position update in local leader phase is done using Equ (8).
1 2 1 2( ) ( ) ( ) (0,1), ( 1,1), ..(8)newij ij kj ij rj ij rj ijSM SM LL SM SM SM f SM SM Here f decided by GSS               
Position update in local leader phase is done using Equ (9).
1 2 1 2( ) ( ) ( ) (0,1), ( 1,1), ..(9)newij ij kj ij rj ij rj ijSM SM LL SM SM SM f SM SM Here f decided by GSS               
The detailed modified position update in SMO algorithm is outlined in algorithm 3. The proposed algorithm tries
to balance the exploration and exploitation process by controlling step size.
Algorithm 1: Spider Monkey Optimization (SMO) Algorithm:
Step 1. Initialize Population, Local Leader Limit (LLlimit), Global Leader Limit (GLlimit) and Perturbation
rate (pr).
Step 2. Compute fitness (The distance of each individual from corresponding food sources).
Step 3. Select leaders (global and local both) by applying greedy selection.
Step 4. while (Annihilation criteria is not fulfilled) do
Step 5. Generate the new locations for all the group members by using self experience, local leader
experience and group member’s experience. Using Equ. (2)
Step 6. Apply the gluttonous selection process between existing location and newly generated location,
based on fitness and select the better one;
Step 7. Calculate the probability pi for all the group members using Equ. (4).
Step 8. Generate new locations for the all the group members, selected by pi, by using self experience,
global leader experience and group members experiences Using Equ. (3)
Step 9. Update the position of local and global leaders, by applying the greedy selection process on all the
groups.
Step 10. If any Local group leader is not updating her position after a specified number of times (LLLimit)
then re-direct all members of that particular group for foraging by algorithm using Equ. (5).
Step 11. If Global Leader is not updating her position for a specified number of times (GLLimit) then she
divides the group into smaller groups by following steps.
Step 12. End While
Algorithm 2: Golden Section Search process
Input: Optimization function minf(x) s.t. a ≤ x ≤ b and termination criteria
Repeat while termination criteria fulfill
Calculate F1and F2 as follow
F1=b-(b-a)*Ѱ and F2=a+(b-a)*Ѱ here a = −1.2, b = 1.2 and Ѱ=0.618(Golden ratio)
Calculate f(F1) and f(F2)
If f(F1)< f(F2) then
b = F2 and the solution fall in range [a,b]
else
a = F1 and the solution fall in range [a,b]
end if
end while

February 2014, pp. 198-204
IV. Experimantal Results
A. Test Problems
In order to check performance of modified position update in SMO algorithm it is tested over some well
known benchmark optimization functions f1 to f9 (Listed in Table I). These are continuous optimization problems
and have different degrees of complexity, search range and multimodality. Test problems are taken from [16],
[17] with the associated offset values.
B. Experimental setting
To prove the competence of MPU-SMO, it is compared with original SMO algorithms. To test MPU-SMO
over considered problems, subsequent experimental setting is adopted [10]:
 The Swarm size N = 50,
– MG = 5,
– Global Leader Limit (GLlimit)=50,
– Local Leader Limit (LLlimit)=1500,
– pr ∈ [0.1, 0.4], linearly increasing over iterations,
All other parameter setting remains as in its original SMO algorithm [10].
C. Experimental Result Comparison
Statistical results of MPU-SMO with experimental setting as per last subsection are outlined in Table II. Table
II show the comparison of results based on mean function value (MFV), standard deviation (SD), mean error
(ME), average function evaluations (AFE) and success rate (SR) are accounted. Table II shows that most of the
time MPU-SMO do better than other considered algorithms in terms of competence (with less number of function
appraisals) and correctness. Table III shows upshots of table II between MPU-SMO and original SMO algorithm.
The proposed algorithm at all times gets better AFE and most of the time it also improve SD and ME. It is due to
newly introduced modified GSS process in local leader phase and global leader phase.
Algorithm 3 Modified Position Update in Spider Monkey Optimization (SMO) Algorithm:
Step 1. Initialize Population, Local Leader Limit (LLlimit), Global Leader Limit (GLlimit) and Perturbation rate (pr).
Step 2. Compute fitness (The distance of each individual from corresponding food sources).
Step 3. Select leaders (global and local both) by applying greedy selection.
Step 4. while (extermination criteria is not fulfilled) do
Step 5. For finding the objective (Food Source), generate the new locations for all the group members by using
self experience, local leader experience and group member’s experience.
1 2 3 1 2 3( ) ( ) ( ) (0,1), ( 1,1),newij ij kj ij rj ij rj ijSM SM LL SM SM SM SM SM Here decided by GSS                
Step 6. Apply the gluttonous selection process between existing location and newly generated location, based on
fitness and select the better one;
Step 7. Calculate the probability pi for all the group members using.
max
0.9 0.1i
i
fitness
p
fitness
  
Step 8. Generate new locations for the all the group members, selected by pi, by using self experience, global
leader experience and group member’s experiences.
1 2 3 1 2 3( ) ( ) ( ) (0,1) ( 1,1),newij ij j ij rj ij rj ijSM SM GL SM SM SM SM SM Where decided by GSS                
Step 9. Update the position of local and global leaders, by applying the greedy selection process on all the groups.
Step 10. If any Local group leader is not updating her position after a specified number of times (LLLimit) then re-
direct all members of that particular group for foraging by algorithm
if U(0,1)≥pr
min max min( ) (0,1)newij j j jSM SM SM SM Where     
else
( ) ( ) (0,1)newij ij j ij ij kjSM SM GL SM SM LL Where         
Step 11. If Global Leader is not updating her position for a specified number of times (GLLimit) then she divides the
group into smaller groups by following steps.
if Global Limit Count > GLLimit then set Global Limit Count = 0
if Number of groups < MG then
Divide the population into groups.
else
Combine all the groups to make a single group.
Update Local Leaders position.

February 2014, pp. 198-204
Table I Test problems
Test Problem Objective Function Search Range Optimum Value D Accepta
ble
Error
Rosenbrock
1
2 2 2
11
1
100( )( 1 )) (
i D
i i
i
ix x xf x




  
 [-30, 30] f(0) = 0 30 1.0E-01
Zakharov
2 41
2
1 1
1
2
( ) ( ) ( )
2 2
D D
i
i
i
i
D
i
ix ix
xf x
 

    [-5.12, 5.12] f(0) = 0 30 1.0E-02
Inverted Cosine
wave function
2 21
1 1
3
1
( 0.5
( ) (exp(
8
D
i i i i
i
x x x x
f x

 

  
 
2 2
1 1... cos(4 0.5 )i i i iWhere I x x x x   
[-5. 5] f(0) = -D+1 10 1.0E-05
Neumaier 3
Problem (NF3)
2
4 1
1 2
( ) ( 1)
D D
i i i
i i
f x x x x 
 
    [-100, 100] f(0) = -210 10 1.0E-01
Colville function
2 2 2 2 2 2
5 2 1 1 4 3 3
2 2
2 4 2 4
( ) 100( ) (1 ) 90( ) (1 )
10.1[( 1) ( 1) ] 19.8( 1)( 1)
f x x x x x x x
x x x x
       
      
[-10, 10] f(1) = 0 4 1.0E-05
Braninss Function 2 2
6 2 1 1 1( ) ( ) (1 )cosf x a x bx cx d e f x e      
1
2
[ 5,10],
[0,15]
x
x
 

f(-π, 12.275) =
0.3979
2 1.0E-05
Kowalik function
211 21 2
7 21
3 4
( )
( ) ( )i i
i
i
i i
x b b x
f x a
b b x x

 
 
 [-5, 5]
f(0.1928, 0.1908,
0.1231, 0.1357) =
3.07E-04
4
1.0E-05
2D Tripod
function
8 2 1 1 2 1
2 2
( ) ( )(1 ( )) ( 50 ( )(1 2 ( )))
( 50(1 2 ( )))
f x p x p x x p x p x
x p x
    
  
[-100, 100] f(0, -50)=0 2 1.0E-04
Shifted
Rosenbrock
1 2 2 2
9 1
1
1, 2 1 2
( ) (100( ) ( 1) ,
1, [ ,... ], [ , ,....... ]
D
i i i bias
i
D D
f x z z z f
z x o x x x x o o o o



    
    
 [-100, 100] f(o)=fbias=390 10 1.0E-01
Table II Comparison of the results of test problems
Test Problems Algorithm MFV SD ME AFE SR
f1 SMO 4.86E+01 4.10E+01 4.86E+01 203129.5 0
MPU-SMO 7.18E+01 6.17E+01 7.18E+01 200085.7 1
f2 SMO 2.58E-02 2.03E-02 2.58E-02 198086.6 21
MPU-SMO 1.16E-02 4.84E-03 1.16E-02 150209.7 82
f3 SMO -8.99E+00 5.22E-02 5.61E-03 62104.7 98
MPU-SMO -9.00E+00 1.47E-06 8.23E-06 88707.36 100
f4 SMO 1.54E+02 7.05E+02 1.54E+02 146426.6 62
MPU-SMO 5.65E+02 1.12E+03 5.65E+02 140628.8 65
f5 SMO 2.53E-05 8.74E-05 2.53E-05 121470.4 87
MPU-SMO 7.28E-06 2.23E-06 7.28E-06 110818.1 100
f6 SMO 3.98E-01 6.84E-06 6.07E-06 32822.14 85
MPU-SMO 3.98E-01 6.71E-06 5.75E-06 25335.75 89
f7 SMO 3.29E-04 9.93E-05 2.15E-05 113055.6 95
MPU-SMO 3.16E-04 1.74E-06 8.19E-06 93956.36 100
f8 SMO 6.55E-05 2.46E-05 6.55E-05 10498.41 100
MPU-SMO 6.50E-05 2.77E-05 6.50E-05 5926.14 100
f9 SMO 3.91E+02 6.84E+00 1.45E+00 129334.4 75
MPU-SMO 3.90E+02 3.92E-01 1.37E-01 97455.31 93
V. CONCLUSION
This paper suggests two changes in original SMO algorithm. Both local leader and global leader phases are
modified by incorporating GSS process. Newly added steps are inspired by memetic search in ABC and position
update achieved on the basis of appropriateness of individual in order to balance intensification and
diversification of local search breathing space. Additional, the advanced strategy is applied to get to the bottom of
9 well-known benchmark functions. With the help of experiments over test problems, it is shown that the addition
of the proposed strategy in the original SMO improves the trustworthiness, competence and accurateness as
weigh against to their original adaptation. Table II and III show that the anticipated MPU-SMO is competent to
solve largest part the considered problems with smaller amount of time and efforts.

February 2014, pp. 198-204
Table III Summary of table II outcomes
Test Problem MPU-SMO vs. SMO
f1 +
f2 +
f3 +
f4 +
f5 +
f6 +
f7 +
f8 +
f9 +
Total number of + sign 9
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