Comparative study of_hybrids_of_artificial_bee_colony_algorithm

International Journal of Information, Communication and Computing Technology
Jagan Institute of Management Studies, New Delhi
1
Research Scholar, Department of Computer Science & IT, Jagannath University, Jaipur
Email: 1
sandeep.kumar@jagannathuniversity.org
1
Assistant Professor, Department of Mathematics, Jagannath University, Jaipur
Email: 1
vivek.sharma@jagannathuniversity.org
1
Research Scholar, Department of Computer Science & IT, Jagannath University, Jaipur
Email: 1
rajanikpoonia@gmail.com
Copyright ©IJICCT, Vol I, Issue II (July – Dec2013): ISSN 2347- 7202 20
Comparative study of Hybrids of Artificial Bee Colony Algorithm
1
Sandeep Kumar, 2
Dr. Vivek Kumar Sharma, 3
Rajani Kumari
ABSTRACT
Artificial bee colony (ABC) algorithm is a well known and one
of the latest swarm intelligence based techniques. This method
is a population based meta-heuristic algorithm used for
numerical optimization. It is based on the intelligent behavior
of honey bees. Artificial Bee Colony algorithm is one of the
most popular techniques that are used in optimization
problems. Artificial Bee Colony algorithm has some major
advantages over other heuristic methods. To utilize its good
feature a number of researchers combined ABC algorithm with
other methods, and generate some new hybrid methods. This
paper provides comparative analysis of hybrid differential
Artificial Bee Colony algorithm with hybrid ABC – SPSO,
Genetic algorithm and Independent rough set approach based
on some parameters like technique, dimension, methodology
etc.
KEYWORDS
Optimization techniques, Nature inspired techniques, Artificial
Bee Colony algorithm, DE, Genetic Algorithm, Rough Set,
PSO.
1. INTRODUCTION
Optimization problems have been solved by a number of
techniques. Now a day an alternative to the traditional methods
in operations research we use heuristic methods to solve
optimization problems.
The Nature Inspired methods are methods that are inspired by
natural and biological events, like immune system, foraging
behavior of ants and some other insects. Swarm intelligence is
one of the branches of nature inspired algorithms which are
used for function optimization. This branch is inspired by
intelligent behavior of insects [1]. Swarm intelligence an
algorithm leads to develop new meta-heuristics which can
utilize insect's problem solution abilities [2]. It has also been
investigated by a number of researchers that these meta
heuristic algorithms are able to provide very good solutions in
comparison to traditional algorithms. Optimization Techniques
is a unique reference source of methods for achieving the
optimal solution [3]. Optimization is stream computational
science in which we studies techniques for finding the best
possible solutions.
The organization of rest of paper is as follow: In section 2, we
introduce Artificial Bee Colony Algorithm, one of the newest
swarm based method introduces by D. Karaboga [4]. Next
section introduces hybrid optimization techniques. Section 4
introduces genetic algorithms. Next section describe hybrid of
ABC algorithm and Evolutionary Algorithm. Section 6 outlines
a hybrid of ABC and SPSO. Next section gives hybrid
algorithm of ABC and GA. Section 8 gives hybrid algorithm of
ABC and Rough Set approach. In next section we decided
some parameters to compare these four hybrid methods and
finally conclude the result in section 10.
2. ARTIFICIAL BEE COLONY ALGORITHM
The ABC algorithm is based on intelligent behavior of honey
bee swarm. This algorithm proposed by Dervis Karaboga in
2005 [4]. The major advantage of ABC over other swarm
intelligence methods is that in the ABC algorithm the possible
solutions indicate food sources (in this case flower); in spite of
individuals (honeybees swarm). In this method we divide bees
into two equal parts one is known as onlooker and other as
employee bees. Onlookers are applied to a food source
depending on the profitability. Onlookers compute a new
solution from its food source. When a food source is exhausted,
the bees employed on it converted into unemployed bees and
they have two options either they become a scout bee and
employed on another food source to exploit randomly or may
become an onlooker bees and waiting for information from
another bees about other food sources that are currently
exploited.
After fixed number of cycles (Maximum number of cycles that
is called as limit), if food source cannot be further improved, it
is rejected and replaced by randomly computed food source.
This is called exploration process and it takes place with help
of scout bees. It can be concluded that, employed and onlooker
bees are responsible for exploitation process, while scout bees
are engaged in process of exploration. Total number of
employed bees is equal to the number of food sources, each
employed bee represent a food source. Food source represents a
solution for the problem that need to be optimized. The basic
steps of the ABC algorithm are as given below [4], [5].
ALGORITHM 1: ABC ALGORITHM
 Compute the initial population of solutions
 Estimate the population

o Compute new solutions for the employed
bees
o Apply the greedy selection mechanism
o Estimate the fitness and the probability
values
o Compute the new solutions for the onlookers
o Apply the greedy selection mechanism
o Determine the abandoned solution for the
scout bee, and change it with a latest
randomly generated solution
o Memorize the best solution achieved so far
 Repeat the above steps for pre decided limit
Artificial bee colony algorithm can be divided in four phases.
Each of the phases is outlined as follows:
2.1 Initialization of the swarm
The ABC algorithm has some parameters like: the number of
food sources, the number of trials (a threshold value after
which a food source is assumed to be discarded) and the
criteria for termination. In the original ABC, the number of
food sources is equal to the population of employed bees or
onlooker bees. Initially, a equally distributed initial swarm of
total food sources where each food source xi(i = 1, 2, ..., SN) is
a vector of D-dimension generated. Here D is taken as the
number of variables in the target problem and xi represent the
ith
food source in the swarm. Each food source is generated as
follows:
xij = xminj + rand[0,1] (xmaxj − xminj), (1)
here xminj and xmaxj are bounds of xi in jth
direction and rand [0,
1] is an evenly dispersed random number in the range [0, 1].
2.2 Employed bee phase
Second phase is employed bee phase; in this step modification
in the current solution (food source) is done by employed bees
as per the information gathered by individual experience and
the new fitness value. The position of ith
candidate updated
using equation 2.
(2)
here k ∈ {1, 2, ..., SN} and j ∈ {1, 2, ...,D} are arbitrarily taken
indices. k must be different from i. is a random number
between range [-1, 1].
2.3 Onlooker bees phase
In this phase, all the employed bees transfer information to
onlooker bees in the hive. Now they analyze this information
and select a solution with a probability probi related to its
fitness, which can be calculated using below mentioned
equation.
(3)
here fiti represent the fitness of the ith
solution.
2.4 Scout bees phase
If position of a food source not updates for some predecided
number of times then it is discarded. In this step, some bee
becomes scout whose food source has been rejected. The scout
bees change this food source by with help of equation 4.
xij = xminj + rand[0,1](xmaxj − xminj), for j∈{1,2,.,D} (4)
where xminj and xmaxj are bounds of xi in jth
direction.
The major reason behind use of ABC in case of separable
functions is that its modification step only modify exactly on
problem variable at a time after which the new solution is re-
computed. The performance of ABC algorithm has been
compared with some another optimization methods by many
researchers and they suggested modification in original ABC
algorithm in their way. One of those suggestions is a best-so-
far method for solution modification in the Artificial Bee
Colony algorithm was proposed in [6]. They replaced the
neighboring solutions-based approach with the best-so-far
technique to enhance the local searching power of the onlooker
bees. The searching method based on a dynamic adjustment of
search range depending on the iteration was introduced for
scout bees [6].
3. HYBRID OPTIMIZATION TECHNIQUES
Many techniques exist for combinatorial optimization.
Roughly, we will divide the sequence of accessible ways into
actual ways, that are in essence able to finding a best answer
and proving optimality however may have excessive
computation times, and heuristic ways together with
metaheuristics and evolutionary algorithms. Each world has
their specific properties, advantages, and disadvantages. We
work on the development of more practical hybrid optimization
systems exhibiting benefits of each world. In several
application areas, like network style, cutting and packing,
issues on graphs in general, scheduling, timetable management
and routing, such hybrids usually yield higher solutions in
shorter time. In distinction to classical heuristics, quality
guarantees or often even optimality-proofs will typically be
provided for obtained solutions [8].
In specific, we have a tendency to mix local-search-based
techniques like simulated annealing, tabu search, evolutionary
algorithms, and variable neighborhood search with clever
enumeration techniques using applied mathematics, like
cutting-plane algorithms, branch and prize, branch and cut and
branch and cut and prize. Beside the mix of metaheuristics with

International Journal of Information, Communication and Computing Technology (IJICCT)
precise ways, we've additionally approached several issues by
mixtures of evolutionary algorithms and problem-specific
heuristics, native improvement and repair techniques. Such
mixtures are usually termed memetic algorithms [8].
The Nature Inspired methods have some advantages and
disadvantages as they are motivated by natural events. If these
methods are used individually they may inhibit some
weaknesses. These methods are complementary. To utilize the
advantages of various Nature inspired algorithm while avoiding
their drawbacks promotes advances in hybrid Nature inspired
algorithm. The idea behind motivation for hybridization of two
or more nature inspired computing algorithms is to enhance
rate of convergence, robustness, and reliability [7], [8].
The major goal behind development of new hybrids of ABC
algorithm is to improve rate of convergence. Hybrid Nature
inspired Computing methods [8] can be classified into different
categories according to the dimensions used, like application,
nature of problem, motivation for hybridization and
architecture of hybridization. In general, hybrid NIC methods
may be classified into completely different classes, based on
the measures used, like motivation for union and design of
union. We can divide them into „preprocessors and
postprocessors‟, „cooperators‟, and „embedded operators‟
supported by the link among the whole Nature impressed
formula concerned. Actually, a careful and comprehensive
analysis of the classification of the union would facilitate to
realize a deep understanding of the character impressed
formula and conjointly select the most effective combos for the
targeted improvement issues [8].
4. GENETIC ALGORITHM
Genetic Algorithm is traditional algorithm. This algorithm
proposed by Jhon Holland [14] in 1975. T A basic Genetic
Algorithm (GA) consists of 5 parts and major operators. First
of these part is a random number generator. Second is a fitness
analysis unit. Third part is genetic operators for reproduction,
forth is crossover phase and last is mutation operations. The
major algorithmic rules are outlined below:
 Initialize Population
 Repeat
o Analysis
o Reproduction
o Crossover
o Mutation
 Till the criteria met
The initial population needed at the beginning of the
algorithmic rule, could be a set of variety strings generated by
the random generator. Every string could be a illustration of an
answer to the improvement downside being addressed. Binary
strings square measure ordinarily used. Related to every string
could be a fitness price computed by the analysis unit. A fitness
value decides the quality of solution.
The aim of the genetic operators is to remodel this set of
strings into sets with higher fitness values. The reproduction
operator performs a simple selection process that is referred to
as seeded selection. Individual strings are derived from one set
to future per their fitness values, the higher the fitness worth,
the bigger the likelihood of a string being selected for future
generation. The crossover operator chooses pairs of strings
every which way and produces new pairs. The best crossover
operation is to chop the initial parent strings at a at random
selected purpose and to exchange their tails. The amount of
crossover operations is ruled by a crossover rate. The mutation
operator at random mutates or reverses the values of bits during
a string. The amount of mutation operations is set by a
mutation rate. A part of the formula consists of applying the
analysis, copy, crossover and mutation operations. A brand
new generation of solutions is made with every part of the
formula [13].
5. HYBRID DIFFERENTIAL ARTIFICIAL BEE
COLONY ALGORITHM
In this section we are going to discuss one of the powerful
heuristic methods, to solve non-linear, non-differentiable and
multi model optimization problems that is called as Differential
Evolution (DE). This simple and most powerful evolutionary
algorithm was given by Price and Storn. In last decade, DE
algorithm has become most popular in the field of machine
intelligence. Differential evolution combines mutation,
crossover and selection operators [9]. Differential Evolution an
evolutionary algorithm is a most powerful heuristic for solving
non-linear, non-differentiable and multi model optimization
problems [10].
The basic idea behind DE is a scheme for computing trail
parameter vectors. Mutation and crossover are used to compute
new vectors, and selection then after decides which of the
vectors have to be surviving for the next computation.
Classical evolutionary operators described by [9] can be stated
as follows:
 Mutation: Mutation is a process in which DE
generates a donor vector Vi_G corresponding to each
population individual (member) or target vector Xi_G
in the current generation, after initialization of
population.
 Crossover: Under this operation, the donor vector
exchanges its components with the target vector Xi_G
to form the trail vector.
 Selection: Selection determines which of the vectors
have to be surviving for the next generation. If the
new trail vector yields an equal or lower value of the
objective function, it alters the corresponding target
vector in the next generation; otherwise the target is
retained in the population.

Hybridizing the Differential Evolution with heuristic
algorithms is expected to provide better convergence and
desired values. We proposed a new Hybrid Differential
Artificial Bee Colony (HDABC), which combines ABCA with
Differential Evolution algorithm. The goal of integrating DE
with ABC Algorithm is achieve better result, to combine their
advantages, and to remove their disadvantages [9].
The basic steps of HDABC Algorithm are as follows:
ALGORITHM 2: HDABC ALGORITHM
 Initialize all parameters
 Send the employed bees onto their corresponding food
sources and Compute their nectar amounts.
 Identify the position of the onlooker bees depending
upon the nectar amounts obtained by employed bees.
 Employ the scouts for exploiting new food sources.
 Memorize the best food sources identified so far.
 Select best (best fitness value) n differential vectors
from the population based on the fitness calculated to
generate initial population for Differential evolution
 DO
o FOR i = 1 to number of particles
o DO
 Mutation
 Crossover
 Selection
 END FOR
 If a termination criteria is not satisfied, go to second
step; otherwise stop the process and compute the best
solution is obtained so far [9].
The planned hybrid methodology additionally employs
identical constant quantity established that had been used by
the artificial Bee Colony algorithmic rule and Differential
Evolution. The sole amendment is that a number of the
numerical values are been accomplished than that of basic
parameters. During this hybrid theme, once the ABC completes
random improvement. Best ten food supplies are picked out
supported the fitness and these are the initial population for
Differential Evolution algorithmic rule. Then the population is
finely tuned by DE with support of the operators: Mutation,
crossover and choice. Because the best fitness value cannot be
obtained in single iteration that‟s why we set it as twenty
generations i.e., when each ABC iteration DE was allowed to
run supported by the user outlined generations. Combining
these 2 improvement techniques are expected to bring best
values with in lesser number of generations and additionally to
possess higher convergence speed [9].
6. A HYBRID ABC-SPSO ALGORITHM FOR
CONTINUOUS FUNCTION OPTIMIZATION
Particle Swarm optimization is based on swarm intelligence
algorithmic rule. This algorithmic rule simulates the movement
of a swarm of birds or a group of fish searching for food. PSO
is a metaheuristic because it makes few or no assumptions
concerning the matter being optimized and might search very
massive areas of candidate solutions. However, metaheuristics
like PSO don't guarantee for the best answer is ever found.
More specifically we can say that PSO doesn't use the gradient
of the matter being optimized, which implies PSO doesn't need
that the optimization problem be differentiable as is needed by
classic optimization strategies like gradient descent and quasi-
Newton strategies. PSO will thus even be used on optimization
issues that are partly irregular, noisy, modification over time,
etc. It has been wide applied to several benchmark functions
likewise as engineering applications with an excellent success.
The standard PSO (SPSO) algorithmic rule provides a choice
for a rotation of the random step in order to form it less
sensitive to rotations. Another advantage is that SPSO is
extremely prosperous for uni-modal optimization functions
[11].
ABC-SPSO is hybridizing two successful algorithms on the
components level in order to gain benefit from their respective
strengths [11]. This is hybridization of Particle Swarm
Optimization (PSO) with Artificial Bee Colony Algorithm
(ABC) [12]. The goal of integrating PSO with ABC is to
improve the personal bests of the particles. In hybrid algorithm
mix components from both ABC and SPSO in order to have an
algorithm that easily solve separable problems as ABC while
having a rotationally invariant behavior as SPSO at the same
time[11], [12].
For every particle 𝑖 in the swarm, the ABC algorithm update
equation is applied to its personal best pbest𝑖. This is done after
randomly selecting another particle 𝑘 and a random problem
variable 𝑗, hence pbest𝑖 is updated as follows:
𝑝𝑏𝑒𝑠𝑡𝑖𝑗 = 𝑝𝑏𝑒𝑠𝑡𝑖𝑗 + 𝜙𝑖𝑗 × (𝑝𝑏𝑒𝑠𝑡𝑖𝑗 − 𝑝𝑏𝑒𝑠𝑡 𝑘𝑗) (5)
The new pbest𝑖 value replaces the previous one if it has a better
fitness. These steps applied on the some benchmark functions
that are categorized into various classes like [11].
 Uni-modal functions
 Multimodal functions
 Hybrid functions
ALGORITHM 3: ABC-SPSO ALGORITHM
 Initialize the swarm population
 Evaluate the swarm
 Max_Iterations = 𝑀𝑎𝑥_𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛_𝐸𝑣𝑎𝑙𝑢𝑎𝑡𝑖𝑜𝑛𝑠
𝑁𝑢𝑚_𝑃𝑎𝑟𝑡𝑖𝑐𝑙𝑒𝑠
 Iteration_number=1
 WHILE 𝐼𝑡𝑒𝑟ation_𝑛𝑢𝑚𝑏𝑒𝑟 ≤ 𝑀𝑎𝑥_𝐼𝑡𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑠
 DO
o FOR( each particle 𝑖)

o DO
 Modify v𝑖
 Modify x𝑖
 IF (pbest𝑖) ≤ (x𝑖) then
 pbest𝑖 = x𝑖
 end IF
o END FOR
o Update gbest
o FOR each particle 𝑖 do
 Choose a different random particle
 Choose a random problem variable
 Employ ABC update rule to pbest𝑖
 Modify pbesti and gbest
o END FOR
o Iteration_number = Iteration_number + 1
 END WHILE
 Return gbest
Above defined algorithm propose a hybridization of ABC and
SPSO. This is possible by incorporating an ABC component
into SPSO, which updates the personal best information of the
particles in each iteration using the ABC update method. Both
approaches are verified, one that updates all the particles using
the ABC component and one that updates the particles in
fitness proportionate approach. The proposed hybrid algorithm
able to retain the good SPSO performance in case of uni-modal
optimization problems while retaining the good ABC
performance on separable optimization problems as well.
However, the major disadvantage is that the ABC component is
only able to improve particles in a low percentage of the
computational power allocated to it [11].
7. HYBRID OF ARTIFICIAL BEE COLONY
ALGORITHM WITH GENETIC ALGORITHM FOR
NUMERICAL OPTIMIZATION
A newly proposed Hybrid Artificial Bee Colony (HABC)
algorithm, which combine canonical ABC with Genetic
Algorithm(GA) is use a real value single-point crossover
operator of Genetic Algorithm which is improve the canonical
ABC in solving complex optimization problems. This
algorithm deals with high dimension problems. This
optimization technique is used to find near-optimal solutions to
the complex numerical and engineering optimization problems.
Here only change in ABC algorithm is addition of crossover
phase among onlooker bees and scout bees [6], [7].
The crossover operator crosses two parent individuals to
produce new ones. Usually, the parent individuals are selected
with higher fitness. So, the good gene information will be
inherited and the individuals newly produced may be good
ones. A crossover point is generated randomly within the
dimension, and the two parent individuals exchange the values
of dimensions after the crossover point.
ALGORITHM 4: HABC
 Set Iteration_number =0;
 Initialize the food source positions;
 Compute the fitness value of food sources;
 WHILE
o FOR (every employed bee)
 Identify a new food source;
 Compute the fitness of the new food
source;
 Employ greedy selection method;
o END FOR.
 Calculate the probability P for each food source;
 FOR(each onlooker bee)
o Send onlooker bees to food sources
depending on Probability p;
o Produce a new food source;
o Compute the fitness of the new food source;
o Employ greedy selection method;
 END FOR
 Produce parent population applying tournament
selection;
 Select a certain amount of bad food sources;
 FOR(each selected food source)
o Select two parents randomly from the parent
population
o Produce two new food sources by crossing
the selected parents;
o Employ greedy selection for the selected
food source and the newly produced food
sources;
 END FOR
 IF(an employed bee becomes into a scout bee)
o Send the scout bee to a new randomly
produced food source;
 END IF
 Remember the best solution achieved so far;
o Iteration_number = Iteration_number +1;
 END WHILE
Now we can conclude that, the HABC algorithmic rule
employs a new management parameter to introduce a crossover
operator within the ABC algorithmic rule. With the new
operator, one can exchange additional data within the early
stage of the algorithmic rule, which reinforces the convergence
ability of the algorithmic rule. At the tip of the algorithmic rule,
the distinction between individuals‟ decreases and the agitation
of crossover operator also decreases. Population move towards
the improvement purpose.

8. HYBRID OF ARTIFICIAL BEE COLONY
ALGORITHM WITH INDEPENDENT ROUGH SET
APPROACH FOR DIMENTIONALITY
REDUCTION
Rough set approach provides a mathematical mechanism that
can be used for machine learning, feature selection and
knowledge mining [8].
A new approach for dimensionality reduction proposed in [9].
This algorithm is hybrid of Artificial Bee Colony (ABC)
algorithm with independent Rough Set Approach. This
approach work in two steps:
 Identify the subset of attributes independently based
on decision attributes.
 Identify the final reduct [9].
This approach is used to find out the set of minimal attributes,
that is called „reducts‟ to classify objects without degradation
in quality of classification and generate minimal length
decision rules deep-rooted in a given information system. In
order to improve the performance of this method, an element of
pruning was introduced. Quick Reduct designate for a
dimensionality of n. For the worst-case dataset, evaluations of
the dependency function may be performed (n2
+n)/2 times. The
dependency of each attribute is estimated and it selects the best
candidate [9].
This hybrid algorithm is used to solve both constrained and
unconstrained type of optimization problems. This hybrid
algorithm uses the rough set theory in a number of real world
domains. The performance of this approach is analyzed with
five different medical datasets namely Dermatology, Cleveland
Heart, HIV, Lung Cancer and Wisconsin and compared with
six other reduct algorithms [9].The algorithm given by [9] is as
follow:
ALGORITHM 5: IQRBEE(Fc,Fd)
 Fc, the set of all conditional features;
 Fd, the set of decision features.
 Bundle the domain
 Identify the reduct for each class
 Build-up the core reduct and reduct sets
 Choose the initial parameter values for ABC
 Initialize the population (xi)
 Compute the objective and fitness value
 Find the optimum feature subset as global
 do
o Produce new feature subset(vi)
o Apply the greedy selection between xi and vi
o Calculate the fitness and probability values
o Generate the solutions for onlooker
o Employ the greedy selection for onlookers
o Decide the rejected solution and scouts
o Compute the cycle best feature subset
o Remember the best optimum feature subset
 Repeat up to max_number_of_cycles
The suggested algorithm can be used for feature reduction. Let
the bees choose the feature subsets arbitrarily and calculate
their fitness and realize the simplest one in every iteration. This
procedure is continual for variety of iterations to search out the
optimum set. As mentioned earlier, when selecting the core
reduct, with the remaining attributes at every cluster, the
utilized bee produces the feature subset in random. Consider a
domain that contains variety of distinctive decision values, then
an equivalent range of bees has been chosen because of the
population size. From this population half the bees are thought-
about as utilized bees and therefore the remaining half is
thought-about as onlooker bees. For every used bee, a random
subset from one reduct set is allotted. The random sets allotted
to all or any the bees are combined to make the feature subset
[9].
For instance, contemplate a database that contains ten numbers
of conditional attributes (c1,c2,…,c10) and three numbers of
call attributes with five hundred records. At the start the
records are bundled into three groups supported the choice
attribute then the reduct is applied for every cluster. As an
example, if we have a tendency to getting the reduct as:
R1 = {c1,c3,c4,c8}
R2 = {c3,c4,c9}
R3 = {c3,c4,c6,c7,c10}
From these reducts, the common attributes are chosen as base
reduct; during this example, Rc = {c3,c4} and also the
remaining attributes are off from every reduct:
R1 = {c1, C8}; R2 = { c9}; R3 = {c6,c7,c10}
Now three bees are employed to generate a reduct, by choosing
random subsets from these reducts and bundling them with the
base to find the optimum one. Like:
 Rc + Bee_1 = {c8} + Bee_2 = {c9} + Bee_3 =
{c7,c10}
 {c3,c4,c8,c9,c7,c10}
This new reduct is computed using the ABC algorithm. In the
second step of the algorithm, for every employed bee, That are
exactly half of the number of food sources, a new source is
generated as follow:
Vi,j = Xi,j +Ø(Xi,j – Xk,j) (6)
Where, Øij may be a uniformly distributed real random number
among the range [-1,1], k is the index of the answer chosen
indiscriminately from the colony (k = int (rand * N) + 1), j = 1,

. . .,D and D is the dimension of the problem. When generating
vi, this new answer is compared to the existing solution Xi and
therefore the utilized bee exploits the higher supply. In the last
step of the algorithmic rule, a witness bee chooses a food
supply with the likelihood and produces a brand new supply in
designated food supply domain. As for utilized bee, the higher
supply is set to be exploited. The projected hybrid methodology
comes out with a far better optimal reduct than the others; that
shows its superior performance [9].
9. PARAMETER
In this paper, the following parameters are used for comparison
between hybridization of artificial bee colony algorithms.
 Hybrid
 Aim
 Techniques
 Methodology
 Dimension
 Developer
 Linear/Non linear
 Constrained/Unconstrained
 Known application area
The comparative analysis of the different hybrids of ABC
optimization technique with Genetic algorithm and
Independent rough set is given in form of table-1 at the end of
paper.
10. CONCLUSION
There are a large number of hybrids of NIC methods to solve
complex optimization problems. These hybrids can be
classified as per their applicability to the problem and nature of
problem. It may be possible that one problem can be solved by
many techniques. We can apply more than one optimization
techniques to solve the same problem. So in this situation, the
complexity plays an important role. It also matter through
which technique the best result is obtained whether that result
is optimum or not.
Now, the different techniques have different complexity. Any
problem must be solved with minimum complexity but also
provide the optimal solution. Here both ABC-SPSO and
HDABC techniques are able to solve constrained and
unconstrained problem, ABC-SPSO can be applicable in both
2D/3D where as HDABC is able to solve the problem of D-
dimensional space. ABC-SPSO and HDABC both can solve
non-linear problems. Different problems can find optimal
solution with different techniques. Hybrid Artificial Bee
Colony algorithm improve the canonical ABC in solving
complex optimization problem whereas Hybrid of Artificial
Bee Colony Algorithm with Independent Rough Set Approach
for Dimensionality Reduction can solve constrained/
unconstrained optimization problem and use mathematical
tools for both feature selection and knowledge discovery. This
paper these techniques are presented in an abstract way i.e. in
the form of table so that user can choose the technique
according to the problem.
REFERENCES
[1] L. A. Zadeh, “The roles of soft computing and fuzzy logic
in the conception, design and deployment of intelligent
system,” in Proceedings of the IEEE Asia Pacific
Conference on Circuits and Systems, Seoul, Korea,
November 1996, pp. 3-4.
[2] M. Subotic, M. Tuba, N. Stanarevic, “Parallelization of the
Artificial Bee Colony (ABC) Algorithm” Recent Advances
in Neural Networks, Fuzzy Systems & Evolutionary
Computing, ISSN: 1790-5109,2010, pp. 191-196.
[3] K. Price, “An Introduction to differential evolution,” New
Ideas in Optimization. D. Corne, M. Dorigo, and F. Glover
(Eds.), London, UK: McGraw Hill, 1999.
[4] D. Karaboga, “An Idea based on Bee Swarm for
Numerical Optimization”, Technical Report, TR-06,
Erciyes University Engineering Faculty, Computer
Engineering Department (2005).
[5] D. Karaboga, A. Basturk, “A powerful and efficient
algorithm for numerical function optimization: artificial
bee colony (ABC) algorithm”, J. of Global Optimization,
Volume 39, No. 0925-5001, 2007, pp. 459-471.
[6] A. Banharnsakun, T. Achalakul, B. Sirinaovakul, “The
best-so-far selection in Artificial Bee Colony algorithm”,
Applied Soft Computing 11 (2011), pp.2888–2901.
[7] L. N. de Castro, “Fundamentals of natural computing: An
overview,” Physics of Life Reviews, vol. 4, no. 1, pp. 1-36,
2007.
[8] X. Wang, X. Z. Gao, and S. J. Ovaska, “Hybrid Nature-
Inspired Computation Methods for Optimization” IEEE
Soft Computing Research, e-newslatter, December 2009,
issue#29
[9] A. Abraham, R. K. Jatoth, A. Rajasekhar, “Hybrid
Differential Artificial Bee Colony Algorithm” Journal of
computational and theoretical Nanoscience Vol.9,1-
9,2012.
[10]K. Price and R. Storn, Differential Evolution – a Simple
and Efficient Adaptive Scheme for Global Optimization
over Continuous Spaces, Technical Report, International
Computer Science Institute, Berkley (1995).
[11]Md. El-Abd, “A Hybrid ABC-SPSO Algorithm for
Continuous Function Optimization” in IEEE Symposium
on Swarm Intelligent System (SIS), 2011.
[12]J. Kennedy and R. C. Eberhart, “Particle swarm
optimization,” in Proceedings of IEEE International
Conference on Neural Networks, vol. 4. Piscataway, NJ:
IEEE Press, 1995, pp. 1942–1948.
[13]D. Karaboga, B. Akay, “A comparative study of Artificial
Bee Colony algorithm”. Applied Mathematics and
Computation 214 (2009), pp. 108–132
[14]J. J. Holland, Adaptation in Natural and Artificial Systems,
University of Michigan Press, 1975.

Table 1. Comparative Analysis of Hybrid of ABC Algorithm
SN Parameter HABC ABC – PSO HDABC BEEIQR
1 Hybrid ABC and Genetic
Algorithm
ABC and SPSO ABC and DE ABC and RSAR
2 Aim Improve the
canonical ABC in
solving complex
optimization
problem.
Aimed at mixing
components from
both ABC and
SPSO in order to
have an algorithm
that easily solve
separable problem
as ABC while
having a
rotationally
invariant behavior
as SPSO at the
same time.
Optimization
process is applied
to each and every
individual in the
population
followed by
further
improvement
using Differential
Evolution search
Solve constrained/
unconstrained optimization
problem and use mathematical
tools for both feature selection
and knowledge discovery.
3 Technique Single point
crossover operator
of Genetic
Algorithm
Population based
search and
stochastic
optimization
method.
Mutation,
Crossover and
Selection operator
with Differential
Evolution.
Rough set theory in a number
of real world domains.
4 Methodolog
y
 Set i=0
 Initialize the
food source
positions.
 Evaluate the
nectar amount
(fitness).
 While the
termination
condition are
not met
 Execute
employed bees
phase
 Calculate the
probability P
for each food
source.
 Execute
onlooker bees
phase
 Execute
Crossover
phase
 Execute scout
 Initialize and
evaluate the
swarm
 Calculate Max
iterations
 For each
iteration
 For every
particle i
 Update vi and
xi
 If f(pbesti)<
f(xi)
 Pbesti = xi
 Update gbest
 For every
particle i
 Choose diff
random
particle and
random
problem
variable
 Apply ABC
 Initialization
While the
termination
criteria is not
met
 Apply ABC
algorithm
 Select best n
differential
vectors from
the
population
based on the
fitness
calculated to
generate
initial
population
for DE.
 For each
particle s
apply
Mutation
Crossover
Selection
 Stop
 Cluster domain
 Find reduct for each class
 Select initial parameter
for ABC
 Initialize population
 Calculate objective and
fitness value
 Find optimum feature
subset
 Repeat for maximum no
of cycles
 Produce initial solution
and apply greedy
selection
 Stop condition

bees phase
 Memorize the
best solution
achieved so
far.
 i=i+1.
 Stop condition
update rule to
pbesti
 Update pbesti
and gbest
 Stop condition
condition
5 Dimension 2D/3D 2D/3D D- Dimension
space
-
6 Developer(s
)/
Proposer(s)
Xiaohui Yan,
Yunlong Zhu,
Wenping Zou
Mohammed El-Abd Ajith Abraham,
Ravi Kumar, A.
Rajasekhar
Nambiraj Suguna, Keppana
Gowder
7 Year 2011 2011 2012 2011
8 Linear/
Non Linear
Linear Non – linear Non - linear -
9 Constrained
/
Unconstrain
ed
Both Both Both Both
10 Known
application
area
Solve numerical
optimization
function
Function
optimization,
ANN, Fuzzy
control system
Real world
application:
Dynamic load
dispatch,
harmonic
estimation radar
tracking
Real world application:
Medical field
11 References [3] [4] [5] [6] [7] [8] [9] [10] [11]

Comparative study of_hybrids_of_artificial_bee_colony_algorithm

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (17)

Destaque

Destaque (14)

Semelhante a Comparative study of_hybrids_of_artificial_bee_colony_algorithm

Semelhante a Comparative study of_hybrids_of_artificial_bee_colony_algorithm (20)

Mais de Dr Sandeep Kumar Poonia

Mais de Dr Sandeep Kumar Poonia (13)

Último

Último (20)

Comparative study of_hybrids_of_artificial_bee_colony_algorithm