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CHAPTER 4

ARTIFICIAL BEE COLONY WIRELESS CLUSTERING

4.1. INTRODUCTION TO ARTIFICIAL BEE COLONY

Swarm intelligence is a new discipline of study that contains a

relatively optimal approach for problem solving which are the imitations

inspired from the social behaviour of insects and animals, for example,

Ant Colony Optimization (ACO) algorithm, Honey Bee Algorithms, Fire

inspiration for the design of novel algorithms, which is the solution for

optimization and distributed control problems. The Honey Bee Mating

algorithm is the growing technique, which is proposed in late 2005, for

many engineering applications.

Honey bees are insects that live in large colonies (around

50,000 bees as a colony) usually containing one queen and her progeny,

some 20,000 40,000 female workers and 200 300 male drones. Michael

et al (2010) has a detailed study of honeybee in the biological aspect and

about the foraging behaviour. There are many syndromes observed like

aggression syndrome, waggling dance, from the honey bee colony which

is used for solving optimization problems. Although honey bees are

depicted in many cave paintings dated from 6000BC, Aristotle made the

first recorded observation of bee behaviour.

The honey bee is a diffuse creature which can extend itself over

long distances in multiple directions in order to find a large number of

food sources and at the same time to find the best food source from the

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collection of food sources. For example, the flower patches with plentiful

amounts of nectar or pollen that can be collected with less effort should

be visited by more bees, whereas patches with less nectar or pollen should

receive fewer bees.

The foraging process begins in a colony by scout bees, these

bees are sent to search for promising flower patches. Scout bees search

randomly from one patch to another. When they return to the hive, those

scout bees that identified from a patch which is rated above a certain

threshold which is measured as a combination of some constituents, such

as sugar content, deposit their nectar or pollen and go to the "dance floor"

to perform a dance known as the "waggle dance".

This dance is essential for colony communication, and contains

three vital pieces of information regarding flower patches: the direction in

which it will be found, its distance from the hive and its quality rating (or

fitness). This information guides the bees to find the flower patches

precisely, without the use of guides or maps. Each individual's knowledge

of the outside environment is gleaned solely from the waggle dance. This

dance enables the colony to evaluate the relative merit of different patches

according to both the quality of the food they provide and the amount of

energy needed to harvest it.

To identify the optimal location of bio-mass power plant

(David et al 2010), Resource Allocation (Nicanor and Kevinl 2010),

Continuous Optimization Problem (Hai Bin et al 2010), Constraint

Optimization Problem (Dervis et al 2011), Economic power dispatch

(Rajesh et al 2011), data Clustering in data mining (Mohammad et al

2007) (Dervis and Celal 2011) (Changsheng et al 2010), and Path

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management in the computer network are some of the successful

solutions based on ABC algorithm. The detailed honey bee mating

algorithm is explained below.

4.2. RECENT RESEARCH IN ABC

The survey on ABC which includes the detailed problem

description, implementation and comparison with its counterpart is

described in Dervis and Bahriye (2009), Taher et al (2010), Dervis and

Celal (2011), Hongnian et al (2010). In which Hongnian et al (2010),

Dervis and Bahriye (2009) described the biological nature of honey bee

and its colony behaviour. And this work described the study of bionics

bridges with the engineering functions, biological structures of animals

and insects, and organizational principles found in the nature which

mapping with the modern technologies.

Michelle and Stephen (2005) explained the numerous

mathematical definitions and compared the implementation of ABC with

other exiting meta-heuristic algorithms. This work explains the

knowledge transferring process from the life forms to the human modern

technologies. The output of bionics study of ABC includes not only

physical products, whereas also various computation methods that can be

applied in different areas.

The authors reviewed the various nature-inspired algorithms

such as ACO, ABC, Genetic Algorithm (GA), and Fire-Flies (FF)

Algorithm and concluded that the nature-inspired algorithms could

hybridize together with other algorithms to enhance it to be faster, more

efficient, and more robust.

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The ABC algorithm was first proposed for unconstrained

optimization problems where it showed superior performance. Dervis and

Celal (2011) describes a modified ABC algorithm for constrained

optimization problems and compares the performance of the modified

ABC algorithm against those of state-of-the-art algorithms for a set of

constrained test problems.

consisting of three simple heuristic rules and a probabilistic selection

scheme for feasible solutions based on their fitness values and unviable

solutions based on their violation values. Structural optimization,

engineering design, economics and resource allocation are just a few

examples of fields for constrained optimization problems.

The authors implemented the ABC with various benchmark

functions and compared with Particle Swarm Optimization (PSO),

Differential Evaluation Algorithm, and GA. The author concluded that the

performance of ABC algorithm is better than the other algorithms even

though it uses less control parameters and it can be efficiently used for

solving multimodal and multidimensional optimization problems.

Jun Zhao et al (2011) further extended the ABC for parallel

computing problem; the parallel computing provides efficient solutions

for combinatorial optimization problem. Parallel computing can shorten

the time to give a solution. Therefore greater attention has been made by

the researchers towards parallel computation. However, the actual parallel

or distributed algorithm is generally based on the real devices of computer

cluster or multi-core processor.

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The ABC has already applied in various engineering problem

and established optimal performance than existing algorithms which

includes clustering in data mining (Changsheng et al 2010), Travelling

Salesman Problem (Peibo and Huaxi 2010), Economic power dispatch

(Rajesh et al 2011), resource allocation (Nicanor and Kevin 2010),

optimal location computation (David et al 2010) and for vehicular routing

(Yannis and Magdalene 2010).

Nicanor and Kevin (2010) illustrated the practical utility of the

theoretical results and algorithm of honey bee algorithm, and show that

how it can solve a dynamic voltage allocation problem to achieve a

maximum uniformly elevated temperature in an interconnected grid of

temperature zones. In Jiejin et al (2010), the authors proposed a novel

hybrid ABC and Quantum Evolutionary Algorithm for solving continuous

optimization problems. ABC is adopted to increase the local search

capacity as well as the randomness of the populations.

These implementations have been tested on several well-known

real datasets and compared with other popular heuristics algorithms such

as Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search

(TS), ACO and the recently proposed algorithms like improved PSO.

The computational simulations reveal very encouraging results

in terms of the quality of solution and the processing time required

Honey-bees are among the most closely studied social insets. Their

foraging behaviour, learning, memorizing and information sharing

characteristics have recently been one of the most interesting research

areas in swarm intelligence.

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Rajesh et al (2011) presented a new multi-agent based hybrid

particle swarm optimization technique applied to the economic power

dispatch. The earlier PSO suffers from tuning of variables, randomness

and uniqueness of solution. The algorithm integrates the deterministic

search, the Multi-agent system, the PSO algorithm and the bee decision-

making process. The economic power dispatch problem is a non-linear

constrained optimization problem.

Classical optimization techniques like direct search and

gradient methods fails to give the global optimum solution. Other

Evolutionary algorithms provide only a good enough solution. To show

the capability, the author has applied to two cases 13 and 40 generators,

respectively. The results show that this algorithm is more accurate and

robust in finding the global optimum than other.

David et al (2010) discussed a new calculation tool based on

particles swarm which named as Binary Honey Bee Foraging (BHBF).

Effectively, this approach has made it possible to determine the optimal

location, biomass supply area and power plant size that offer the best

profitability for investor. Moreover, it prevents the accurate method,

which may not be feasible from computational viewpoint. In this work,

Profitability Index (PI) is set as the fitness function for the BHBF

approach.

Changsheng (2011) proposed a clustering approach for

optimally partitioning of N objects into K clusters. The author tested the

proposed system with several well-known real datasets and concluded

that the ABC performs well than other popular heuristics algorithm in

clustering, such as GA, PSO, Scatter Search (SS), TS, and ACO. The

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result of all above proposals shows that the performance of honey bee

algorithm is optimal than other existing algorithms.

Alok (2009) and Michael et al (2010) applied the ABC in the

studies of computer science and engineering for network routing and

minimum spanning tree. Alok (2009) designed and implemented the ABC

for leaf-constrained minimum spanning tree problem and concluded that

computation time in the ABC is quite small and it completely

outperforms both in terms of solution quality as well as running time. The

author proposes ABC based solution for the given an undirected,

connected, weighted graph, the leaf-constrained minimum spanning tree

problem.

This work seeks on this graph a spanning tree of minimum

weight among all the spanning trees of the graph that have at least number

of leaves. This work differs from other implementations in the following

features: In existing implementation, if the solution associated with an

employed bee does not improve for a predetermined number of iterations

then it becomes a scout bee. Second possibility of a scout bee may be due

to collision.

There are no limits on the number of scouts in a single iteration

like other ABC algorithms. Also number of scouts depend on the above

two conditions. There can be many scouts in the iteration if these two

conditions are satisfied many times, or there can be no scout if these two

conditions remain unsatisfied.

Michael et al (2010) made a detailed review of bio-inspired

routing algorithm such that ABC and ACO. The author discusses in some

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depth why biology is an appealing and appropriate place to find

inspiration for computer networking research.

The work covers a review on routing research inspired by the

behaviour of social insects, intrusion and misbehavior detection research

inspired by the immune system, network services modeled on the

interactions and evolution of populations of organisms, research that

applies techniques from the field of epidemiology, and presents a

sampling of newly emerging bio-inspired research topics.

It is observed that the performance of ABC may be further

improved by 1) optimal value assignment for the constants, which was

assumed for almost all the previous work, and 2) the initial number of

scout bee, if this is not optimally selected then there are many chances for

local optimal (zero-to-infinity) problem.

4.3. PROPOSED ABC BASED WIRELESS CLUSTERING

The proposed Artificial Bee Colony Based Wireless Clustering

(ABCWC) requires a number of parameters to be set, namely:

1. number of scout bees (n),

2. number of elite bees (e),

3. number of patches selected out of n visited points (m),

4. number of bees recruited for patches visited by "elite bees"

(nep),

5. number of bees recruited for the other (m-e) selected patches

(nsp),

6. size of patches (ngh) and

7. stopping criterion.

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4.3.1 Electing Efficient Cluster Head using ABC Algorithm

The algorithm starts with the n scout bees being placed

randomly in the search space.

The bees search for food sources in a way that maximizes the

ratio, the energy function is shown in the following equation (4.1)

T

E)(F

(4.1)

Where, E is the energy obtained, and T is the time spent for

foraging. Here E is proportional to the nectar amount of food sources.

In a maximization problem, the goal is to find the maximum of

RP. R

P represents the region of search

area.

i is the position of the ith

i)

i and it is

i).

i(C) | i = 1, 2... S} represent the population of

food sources being visited by bees, in which, C is cycle, and S is number

of food sources around the hive. The preference of a food source by the

nectar amount of the food source increases, the probability with the

preferred source by the worker bee increases proportionally. Therefore,

i will be chosen by a bee

can be expressed in equation (4.2)

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s

1k

k

i

i

)(F

)(FP

(4.2)

The position of the selected neighbour food source is

calculated as the following equation (4.3) and (4.4),

)C()1C(ii (4.3)

and the stop criteria of the system is

thiiH)E(N)Q(N (4.4)

where,

Ni (Q) represents the values of nectar of Queen,

Ni (E) represents the values of nectar of Elite bee, and Hth

represents the minimum threshold value of the Hive.

At the end of iteration, the colony will have two parts to its

new population - representatives from each selected patch and other scout

bees assigned to conduct random searches.

4.3.2 Pseudo-code of ABC Algorithm

Initialization

Generate the initial population of the bees

Selection of the best bee as the queen

Selection of the maximum number of mating flights (n)

Main Phase

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do while i n

Initialize queen spermatheca, energy and speed.

do while energy > threshold and spermatheca is not full

Select a drone

if the drone passes the probabilistic condition then

Add sperm of the drone in the spermatheca

endif

Update Speed

Update Energy

enddo

do j = 1, Size of Spermatheca

Select a sperm from the spermatheca

Generate a brood by applying a crossover operator between the queen, the

selected drones and the adaptive memory

Select, randomly, a worker

if the brood then

Replace the queen with the brood

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Else

if

Replace the drone with the brood

endif

endif

enddo

enddo

return The Queen (Best Solution Found)

A sample wireless adhoc network is shown in the Fig.4.1. The

sample nectar (energy / battery power) of each node is shown in the

Fig.4.1 is listed in the Table 4.1. The energy function is applied to fig.4.1

based on the values shown in table 4.1, which is recorded in table 4.2.

As the first step, the hive generates 16 scout bees which is

simply a hello message in the network terminology. These bees fly which

is simply flooding of hello message, into the region of food source

(wireless network). These all scout bee access any one food source

(flower) and collect the nectar (energy).

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11

10

8

9

7

6

5 4

3

2

12

1

15

14

13

H

16

Fig.4.1 Sample Node Deployment

Table 4.1 Node Details of Figure 4.1

Flower (Node) Nectar (Energy)

1 90

2 75

3 80

4 40

5 50

6 60

7 90

8 75

9 80

10 40

11 76

12 60

13 45

14 75

15 80

16 40

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Table 4 i) of Figure 4.1

Node Energy IMP i)

1 90 4 23

2 75 3 25

3 80 4 20

4 40 5 8

5 50 4 13

6 60 3 20

7 90 3 30

8 75 4 19

9 80 5 16

10 40 4 20

11 76 3 25

12 60 2 30

13 45 2 23

14 75 1 75

15 80 1 80

16 40 1 40

At the 8th

time unit (refer the Table 4.2), the dance of

scout_bee_4 in the food_source_4 is elapsed, here for the simple

neighbouring bee, here scout_bee_1, 3, 5 in the food_source_1, 3, 5 is

dancing with the rhythm values of 23, 20 and 13 respectively, so the

scout_bee_4 enter into food_source_1. When the guest bee (scout_bee_4)

enters, the scout_bee_1 will update its guest nectar table, which is shown

in the Table 4.3.

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Table 4.3 Nectar Table of Scout_bee2

Food Source ID Nectar Ratio F

1 8

Then it flies to the hive with its own nectar (routing) table.

2nghb (4.5)

From the given example, at 23rd

time units as indication in

the table 4.4, all the neighbouring bees appeared in the dancing floor of

scout_bee2 with the value of (3,20),(5,13),(1,23),(4,8),(6,20), where the

first value indicates the food source id and the second value indicates the

nectar value of the concerned food source.

Now there are 5 bees appeared, so the scout_bee_2 is elected

as the elite bee of the patch (consider this as patch 1). The nectar table of

patch1 is shown in the Table 4.4.

Table 4.4 Nectar Table of Elite Bee in the Patch1

Food Source ID Nectar Ratio F

1 23

3 20

4 8

5 13

6 20

In the hive, elite bee from patch1, patch2 and patch3 is

reached, and then the Elite Bee and Patch Routing table of Hive is

formed, which is shown in the Tables 4.5 and 4.6 respectively.

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Table 4.5 Elite bee Table in the Hive

Food Source ID Patch ID

2 1

7 2

15 3

Table 4.6 Patch Routing Table in the Hive

Food Source ID Next Hop Patch ID

1 2 1

2 * 1

3 2 1

4 2 1

5 2 1

6 2 1

7 * 2

8 7 2

9 7 2

10 7 2

11 7 2

12 15 3

13 15 3

14 15 3

15 * 3

16 15 3

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In the

and the number indicates the id of the concern elite bee.

The bio-logical terms and its corresponding network

terminology are mapped in the Table 4.7.

Table 4.7 Mapping of Biological Terminology with

Network Terminology

Bio-logical terms Network Routing

Bee Hello message

Food Source (or Flower) Node

Nectar Energy / Power

Nectar (or Patch) Table Routing Table

Waggling Dance Waiting Time

Elite Site Cluster Head

Hive Control Station (Real /Imaginary Node)

4.4 RESULTS AND PERFORMANCE ANALYSIS

Figure 4.2 Design of Type1 Wireless Network

WL1 WL2

WL5 WL6

WL8 WL7

WL4 WL3

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The routing protocol defined in the implementation (shown in

the tables and in the figures) is the advancement of the latest proposal

over the traditional routing protocol, AODV (Yang et al 2011). The

average response time which shown in the tables 4.9 and fig.4.3 are the

combination of route discovery time, transmission time, propagation

delay in each node, and waiting time in the intermediate queue.

The proposed ABCWC is implemented in Network Simulator

2 (NS2). In the huge wireless routing, the AODV is prominent routing

protocol (Kalwar, 2010) which modified by many researchers in the past

few decades. In which, the neighbour detection for AODV (Krco et al,

2003), improving efficiency of AODV, combination of AODV with DSR

(Bai et al, 2006), secured AODV (Cerri et al, 2008), dynamic anomaly

detection (Nakayama et al, 2009), Route Recovery mechanism (Pereira et

al, 2010) are remarkable work. The proposed work is compared with

recent AODV routing in cluster environment which is proposed by

Pereira et al (2010).

The performance is tested in a variety of nodes on wireless

network, and using various transport protocol on UDP. Figure 4.2 shows

the design of type 1 wireless network and Table 4.8 shows the various

types of wireless network used for the simulations. The simulation is

implemented for 10 seconds. The throughput, response time and packet

loss are calculated for entire 10 seconds and the mean value of each

calculation is shown in the following Table 4.9, 4.10, 4.11.

The average response time shown in the Table 4.9 and fig.4.3

are the combination of route discovery time, transmission time,

propagation delay in each node, and waiting time in the intermediate

queue. The average response time and throughput in wireless

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environment are recorded in the Table 4.9 and 4.10. The average response

time and the throughput of proposed work are improved in the present

work. This causes improvement in the throughput of proposed ABCWC,

which reflects in the Table 4.10.

Table 4.8 Design Details of Wireless Network

Type of

network

No of

nodes

No of

cluster

No of Nodes in

each cluster

No of mobile

Nodes

Type 1 8 2 4 6

Type 2 20 4 5 18

Type 3 50 10 5 27

Type 4 75 5 15 24

Type 5 100 5 20 24

Type 6 200 10 20 27

Table 4.9 RTT in Wireless Routing

No of nodes No of cluster No of Nodes in each

cluster AODV

Proposed

ABCWC

10 2 5 178 168

20 4 5 196 178

40 8 5 213 189

100 20 5 242 214

200 20 10 288 254

500 25 20 345 297

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Table 4.10 Throughput in Wireless Routing

No of nodes AODV Proposed ABCWC

10 187.2 193.5

20 198.7 206.4

40 212.1 229.9

100 278.2 294.2

200 389.4 411.9

500 467.9 496.7

Table 4.11 Packet Loss of proposed and existing routing protocols

No of Nodes AODV Proposed ABCWC

10 6 1

20 12 2

40 19 4

100 28 6

200 37 16

500 49 27

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Fig.4.3 Comparison of Round Trip Time

Fig.4.4 Comparison of Throughput

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Fig.4.5 Comparison of Packet Loss

Fig.4.6 Packet Loss in proposed work with trend line

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Fig.4.7 Throughput of proposed work with trend line

Fig.4.8 Round Trip Time in proposed work with trend line

Fig4.3 seems the performance of proposed ABCWC is

improved one than the existing AODV as a minimum of 6% and as a

maximum of 14% in RTT. The performance of proposed ABCWC is

improved one than existing AODV as a minimum of 3% and as a

maximum of 8% in Throughput.

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Fig4.4 seems the throughput of ABCWC is improved around

5kbps than the existing routing protocol (Pereira et al, 2010) and a

consequence the proposed work reduces the average response time (from

2ms to 3ms). As the result of reduced response time the number of packet

travelled in a unit time is increased.

There are no packet losses in the proposed work at lesser no.

of nodes and packet losses on higher no. of nodes are reduced

dramatically then existing AODV protocols.

The proposed ABCWC reduces response time in UDP. The

average response time shows transmission rate of the network, which

leads to the number of packet travelled in a unit time, is increased. This

efficient data transfer is visualized in the throughput. Therefore, the

proposed ABCWC provides efficient data transmission on wireless

network, hence it is concluded that ABCWC is an efficient routing

protocol than existing system.