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FLAR: AN ADAPTIVE FUZZYROUTING ALGORITHM FORCOMMUNICATIONS NETWORKSUSING MOBILE ANTSSeyed Javad Mirabed ini

a, Moham mad Teshnehl ab

b, Moham mad Hassan Shenasa

b& Amir Masoud

Rahmania

a Islamic Azad University, Science and ResearchBranch, Tehran, Iranb

Elec t ri cal Eng. K. N. Tossi Univ ersit y, Tehr an, IranVersion of record f irst published: 26 Aug 2008.

To cite this art icle: Seyed Javad Mirabed ini , Mohamm ad Teshnehl ab , Mohamm adHassan Shenasa & Amir Masoud Rahmani (2008): FLAR: AN ADAPTIVE FUZZY ROUTING

ALGORITHM FOR COMMUNICATIONS NETWORKS USING MOBILE ANTS, Cybernet ics andSyst ems: An Inter nat ional Journal, 39:7, 686-704

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FLAR: AN ADAPTIVE FUZZY ROUTING

ALGORITHM FOR COMMUNICATIONS

NETWORKS USING MOBILE ANTS

SEYED JAVAD MIRABEDINI1, MOHAMMADTESHNEHLAB2, MOHAMMAD HASSAN SHENASA2,

and AMIR MASOUD RAHMANI1

1Islamic Azad University, Science and Research Branch,

Tehran, Iran2Electrical Eng. K. N. Tossi University, Tehran, Iran

Swarm intelligence, as demonstrated by a natural biological swarm,such as an ant colony, has many powerful properties that are desir-

able for effective routing in communications networks. In this paper,

we propose an intelligent routing algorithm that we are calling Fuzzy

Logic Ant-based Routing (FLAR), which is inspired by ant colonies

and enhanced by fuzzy logic techniques. Using a fuzzy system as an

intelligent and expert mechanism allows multiple constraints to be

considered in a simple and intuitive way. Simulation results and a

comparison of the proposed method with two state-of-the-art rout-

ing algorithms show better performance and a higher fault tolerance

for our approach, particularly in regard to link failures.

INTRODUCTION

Modern communication networks are becoming increasingly diverse and

heterogeneous. This is the consequence of the addition of an increasing

array of devices and services, both wired and wireless. The need for

seamless interaction of numerous heterogeneous network components

Address correspondence to Engineering Campus, Islamic Azad University, Science

and Research Branch, Toward Hesarak, Ashrafi Esfehari Expressway, Poonak Square,

Tehran, Iran. E-mail: [email protected]

Cybernetics and Systems: An International Journal, 39: 686704

Copyright Q 2008 Taylor & Francis Group, LLC

ISSN: 0196-9722 print=1087-6553 online

DOI: 10.1080/01969720802257915



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represents a formidable challenge, especially for networks that have tra-

ditionally used centralized methods of network control. A network is

said to have centralized control when one node handles all the decisions.In such a system there is a clear leader, and the assumption is that it can

make impartial and coordinated decisions. Problems arise when the

network is geographically distributed and the central node has to make

decisions utilizing incomplete and possibly out-dated knowledge. A link

failure could also cause the isolation of a part of the network, and if the

central node failed, the whole network could become inoperable. At the

other extreme, in a decentralized network each node makes all of its own

decisions. As a whole, the Internet runs on this basis. But decentralizedalgorithms have also oscillations and stability problems. Current routing

algorithms are inadequate to handle the increasing complexity of such

networks. Routing algorithms in modern networks must address numer-

ous problems. Two of the usual performance metrics of a network are

average throughput and delay. The interaction between routing and

flow control affects how well these metrics are jointly optimized

(Tannenbaum 2003; Barabaasi 2003; Park 2003; Spencer 2002).

Swarm intelligence routing provides a promising alternative to these

approaches. Swarm intelligence utilizes mobile software agents for net-

work management. These agents are autonomous entities, both proactive

and reactive, and have the ability to adapt, cooperate, and move intelli-

gently from one location to the other in the communication network.

Swarm intelligence, in particular, uses stigmergy (i.e., communication

through the environment) for agent interaction. Swarm intelligence

exhibits emergent behavior, wherein simple interactions of autonomous

agents, with simple primitives, give rise to a complex behavior that has

not been explicitly specified. Swarm intelligence boasts a number ofadvantages due to the use of mobile agents and stigmergy.

1. Scalability: population of the agents can be adapted according to the

network size. Scalability is also promoted by local and distributed

agent interactions.

2. Fault tolerance: swarm intelligent processes do not rely on a centra-

lized control mechanism. Therefore, the loss of a few nodes or

links does not result in a catastrophic failure, but leads to scalabledegradation.

3. Adaptation: agents can change, die, or reproduce, according to

network changes.

ANTS, FUZZY LOGIC, AND COMMUNICATION NETWORKS 687



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4. Speed: changes in the network can be propagated very fast, in

contrast with the Bellman-Ford algorithm.

5. Modularity: agents act independently of other network layers.6. Autonomy: little or no human supervision is required.

7. Parallelism: an agents operations are inherently parallel.

These properties make swarm intelligence very attractive for routing

in communication networks, quality of service routing for next-

generation high-speed networks, etc. They also render swarm intelli-

gence suitable for a variety of other applications, apart from routing,

such as robotics and optimization (Bonabeau 1999). In the next section,we give an overview of an ant-based routing algorithm. A general defi-

nition about fuzzy logic is presented in section three; a discussion of

our proposed method and its attractive features appears in section four;

in section five simulation is given; in section six, we present results and

discussion; and in section seven we conclude the paper.

ANT-BASED ROUTING ALGORITHM

In the ant-based routing algorithm such as the AntNet system, routing isdetermined through complex interactions of network exploration agents,

called ants. These agents are divided into two classes: the forward ants

and the backward ants. The idea behind this subdivision is to allow the

backward ants to utilize the useful information gathered by the forward

ants on their trip from source to destination. Based on this principle, no

node routing updates are performed by the forward ants, whose only pur-

pose in life is to report network delay conditions to the backward ants.

This information appears in the form of trip times between each networknode. The backward ants inherit this raw data and use it to update the

routing tables of the nodes. A typical routing table is shown in Table 1.

Table 1. Ant-based routing table for node A

Neighbor node

Node A B C

Destination

E 0.35 0.65

F 0.40 0.60

688 S. J. MIRABEDINI ET AL.



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The entries on the routing table are probabilities, and as such, they addup to a sum of one for each horizontal row.

In Table 1, the probability value Pdn expresses the probability of

choosing n as a neighbor node when the destination node is d, with

the constraint defined in Eq. (1) :Xn2Nk

Pdn 1; d 2 1; N; Nk fneighbors of kg: 1

These probabilities serve a dual purpose. The exploration agents ofthe networkthe antsuse them to randomly decide the next hop to a

destination. When forward ants revisit a node, the circuit that they have

possibly traveled in is cleared from their memory to avoid reinforcing cir-

cular routes. To attempt to provide a faster feedback mechanism, back-

ward ants have priority over all other packets. A common criticism of

this system is that a faster feedback mechanism would be to design forward

ants to update the routing tables of nodes with regard to the section of the

trip that they already completed. An essential feature of the ant metaheur-

istic is that the reinforcement from poor routes must be delayed propor-

tionally. However, the actual network traffic uses them deterministically,

choosing as the next route the one with the highest probability. In AntNet,

in addition to the routing table, each node also possesses a table with

records of the mean (l)andvariance(r) of the trip time to every destination

(see Table 2). The detailed information about different versions of AntNet

algorithms can be found in Di Caro (1998af) and Dorigo (1999).

FUZZY LOGIC

Fuzzy logic is a superset of conventional logic that has been extended to

handle the concept of partial truth. It was first introduced by L. Zadeh in

Table 2. Trip time table for node A

Neighbor node

B C

Node A l r l r

Destination

E 0.73 0.01 0.58 0.09

F 0.86 0.03 0.41 0.04




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the 1960s as a means to model the uncertainty of natural language, and it

has been widely used for supporting intelligent systems. A key feature of

fuzzy logic is that it handles uncertainties and nonlinearitys found inphysical systems, similarly to the reasoning conducted by human beings,

which makes it very attractive for decision making systems. A fuzzy logic

system comprises basically three elements: A fuzzifier, an inference

method (rules and reasoning), and a defuzzifier. Fuzzy systems are used

to approximate functions, as well as to model any continuous systems.

Figure 1 shows the generalized block diagram of fuzzy system. Some

advantages of fuzzy logic are:

. conceptually easy to understand

. flexible

. tolerant of imprecise data

. can model nonlinear functions of arbitrary complexity

. can be built on top of the experience of experts

. can be blended with conventional control techniques

. based on natural language

The quality of fuzzy approximation depends on the quality of the rules.The result always approximates some unknown nonlinear function that can

Figure 1. Generalized fuzzy system.




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change in time. Fuzzy systems theory or fuzzy logic is a linguistic theory

that models how we reason with vague rules-of-thumb and common sense

(Ghosh 1998). The basic unit of fuzzy function approximation is If-thenrules. A fuzzy system is a set of If-then rules that maps input to output.

The steps involved in the fuzzy inference system design are as follows:

Step 1: Fuzzy Inputs

This step will obtain inputs and normalize them in the range of 0, 1,

then determine the degree to which they belong to each of the appro-

priate fuzzy sets via membership functions. Fuzzification of the input

amounts to either a table lookup or a function evaluation.Step 2: Apply Fuzzy Operator

This step determines the degree to which each part of the antecedent

has been satisfied for each rule. If the antecedent of a given rule

has more than one part, the fuzzy operator is applied to obtain one

number that represents the result of the antecedent for that rule. This

number will then be applied to the output function. The input to the

fuzzy operator is two or more membership values from fuzzified input

variables. The method used may be eitherANDorOR operation, and

the output is a single truth value.

Step 3: Apply Implication Method

Before applying implication proper weights are assigned to each rule.

The input for the implication process is a single number given by the

antecedent, and the output is a fuzzy set.

Step 4: Aggregate All Outputs

Aggregation is the process by which the fuzzy sets that represent the

outputs of each rule are combined into a single fuzzy set. Aggregation

only occurs once for each output variable, prior to the fifth and finalstep, which is defuzzification. The input of the aggregation process

is the list of truncated output functions returned by the implication

process for each rule. The output of the aggregation process is one

fuzzy set for each output variable.

Step 5: Defuzzify

The input for the defuzzification process is a fuzzy set and the output

is a single number. The aggregate of a fuzzy set encompasses a range

of output values, and so must be defuzzified in order to resolve a singleoutput value from the set. Finally, the output is denormalized and

is given as the result (Phillis 1999; Zhang 1998, 1999; Mirabedini

2002, 2004).




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FUZZY LOGIC ANT-BASED ROUTING

Recent advances in fuzzy logic in the optimization of an ant colony

system, telecommunications networks, admission control, the flow

control problems, fuzzy control of queuing systems with heterogeneous

servers, scheduling in simple series parallel networks using fuzzy logic,

and fuzzy routing in connectionless networks can be found in Di Caro

(1998df), Phillis (1999), and Zhang (19982001). In this paper, our

novel FLAR approach is presented. FLAR is constructed with the

communication model observed in ant colonies and in fuzzy logic tech-

nique. In this section we will describe the fuzzy inference system (FIS)

designed for FLAR, and then explain the FLAR Algorithm in detail.

Fuzzy Inference System (FIS)

The FIS for FLAR is a mamdani type system with two inputs and one

output. The system inputs are route (or link) delay and route utilization.

The utilization indicates the amount of used buffer capacity for every

selected route in a path. Both inputs are characterized by the fuzzy mem-

bership functions as shown in Figures 2 and 3. The membership func-tions for the fuzzy sets of inputs are chosen to be triangular. Both

inputs are normalized between 0 and 1 before applying them to FIS.

As shown in Figures 2 and 3, both input variables (route delay and

utilization) have five membership functions, which are entitled VL, L,

M, H, and VH, which stand for very low, low, medium, high, and very high

respectively (Mirabedini 2007).

Figure 2. Membership functions for first input variable route delay (X1).




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The rules of the FIS are designed for optimal performance. Table 3

shows the rule base for the FIS. In this table, the values for the amount of

goodness from lowest to highest are defined as LL (very low), LM (low

medium), LH (low high), ML (medium low), MM (medium), MH (medium

high), HL (high low), HM (high medium), and HH (very high).

There are 25 rules defined for this fuzzy system. For example, two of

the rules are these:

R1: If route delay is VL and route utilization is VL, then congestion rate

is LL.

. . .

R25: If route delay is VH and route utilization is VH, then congestion rate

is HH.

The output of FIS which is a route goodness is applied to the

software simulation for evaluations. Design of FIS is the process offormulating the mapping from a given input to its output using fuzzy

Table 3. Fuzzy rule base

Route utilization (%)

Congestion rate VL L M H VH

Route delay (ms) VL LL LM LH ML MM

L LM LH ML MM MH

M LH ML MM MH HL

H ML MM MH HL HM

VH MM MH HL HM HH

Figure 3. Membership functions for second input variable route utilization (X2).




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logic. Mamdani-type inference expects the output membership functions

to be fuzzy sets. After the aggregation process, there is a fuzzy set for out-

put variable as shown in Figure 4. All of the membership functions for

the fuzzy sets of inputs and output are chosen to be triangular for its

easiness in computation, clarity, and noise tolerance (Zhang 2001;

Mirabedini 2007).

The output variable Y has nine membership functions named LL,

LM, LH, ML, MM, MH, HL, HM, HH to indicate low low, low medium,

low high, medium low, medium medium, medium high, high low, high

medium, and high high correspondingly. The fuzzy operator used for

the AND method in if-then rules, such as, If A is a AND B is b, then C

is c is multiplication. The method used for the defuzzification is mean

of centers. The defuzzification is the process of the conversion of a fuzzy

output set into a single number (Mirabedini 2002, 2004). Then, the

output of the fuzzy system is denormalized and applied to the FLAR

algorithm as the criterion for updating the routing table.

The Proposed Algorithm (FLAR)

In this section we describe our novel FLAR algorithm in detail. FLAR is

constructed with the communication model observed in ant colonies,

which is then enhanced by fuzzy logic technique. The sequence of FLAR

algorithm is outlined as follows:

1. Each source node launches forward ants to destinations at regular

time intervals.2. The forward ants find a path to the destination randomly based on

the current routing tables, but the data packets choose the path to

the destination with the highest probability.

Figure 4. Membership functions for output variable Y (Congestion Rate).




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3. Each forward ant creates a stack, pushing in delay time and amount

of buffer utilization for every traversed route (or link) to a node.

The delay can be the sum of the time spent waiting in queue andthe transmission time for each visited node n.

4. When the destination is reached the backward ants inherit the stack.

5. The backward ant pops the stack entries, including delay time and

utilization amount, and takes in the path in reverse to update to

the routing tables of visited nodes.

The total delay of a path is defined as the sum of all delays of the inter-

mediate routes from the current node n to the destination node d via aneighboring node j Eq. (2).

Dnj;d Xsi1

delayi 2

where s is the total number of routes (or links) in the path traversed by

forward ant. The utilization of each buffer on the path is calculated as in

Eq. (3). Each node has an incoming packet buffer with a maximum

capacity of Q. The sum of these utilization measures is taken and usedto generate a weighting measure kui for each buffer i as in Eqs. (4) and

(5). Finally, the estimated path utilization Unj;d from the current node

n to the destination node d via the neighboring node j, is calculated by

multiplying the number of packets in each buffer by its corresponding

weight factor kui, which is shown in Eq. (6).

ui qi

Q; i 1; . . . ;s 3

B Xsi1

ui 4

kui ui

B; i 1; . . . ;s 5

Unj;d Xsi1

kui ui 6

where qi is used as the queue buffer, Q is the maximum capacity of an

incoming packet buffer for each node i, and s is the total number of

nodes traversed by the forward ant.




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6. Using the calculated values pair of Dnj;d and Un

j;d as crisp inputs, we

determine the congestion route for each eligible path via fuzzification

(based on the membership functions shown in Figures 24), fuzzyinference (based on the rule shown in Table 1 and the mamdani impli-

cation), and defuzzification (based on the centers mean method). The

Path congestionnj;d, the amount of the congestion rate to go from a

current node n to a destination node d via a neighboring node j, is

expressed in Eq. (7).

Path congestionn

j;d P

Ml1 Y

llAl

D lAl

U PMl1 lAlD

lAlU

7

where the parameters are:

i: the node an ant is coming from

j: the node where an ant wants to move

M: the number of fuzzy rule bases used (M 25)

Yl: the mean value of each membership function in fuzzy set

lAlD

: the amount of membership functions for delay

lAlU

: the amount of membership functions for path utilization.

Then, the output of the fuzzy system (Path congestionnj;d) is applied

to the FLAR algorithm, which can be used as a criterion for updating

routing tables for each visited node.

7. The new estimation of Path congestionnj;d is computed as expressed in

Eq. (8).

Path congestionnj;dt 1 qPath congestionn

j;dt 1

qPath congestionnj;d: 8

where q is the learning factor that is set to 0.15 in this experiment.

Finally, the routing table probabilities of each traversed node are

updated by Eq. (9) on the basis of the Path congestionnj;d.

Pnj;dt

1Path congestionn

j;dt

P

l2Neighborsofn1

Path congestionnl;d

t

h i 9




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SIMULATION PROCESS

Simulation is designed and implemented in object-oriented language

C to run on a Pentium IV computer. This simulation is used to test

three different routing algorithms of the communication networks. In

this simulation, a network topology model with 17 nodes and 45 bidirec-

tional links are used (see Figure 5). Every link has two specifications:

delay (ms) and bandwidth (mbps). which are indicated in pairs. Nodes

1, 2, and 3 are sources of packet traffic generations, and nodes 15, 16,

and 17 are destinations. The traffic sources are constant bit rate

(CBR), sending 33 UDP (user data packet) packets per second. Each

packet length is 512 bytes and the total simulation time is 30 seconds.Each link has two specifications: delay (ms) and bandwidth (mbps),

which are shown in pairs.

Each node has an incoming packet buffer with a maximum capacity

of 1024. Nodes 1, 2, and 3 act as both traffic-generating nodes and

switching nodes. The other nodes are pure switching nodes. A traffic

route should be determined before a traffic flow is going to be sent off

at its generating node, and the route will be determined according to

the routing tables of nodes.

RESULTS AND DISCUSSION

The problem is to determine the optimal routing policy for each traffic

flow at its generating node based on the state of the system. During

the experiments, all the network situations are considered the same for

Figure 5. Network topology model. Note: There are 17 nodes and 45 bi-directional links.




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the three routing algorithms. The total number of packets generated from

different sources for distinctive destinations are 3000. There are two

strategies considered in the simulation. In the first strategy, there is nochange or link failure in the network topology. In the second strategy,

some of the links ([5, 6], [10, 15], and [12, 17]) are changed to down (fail)

between time 9 and 18 seconds of simulation; at which time they become

UP (repaired) until the end of the simulation time. In our experiments,

we adjusted the parameters of AntNet2.0 as defined in Di Caro

(1998d). The performance of the proposed method (FLAR, Fuzzy logic

ant-based routing) with OSPF (Open shortest path first) and AneNet2.0

is evaluated according to the above strategies with the following metrics.End-to-end delay: delay incurred by a packet being transmitted

between a source and a destination node. Figures 6 and 7 display end-

to-end delay or packet latency time for both strategies, respectively.

As we can see in Figures 7 and 8, the end-to-end delay diagrams for

the three competing algorithms in both strategies (no failure and link

failure states) are drawn, respectively. The horizontal axis determines

the packet number which is properly delivered to the destination, and

the vertical axis indicates the delay time for a packet to reach the desti-

nation node from its source node. From these diagrams it can be inferred

that the FLAR is more successful in transmitting the packets to their des-

tinations. In Figure 6 the diagram shows that the packets sent by FLAR

have less delay compared with the other methods. The delay of packets

Figure 6. End-to-end delay for first strategy (no failure state).




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in the OSPF increases, while the packets sent by the AntNet2.0 have less

delay than the OSPF. Specifically, as shown in Figure 7, in the state of

link failures between 9 to 18 seconds of simulation time, the FLAR

method routs the mechanism in a smooth manner, while the OSPF shows

a sudden increment in the packet transmission time. The performance of

the AntNet2.0 in this metric is less than FLAR, but it behaves better than

OSPF.

. Throughput: the fraction of packets sent by a source node that arrive at

the destination node. Figures 8 and 9 show the comparison of the

delivery rate between the three algorithms in the above strategies.

Figure 8 represents the throughput diagram for first strategy (no fail-

ure state) during the total simulation time. During rgw thirty secondsof simulation run, note that in the beginning of the simulation the

FLAR ant AntNet2.0 is almost the same in throughput metric until

5 seconds elapse. When the number of packets, however, increase

throughout the network, the performance of the FLAR is specifically

enhanced in contrast to its competitor AntNet2.0, but the third rout-

ing method OSPF remains in third place, because it cannot modify

the routing tables when congestion arises in the determined paths.

Figure 9 shows the throughput diagram for second strategy (failurestate). In this figure, as the simulation starts the throughput of FLAR

and AntNet2.0 are almost identical. Not until the ninth second of the

simulation does the network topology change and some links fail

Figure 7. End-to-end delay for second strategy (link failure state).




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(links [5, 6], [10, 15], and [12, 17]. In this case the throughput of all

three methods decrease in the period of between 9 and 18 seconds of

simulation run, but among them, the decrement of FLAR through-

put is the least, whereas AntNet2.0 behaves better than OSPF in

the transmission of packet in state of links failure. OSPF is the worst

in packet transmission. In the eighteenth second of simulation, all

failed links are recovered to work correctly, and the routing tables

of the correspondent nodes are updated to find a better path on

which to route the packets. Then, the throughput of the routing algo-

rithms is increased, but as it is seen in Figure 9, in comparison to the

competing methods, the throughput amount of FLAR is quickly

increased and the superiority of FLAR among others continued to

the end of the simulation time.

Figure 9. Throughput for second strategy (link failure state).

Figure 8. Throughput for first strategy (no failure state).




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. Dropped packets: the dropped packets are data packets that are

dropped during the routing process, because the buffer of the node

is full, or the life time of a packet is expired.. Overhead: involves the number of packets (request or ant) that are

needed to maintain or control the network. Note that the number of

control packets (ants) in FLAR are not more than the conventional

routing methods (i.e, OSPF), because updating the routing tables is

done by ants in interval times, and there is no necessity to have global

updating mechanism, such as flooding the routing tables, among all

nodes which are used in OSPF.

The experimental results for thirty different simulations with three

levels of traffic load (ten simulations for low traffic load, ten simulations

for medium traffic load, and ten simulations for high traffic load) in both

strategies are summarized in Table 4. The failures of links are chosen

stochastically for a second strategy in these simulations. Every traffic

generator source produces 30 packets per second as a low traffic load,

60 packets per second as a medium traffic load, and 90 packets per

second as a high traffic load. In 30 simulations, all the competing algo-

rithms were executed in the same situation. As is shown in Table 4, the

metrics evaluated are: average end-to-end delay, average throughput,

packet drop ratio, and the amount of overhead.

Thus table represents the simulation results for three the competing

algorithms: OSPF, AntNet2.0, and our novel approach FLAR. There are

Table 4. Experimental results obtained from 30 simulations of the three competing

algorithms (S1 and S2 show first and second strategies respectively)

Routing algorithms

Standard criteria OSPF AntNet2.0 FLAR

Avg. end-to-end delay (ms) S1 32.20 19.28 12.3

S2 35.90 20.96 13.6

Avg. throughput (packet=sec) S1 81 225 332.2

S2 71.60 215.60 313.4

Packet drop ratio (%

) S1 7.0 4.4 3.8S2 9.6 6.0 4.8

Overhead (%) S1 6 6 6

S2 10 10 10




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two strategies called S1 and S2 to show no failure states and link failure

states in the network topology, respectively. The first row in the table

shows the average end-to-end delay of competing methods measured inmilliseconds. We can see that the average end-to-end delays of FLAR

in both strategies S1 and S2 (12.3, 13.6) are the smallest, but these values

for the AntNet2.0 (19.28, 20.96) are better than for OSPF (32.20, 35.90).

This is because of the adaptability of ant-based algorithms such as

AntNet2.0 and FLAR in a traffic-congestion situation. The average

throughput obtained in this simulation for FLAR in strategies S1 and

S2 (332.2, 313.4) represents its effective role in transmitting the packets

from their sources to their destinations. The other two algorithms,AntNet2.0 and OSPF, have fewer throughputs and the values for OSPF

are the least. Measuring the amount of packet drop rate for both strate-

gies (S1 and S2), the results are 3.8% and 4.8% for FLAR, 4.4% and

6.0% for AntNet2.0, and 7.0% and 9.6% for OSPF. These values display

indicate FLAR does a better job transmitting packets through the net-

work. The last row in the table represents the percentage of control pack-

ets (overhead) for all competing methods. As exhibited in the table, these

values are considered the same in all experiments. Although the OSPF

does not use agents for routing, but for sending, and receiving hello

packets such as RREQ (Route REQuest) and RREP (Route REPly) in

order to gather information about network environment are as many

as moving agents used in AntNet2.0 and FLAR. In summary, consider-

ing the above experiments, it is obvious that the FLAR approach outper-

forms the competing algorithms in all evaluations terms. Thats because

of its ability to consider different constraints such as route delays and

route utilizations, for decision making in packet routing by applying

fuzzy technique in a simple and intuitive way, along with our ant-basedrouting method.

CONCLUSION

We have proposed a routing algorithm based on ant colonies and

enhanced by fuzzy logic for network routing. The advantages of such

an intelligent algorithm (FLAR) include increased flexibility in the con-

straints that can be considered in making an efficient routing decision, aswell as the simplicity in taking into account multiple constraints. The

computational load of a fuzzy control routing system is not very great,

so it can be used to apply the knowledge of an expert to a system. This




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is mainly due to the simple if-then structure of the rule base. The design

and implementation of our novel approach accompanied by AntNet2.0

and OSPF were presented and tested on more than 30 network simula-tions with three levels of traffic load. The experimental results favor

the FLAR. In addition, FLAR displayed better performance than its

competitors for all considered metrics, especially in regard to state of

link failures. The results of this research indicate an encouraging future

for developing fuzzy logic ant based routing in the world of communi-

cation network routing. including mobile ad hoc networks.

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