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CHAPTER 2

LITERATURE SURVEY

2.1 SURVEY ON NETWORK ROUTING

In essence, the proposed work paying attention to provide

optimal routing algorithm for Ad Hoc network. The mathematical model

for routing is as follows:

Le

visits each city once, which is similar to Traveling Salesman Problem

(TSP).

-ordinates (xr,

yr

becomes an Asymmetric TSP. Choosing a single feasible solution is

called a single path while choosing all possible feasible solution is called

a multi-path. In which, the multi path routing avoids traffic and helps to

improves the network efficiency.

Dijkstra-old-touch-first with multipath routing extension is an

computing all lexicographic- lightest paths from a source to every other

node in the network, but it requires additional computational efforts.

Open Shortest Path First (OSPF) version 2 (Moy 1998) and OSPF

optimized multi-path (Villamizar 1998) are some of the extended version

21

of traditional OSPF. Mishra and Sahoo (2007) proposed S-OSPF, which

is an improved version of OSPF for best effort networks.

Previous routing protocols are identifying the optimum path

based on a single network metric, which may be number of hops, shortest

distance or shortest time. Also Zero-to-infinity in Distance Vector (DV)

and transient loops in Link State (LS) are still an issue (Pierre and Olivier

2007), which may lead heavy congestion and packet loss. Also previous

routing protocols are identifying the optimum path based on mathematical

parameters such as number of hops or shortest distance, and also it

requires more computational efforts (YaSungoh and Ness 2009).

There are variety of wireless routing protocols such as

Dynamic Source Routing (DSR), Destination Sequenced Distance Vector

(DSDV), Adhoc On-demand Distance Vector (AODV), Wireless Routing

Protocol (WRP), Cluster-head Gateway Source Routing (CGSR), Source

Tree Adaptive Routing (STAR), Optimized Link State Routing (OLSR),

Flow Oriented Routing (FSR), Hierarchical State Routing (HSR),

Associativity Based Routing (ABR), and Signal Stability based Adaptive

Routing (SSAR) are proposed in the last few decades. In addition to these

routing protocols, stochastic routing also proposed for wireless networks

(Lott and Teneketzis, 2006).

The extended hybrid version of AODV and DSR, called DOA

(DSR over AODV) is proposed (Rendong and Mukesh 2006) as DSR for

inter-segment and AODV for intra-segment routing for improving packet

delivery ratio. However, it requires more control overhead and

complexity when implementing in the real time.

22

Energy efficiency is a major factor for wireless Ad Hoc

networks, which attracts many researches in the past few decades. For

example, Song et al (2004), Madan et al (2009) and Li et al (2011). In

which, Madan et al (2009) proposed decentralized cooperative routing for

wireless networks. The Song et al (2004) proposed on demand routing

protocol for Ad Hoc networks. Li et al (2011) proposed energy efficient

routing using Ant Colony Optimization algorithm. Natsheh and Buragga

(2010) proposed density based routing algorithm for spare topologies in

wireless mobile networks.

The selection of the routing paths is a major design

consideration (Siva and Manoj 2000) that has a drastic effect on the

resulting performance. Existing routing protocol is not optimal for both

wired and wireless environments. To overcome the above disadvantages,

this thesis proposed, Artificial Bee Colony (ABC) based clustering and Ant

Colony Optimization (ACO) based routing methodology. Table 2.1 shows

the survey on wireless networks.

Table 2.1 Survey Table on Wireless Networks

S.No Year Author Name Methodology

1 1998 Andrew Tanenbaum, S Computer Networks

2 1998 Moy, J. OSPF Version 2

3 2000 Larry, L.P. and Bruce, S.D. Computer Networks A

Systems approach

4 2003 Krco, S. and M. Dupcinov.

Improved neighbor detection

algorithm for AODV routing

protocol

5 2004 Manoj, B.S. and C.S.R.

Murthy,

Ad Hoc Wireless Networks:

Architectures and Protocols

23


6 2004

Song, J.H., W. Vincent, S.

Wong and V.C.M. Leung,.

2004

Efficient on-demand routing

for mobile Ad Hoc wireless

access networks

7 2005

Cavalcanti, D., Agrawal,

D., Cordeiro, C., Bin, X.

and Kumar, A

Issues in integrating cellular

networks WLANs, AND

MANETs: a futuristic

heterogeneous wireless

network

8 2005 Lee, S. and Knignt, D. Realization of Next

Generation Network

9 2005 Osama, H.H., Tarek, N.S.

and Myung, J.L.

Probability Routing

Algorithm for Mobile Ad

Management

10 2006 Bai, R. and M. Sighal

DOA: DSR over AODV

Routing for Mobile Ad Hoc

Networks

11 2006 Forouzan Data Communications and

Networking

12 2006 ISRD Group, Data Communication and

Computer Networks

13 2006 Lott, C and Teneketzis, D. Stochastic routing in Ad Hoc

networks

14 2006 Maria, P.G. and Daniel,

S.K.

Forecasting System

Imbalance Volumes in

Competitive Electricity

Markets

15 2006 Rendong, B. and Mukesh,

S.

DOA: DSR over AODV

Routing for Mobile Ad Hoc

Networks

16 2007

Alfawaer, Z.M., G.W. Hua,

M.Y. Abdullah and I.D.

Mamady,

Power Minimization

Algorithm in Wireless Ad

Hoc Networks Based on PSO

24


17 2007 Mishra, A.K. and Sahoo, A.

S-OSPF: A Traffic

Engineering Solution for

OSPF Based Best Effort

Networks

18 2007 Pierre, F. and Olivier, B.

Avoiding Transient Loops

during the Convergence of

Link-State Routing Protocols

19 2008 Cerri, D. and A. Ghioni

Securing AODV: the A-

SAODV secure routing

prototype

20 2008 Bin Xie, Kumar, A. and

Agrawal, D.P.

Enabling multiservice on 3G

and beyond: challenges and

future directions

21 2008 Laura, R., Matteo, B., and

Gianluca, R.

On ant routing algorithms in

Ad Hoc networks with

critical connectivity

22 2009

Chowdhury, N.M.M.K. and

Boutaba, R

Network virtualization: state

of the art and research

challenges

23 2009 Kaabneh, K., A. Halasa and

H. Al-Bahadili

An effective location-based

power conservation scheme

for mobile Ad Hoc networks

24 2009 Madan, R.; Mehta, N.B,

Molisch, A.F.; Jin Zhang

Energy-Efficient

Decentralized Cooperative

Routing in Wireless Network

25 2009

Nakayama, H., S.

Kurosawa, A. Jamalipour,

Y. Nemoto and N. Kato

A dynamic anomaly

detection scheme for aodv-

based mobile Ad Hoc

networks

26 2009 Sunho Lim; Chansu Yu;

Das, C.R.

Random Cast: An Energy-

Efficient Communication

Scheme for Mobile Ad Hoc

Networks

25


27 2009

Venkateswaran, A.;

Sarangan, V.; La Porta,

T.F.; Acharya, R.;

A Mobility-Prediction-Based

Relay Deployment

Framework for Conserving

Power in MANETs

28 2009 Ya Sungoh, K. and Ness S.

Analysis of Shortest Path

Routing for Large Multi-Hop

Wireless Networks

29 2010 Banerjee, A. and P. Dutta

Link stability and node

energy conscious local route-

repair scheme for mobile Ad

Hoc networks

30 2010

Bao, L. and J.J. Garcia-

Luna-Aceves

Stable energy-aware

topology management in Ad

Hoc networks

31 2010 Amilkar, P., Rafael, B. and

Francisco, H

Analysis of the efficacy of a

Two-Stage methodology for

ant colony optimization: Case

of study with TSP and QAP

32 2010

Hsin-Yun, L., Hao-Hsi, T.,

Meng-Cong, Z. and Pei-

Ying, L.

Decision support for the

maintenance management of

green areas

33 2010 Kalwar, S., Introduction to reactive

protocol

34 2010 Michael, M., Vasileios, P.

and Lixia, Z.

A taxonomy of biologically

inspired research in computer

networking

35 2010 Natsheh, E. and K.

Buragga,

Density based routing

algorithm for spare/dense

topologies in wireless mobile

Ad Hoc networks

36 2010 Pereira, N.C.V.N. and R.M.

de Moraes,

LatinCon05 - comparative

analysis of aodv route

recovery mechanisms in

wireless Ad Hoc networks

26


37 2010

Sudip, M., Sanjay

Dhurandher, K.,

Mohammad Obaidat, S.

Karan, V. and Pushkar, G.

A low-overhead fault-tolerant

routing algorithm for mobile

Ad Hoc networks: A scheme

and its simulation analysis

38 2010 Surachai, C., Ekram, H. and

Jeffrey, D.

Channel Assignment

Schemes for Infrastructure-

Based 802.11 WLANs: A

Survey

39 2010 Xiaohua, T., Yu, C. and

Xuemin, S.

DOM: A Scalable Multicast

Protocol for Next-Generation

Internet

40 2011

Abouei, J., Brown, J.D.,

Plataniotis, K.N. and

Pasupathy, S.

Energy Efficiency and

Reliability in Wireless

Biomedical Implant Systems

41 2011 Chandra Mohan, B. and

Baskaran, R

Energy Aware and Energy

Efficient Routing Protocol

for Adhoc Network using

Restructured Artificial Bee

Colony System


Baskaran, R

Reliable Barrier-free Services

in Next Generation Networks

43 2011 Chandramohan, B. and

Baskaran, R

Reliable Transmission for

Network Centric Military

Networks

44 2011

Goyal, M,Baccelli, E.;

Choudhury, G.; Shaikh, A.;

Hosseini, H.; Trivedi, K.

Improving Convergence

Speed and Scalability in

OSPF: A Survey

45 2011

Jun Zhao, Quanli, L., Wei,

W., Zhuoqun, W. and Peng,

S.

A parallel immune algorithm

for traveling salesman

problem and its application

on cold rolling scheduling

46 2011 Yang, F. Sun and Baolin

Ad Hoc on-demand distance

vector multipath routing

protocol with path selection

entropy

27


47 2012 Visu.P et al.,

Optimal Energy Management

in Wireless Adhoc Network

using Artificial Bee Colony

Based Routing Protocol

2.2 SURVEY ON ANT COLONY OPTIMIZATION (ACO)

Swarm intelligence (SI) is a new discipline of study that

contains a relatively optimal approach for problem solving which is the

imitation inspired from the social behaviours of insects and of other

animals, for ex: Ant colony optimization algorithm, artificial bee colony

algorithms and fire fly algorithm (Ducatelle et al, 2008).

The ACO is an optimization technique which is widely applied

for a variety of optimization problems and in almost all engineering field

of studies. The few application of ACO in the recent year are Job

Scheduling (Li-Ning et al 2010), Project Scheduling (Wang Chen et al

2010, Twomey et al 2010), Production management and maintenance

scheduling (Osama et al 2005), Cash Flow Management (Wei-Neng et al

2010), Manpower Scheduling and management (Hsin-Yun et al 2010),

TSP (Manuel and Christina 2010, Xiao-ming et al 2010), Clustering and

set partitioning (Ali and Babak 2010), Pattern Recognition (Zhiding et al

2010).

Deneubourg et al (1990) thoroughly investigated the

pheromone laying and following behaviour of ants. In an experiment

Argentine ants was connected to a food source by two bridges of equal

lengths. The author used the term Argentine ants for the ants which

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identifies the path, simply says the predictor of the path. The argentine

ants always spread the work place, searching other possible routes. In

such a setting, ants start to explore the surroundings of the nest and

eventually reach the food source. Along their path between food source

and nest, Argentine ants deposit pheromone.

Ant System, Ant Colony System and Ant Net proposed by

(Dorigo et al 1996, Dorigo and Luca 1997, Dorigo and Stutzle 2004) are

the significant implementation of ACO. Dorigo et al (1996) applied the

simple probability rule and Dorigo and Luca (1997) applied the state

transition rule for the decision model. Dorigo and Stutzle (2004)

redefined the pheromone update policy of ACO, and the term argentine

ant is replaced with forward ant.

Furthermore, there are some ACO approaches that adopt the

privileged pheromone lying in which ants only deposit pheromones

during their return trips. In using artificial ants for problem solving, some

of the features and capabilities of bio-logical ants (e.g., using visual and

marks) may be omitted, and other additional techniques (e.g., heuristic

functions) may be used to complement and supplement the use of

pheromone.

In the network routing, Ant-Net Routing using Ant Colony

Optimization (ACO) technique provide a better result than others due to

its real time computation and less control overhead. Kwang and Weng

(2003) comparing all routing algorithms with ACO, concludes that ants

are relatively small, can be piggybacked in data packets and more

frequent transmission of ants may be possible in order to provide updates

of routing information for solving link failures. Hence, using ACO for

29

routing in dynamic network seems to be appropriate. Routing in ACO is

achieved by transmitting ants rather than routing tables or by flooding

LSPs. Even though it is noted that the size of an ant may vary in different

systems/implementations, depending on their functions and applications,

in general, the size of ants is relatively small, in the order of 6 bytes.

Laura et al (2008) proposed a ACO algorithm which aims at

minimizing complexity in the nodes at the expenses of the optimality of

the solution, it results to be particularly suitable in environments where

fast communication establishment and minimum signalling overhead are

requested. However, this proposal is optimal for a less number of nodes in

the cluster and also not suitable for adhoc network. A fault tolerant

routing protocol (Sudip et al 2010) using greedy ACO routing mechanism

may tend to choose single path. This routing achieves high packet

delivery ratio and throughput whereas the packet loss on the link is not

taken into consideration.

Amilkar et al (2010) analysed the performance of ACO on

various case studies in the TSP using a two stage approach and concluded

the performance of ACO is optimal than existing for TSP. The two-stage

approach will converge (Yu and Zhang, 2009) quickly for lesser nodes

whereas it requires more convergence time, if number of nodes increases.

All the above ACO based routing algorithms identify and apply all

routing algorithm (Frank and Carsten 2010).

Goyal et al (2011) concluded that the number of possible routes

increases, the relative performance of multi-path routing also increases till

30

portant

consideration for implementing multi path routing and the optimal value

proposed. Table 2.2 shows the survey on ACO.

Table 2.2 Survey Table on ACO


1 1989 Goss, Aron, Deneubourg,

and Pasteels

Self-organized shortcuts in

the Argentine ant

2 1990 Deneubourg, J.L, Aron, S,

Goss, S. and Pasteels, J.M

The self-organizing

exploratory pattern of the

Argentine ant

3 1996 Dorigo, M., Maniezzo, V.

and Colorni, A.

Ant System: Optimization by

a colony of cooperating

agents

4 1997 Dorigo, M. and Luca,

M.G.

Ant Colony System: A

Cooperative Learning

Approach to the Traveling

Salesman Problem

5 1998 Villamizar OSPF optimized multi-path

(OSPF-OMP)

6 2003

Kwang Mong Sim and

Weng Hong Sun

Ant Colony Optimization for

Routing and Load-Balancing:

Survey and New Directions

2004 Dorigo, M. and Stutzle, T.

7 2004 Dorigo, M., M. Birattari

and T. Stutzle

Ant colony optimization

8 2008 Ren, G., Z. Wu, N. Zhao

and M. Lin,. 2008.

A Mutated Ant Colony

Optimization Algorithm for

Multiuser Detection

31


9 2009 Yu, X. and T. Zhang

Convergence and Runtime of

an Ant Colony Optimization

Model


Baskaran, R

Improving network

performance by optimal load

balancing using ACO based

Redundant Link Avoidance

algorithm

11 2010 Frank, N. and Carsten, W.

Ant Colony Optimization and

the minimum spanning tree

problem

12 2010

Li-Ning, X., Ying-Wu, C.,

Peng, W., Qing-Song, Z.

and Jian, X.

A Knowledge-Based Ant

Colony Optimization for

Flexible Job Shop Scheduling

Problems

13 2010 Manuel, L. and Christina,

B.

Beam-ACO for the travelling

salesman problem with time

windows

14 2010

Twomey, Stutzle, T.,

Dorigo, M., Manfrin, M.

and Birattari, M.

An analysis of

communication policies for

homogeneous multi-colony

ACO algorithms

15 2010

Wei-Neng Chen, Jun

Zhang, Rui-Zhang Huang,

and Ou Liu

Optimizing Discounted Cash

Flows in Project

Scheduling An Ant Colony

Optimization Approach

16 2010 Wu, Z., Y. Kuang, N.

Zhao and Y. Zhao

A Hybrid CDMA Multiuser

Detector with ACO and Code

Filtering System

17 2010 Xiao-ming, Y., Sheng, L.

and Yu-ming, W.

Quantum Dynamic

Mechanism-based Parallel

Ant Colony Optimization

Algorithm

32


18 2010

Xing, L.N., Y.W. Chen, P.

Wang, Q.S. Zhao and J.

Xiong

A Knowledge-Based Ant

Colony Optimization for

Flexible Job Shop Scheduling

Problems

19 2010 Yannis, M. and

Magdalene, M.

A hybrid genetic Particle

Swarm Optimization

Algorithm for the vehicle

routing problem


Baskaran, R

Priority and Compound Rule

Based Routing using Ant

Colony Optimization


Baskaran, R

Survey on Recent Research

and Implementation of Ant

Colony Optimization in

Various Engineering

Applications

22 2011 Li, H., Zhang, X., Liu and

Ying

Energy efficient routing

based on ant colony

algorithm in mine equipment

monitoring

23 2011

Roberto Fernandes

Tavares Neto and Moacir

Godinho Filho

A software model to

prototype ant colony

optimization algorithms


Baskaran, R

Ant Colony Optimization

based recent research and

implementation on several

engineering domain

33

2.3 SURVEY ON ARTIFICIAL BEE COLONY

ALGORITHM

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 paper 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 paper 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.

The ABC algorithm was first proposed for unconstrained

optimization problems on where that ABC algorithm showed superior

performance. Dervis and Celal (2011) describes a modified ABC

34

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.

For constraint handling, AB

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 (Eberhart et al, 2001). Structural

optimization, engineering design, economics and resource allocation are

just a few examples of fields for constrained optimization problems.

Chandramohan and Baskaran (2011a) 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 is capable of

greatly shortening time to give a solution; therefore it has been paid more

attention by the researchers. However, the actual parallel or distributed

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

multi-core processor. Typically, it was described that the serial and the

parallel implementations of simulated annealing.

The ABC is already applied for various engineering application

and proved optimal performance than existing algorithms which includes

35

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 shows 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.

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

36

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 (Rajagopalan and Shen,

2006) 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 is 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 will make 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 feasible from computational viewpoint. In this work,

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

approach.

Changsheng (2010) 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

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

algorithm is optimal than other existing algorithms.

37

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 above paper proposed 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 (Bonadeau et al,

1999) 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. While

the author proposes a second possibility in which an employed bee can

become scout. An employed bee can become scout through collision also.

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

like other ABC algorithms. Also number of scouts depends 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

depth why biology is an appealing and appropriate place to find

inspiration for computer networking research.

38

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. Table 2.3 shows the survey on

ABC.

Table 2.3 Survey Table on ABC


1 1999 Eric Bonabeau, Marco

Dorigo, Guy Theraulaz.

Swarm Intelligence: From

Natural to Artificial

Systems

2 2001 Russell C. Eberhart, Yuhui

Shi, James Kennedy,

Swarm Intelligence

3 2005 Michelle, M.E. and

Stephen, P.R.

Honey bees as a model for

understanding mechanisms

of life history transitions

4 2006 Sundaram Rajagopalan,

Chien-Chung Shen,

ANSI: A swarm

intelligence-based unicast

routing protocol for hybrid

Ad Hoc networks

39


5 2008

Frederick Ducatelle,

Gianni A. Di Caro, and

Luca M. Gambardella.

An Evaluation of Two

Swarm Intelligence

MANET Routing

Algorithms in an Urban

Environment

6 2009

Dervis, K. and Bahriye, A A comparative study of

Artificial Bee Colony

algorithm

7 2009 Alok, S

An artificial bee colony

algorithm for the leaf-

constrained minimum

spanning tree problem

8 2009 Karaboga, D. and B. Akay.

A comparative study of


algorithm

9 2009 Wang, J. and Y. Zhou,

Stochastic optimal

competitive Hopfield

network for partitional

clustering

10 2010 Changsheng, Z., Dantong,

O. and Jiaxu

An artificial bee colony

approach for clustering

11 2010

David, V., Julio, C.,

Francisco, J. and

Nicolas, R

A Honey Bee Foraging

approach for optimal

location of a biomass power

plant

12 2010 Ali M. and Babak, A

A new clustering algorithm

based on hybrid global

optimization based on a

dynamical systems

approach algorithm

13 2010 Hongnian, Z., Shujun, Z.

and Kevin, H.

A Review of Nature-

Inspired Algorithms

14 2010

Jiejin, C., Xiaoqian, M.,

Qiong, L., Lixiang, L. and

Haipeng, P.

A multi-objective chaotic

ant swarm optimization for

environmental

40


15 2010 Nicanor, Q. and Kevin,

M.P.

Honey bee social foraging

algorithms for resource

allocation: Theory and

application

16 2010 Peibo, X. and Huaxi, G.

Intelligent Bees for QoS

Routing in Networks-on-

Chip

17 2010 Quijanoa, N. and K.M.

Passino, 2010.

Honey bee social foraging

algorithms for resource

allocation: Theory and

application

18 2010 Taher, N., Hamed, Z.,

Meymand, and Majid, N.

A practical algorithm for

optimal operation

management of distribution

network including fuel cell

power plants

19 2010

Vera, D., J. Carabias, F.

Jurado and N. Ruiz-Reyes,

2010.

A Honey Bee Foraging

approach for optimal

location of a biomass power

plant

20 2010

Wang Chen, Yan-jun, S.,

Hong-fei, T., Xiao-ping, L.

and Li-chen, H.,

An efficient hybrid

algorithm for resource-

constrained project

scheduling

21 2010

Zhiding, Y., Oscar, C.A.,

Ruobing, Z., Weiyu, Y.

and Jing, T.

An adaptive unsupervised

approach toward pixel

clustering and color image

segmentation

22 2011 Dervis, K. and Celal, O. approach: Artificial Bee

Colony (ABC) algorithm

23 2011 Karaboga, D. and C.

Ozturk.

A novel clustering

approach: Artificial Bee

Colony (ABC) algorithm

41


24 2011 Rajesh, K., Devendra, S.

and Abhinav, S.

-agent

based particle swarm

optimization algorithm for

economic power dispatch


Optimal Energy

Management in Wireless

Adhoc Network using


Based Routing Protocol


Combined Swarm

Intelligence Routing

Protocol for MANET

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