chapter 2 literature survey - shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/16047/10... ·...
TRANSCRIPT
20
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
S.No Year Author Name Methodology
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
S.No Year Author Name Methodology
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
S.No Year Author Name Methodology
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
S.No Year Author Name Methodology
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
42 2011 Chandra Mohan, B. and
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
S.No Year Author Name Methodology
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
28
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
S.No Year Author Name Methodology
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
S.No Year Author Name Methodology
9 2009 Yu, X. and T. Zhang
Convergence and Runtime of
an Ant Colony Optimization
Model
10 2010 Chandra Mohan, B. and
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
S.No Year Author Name Methodology
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
20 2011 Chandra Mohan, B. and
Baskaran, R
Priority and Compound Rule
Based Routing using Ant
Colony Optimization
21 2011 Chandramohan, B. and
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
24 2012 Chandramohan, B. and
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
S.No Year Author Name Methodology
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
S.No Year Author Name Methodology
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
Artificial Bee Colony
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
S.No Year Author Name Methodology
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
S.No Year Author Name Methodology
24 2011 Rajesh, K., Devendra, S.
and Abhinav, S.
-agent
based particle swarm
optimization algorithm for
economic power dispatch
25 2012 Visu.P et al.,
Optimal Energy
Management in Wireless
Adhoc Network using
Artificial Bee Colony
Based Routing Protocol
26 2013 Visu.P et al.,
Combined Swarm
Intelligence Routing
Protocol for MANET