final seminar

Submitted to: Submitted by:
Dr. Ashok Pal Amit Kumar(15MSM1046)
University Institute of Sciences

INDEX
1. CONTENTS UPTO THIRD MST FOR
SEMINAR.
2. IDENTIFICATION OF PROBLEM.
3. LITERATURE SURVEY.
4. CONCLUSION.

CONTENTS UPTO THIRD MST
REPORT BASED ON LITERATURE
SURVEY

Research topic
Heuristic based multiobjective optimization
problem In crisp & fuzzy environment

Soft computing is the branch of computer science. It is the
important tool to find the inexact solution of the problem
having no exact solution.
Fuzzy logic tool was introduced by Lotfi A. Zadeh in 1965.
Ant colony optimization (ACO) was developed by Marco
Dorigo at Politecnico di Milano in 1991.
Lotfi A. Zadeh (1994), “Fuzzy Logic, Neural Networks and
Soft Computing”, Communication of the ACM, 37(3), pp77-
84.
In 1995, Dr. Eberhart and Dr. Kennedy have developed the
Particle swarm optimization (PSO), it is a population based
stochastic optimization technique.

Sr.
No
.
TITTLE AUTHOR JOURNAL Volum
e
Year
Pages
1 Heuristics for
Multi-objective
Optimization of
Two-Sided
Assembly Line
Systems
N. Jawahar,1 S.
G.
Ponnambalam,
2 K.
Sivakumar,1
and V.
Thangadurai3
Optimization of
Two-Sided
Assembly Line
Systems
Volume
2014,
Article
ID
458959
20
march,
2014
17
This paper addresses a multi-objective optimization of two sided
assembly line balancing problem associated with task
directions assignment restrictions for the objective criterion
of minimizing the unbalance among work stations and the
number of workstations.

LITERATURE SURVEYSr.
No
.
e
Year Page
2 A Hybrid
Integrated Multi-
Objective
Optimization
Procedure for
Estimating Nadir
Point
Kalyanmoy
Deb1 ,Kaisa
Miettinen2,
and Deepak
Sharma1
Journals on
computational.
10 2014 15
In this paper, we have extended our previous study on a serial
implementation of
an EMO procedure followed by an MCDM based local search approach to
find
extreme points accurately for estimating the nadir point of a multi-objective
optimization problem.

Sr.
No
.
e
Year Page
3 A HYBRID COA-
DEA METHOD
FOR
SOLVING
MULTI-
OBJECTIVE
PROBLEMS
Mahdi
Gorjestani,
Elham
shadkam,
Mehdi Parvizi
and Sajedeh
Aminzadegan
International
Journal on
Computational
Science &
Applications
Vol.5,
No.4,
Aug,
2015
9
In this paper ,it is tried to solve multi objective problems with a new creative
approach . This approach is the combination of cuckoo optimization
algorithm and DEA method. This method is the one of the fastest ,most
accurate and most logical method to solve multi objective problem because
it is the combination of both efficiency and finding the optimal solution.

Sr.
No
.
e
Year Page
4 International
Journal of
Computational
Intelligence
Research
Margarita
Reyes-Sierra
and Carlos A.
Coello Coello
Multi-Objective
Particle Swarm
Optimizers:
A Survey of the
State-of-the-Art
Vol.2,
No.3
2006 22
We have reviewed the state-of-the-art regarding extensions of PSO to handle multiple
objectives. We have started by providing a short introduction to PSO in which we described
its basic algorithm and its main topologies. We have also indicated the main issues that
have to be considered when extending PSO to multi-objective optimization, and then we
have analyzed each of them in more detail.

Sr.
No
.
e
Year Page
5 A Hybrid SS-
SA Approach
for Solving
Multi-
Objective
Optimization
Problems
Ramadan A.
ZeinEldin
European
Journal of
Scientific
Research
Vol.121
No.3,
2014 11
In this paper we have presented a hybrid approach based o scatter search
and simulated annealing to solve the multi-objectives optimization
problems. Different test problems were used to compare the
performance of our approach with other approaches.

Sr.
No
.
e
Year Page
6 A new ABC-based
multiobjective
optimization
algorithm with an
improvement
approach (IBMO:
improved bee
colony algorithm
for multiobjective
optimization)
Tahir SAG
1;
, Mehmet
CUNKAS2
Turkish Journal
of Electrical
Engineering &
Computer
Sciences
24 15 july
2014
33
This paper presents a new metaheuristic algorithm based on the articial bee colony
(ABC) algorithm for multiobjective optimization problems. The proposed hybrid
algorithm, an improved bee colony algorithm for multiobjective optimization called
IBMO, combines the main ideas of the simple ABC.

Sr.
No
.
e
Year
Pag
e
7 A Simulated
Annealing-
Based
Multiobjective
Optimization
Algorithm:
AMOSA
Sanghamitra
Bandyopadhya
y
, Ujjwal Maulik
IEEE
TRANSACTION
S ON
EVOLUTIONAR
Y
COMPUTATIO
N
VOL.
12, NO.
3,
June
2008
15
This paper describes a simulated annealing based
multiobjective optimization algorithm that incorporates the concept
of archive in order to provide a set of tradeoff solutions for
the problem under consideration.

Sr.
No
.
TITTLE
AUTHOR
JOURNAL
Volume
Year Page
8 Fuzzy Multi-
objective
Optimization
of a Synthesis
Unit
Utilizing
Uncertain Data
Harish
Garg,
S.P.
Sharma
Journal of
Uncertain
Systems
Vol.7,
No.1,
2013 9
It deal with imprecise, uncertain dependent information related to system
performance as the fuzzy methodology provides a better, consistent and
mathematically sound method for handling uncertainties in data than
conventional methods,

Sr.
No
.
e
Year
9 Multi-
Objective
Optimization
of
Time-Cost-
Quality Using
Hungarian
Algorithm
Ventepaka
Yadaiah1, V. V.
Haragopal2
Multiobjective
optimization
problems and its
solutions.
35 20
January
2016
In this paper, we propose an algorithm for solving multi-objective
assignment problem (MOAP)
through Hungarian Algorithm, and this approach emphasizes on
optimal solution of each objective
function by minimizing the resource.

Sr.
No.
TITTLE AUTHOR JOURNAL Volume Year
10 Tabu
Programming for
Multiobjective
Optimization
Problems.
Jerzy
Balicki .
International
Journal of
Computer
Science and
Network
Security
7,No-10 Oct,2007
In this paper, tabu programming for solving multiobjective optimization problems
has been considered. Tabu search algorithm has been extended by using a
computer program instead of a mathematical variable. For finding Paretooptimal
solutions, the ranking procedure in the neighborhood of the current solutions has
been applied.

Sr.
No
.
TITTLE AUTHOR JOURNAL
Volum
e
Year Page
11 Multi Objective
Multireservoir
Optimization in
Fuzzy
Environment for
River Sub Basin
Development
and Managemen
D. G.
REGULWAR,
P. Anand RAJ
Optimization
problems and its
solutions
4 July 22,
2009
10
Multiobjective, multireservoir optimization in fuzzy environment by using GA
is explored in this study. A multireservoir system in Godavari river sub basin in
Maharashtra State, India is considered. A MOGAFU-OPT model is developed
and applied to the case study. The objective function of the GA model was set
to maximize irrigation releases, hydropower production and level of
satisfaction.

Sr.
No.
TITTLE AUTHOR JOURNAL Pages Year
12 A Multiobjective
Approach for the
Heuristic
Optimization of
Compactness and
Homogeneity in
the Optimal
Zoning
B. Bernábe-
Loranca , C. A.
Coello-Coello,
M. Osorio-
Lama
Soft
computing
and its
problems
11 2012
This paper presents a multiobjective methodology for optimal zoning design
(OZ), based on the grouping of geographic data with characteristics of
territorial aggregation. The two objectives considered are the minimization of
the geometric compactness on the geographical location of the data and the
homogeneity of any of the descriptive variables

Sr.
No.
TITTLE AUTHOR JOURNAL Pages Year
13 Bicriteria
Optimization in
Wireless Sensor
Networks: Link
Scheduling and
Energy
Consumption
Christian
Vecchiola,
Michael
Kirley, and
Rajkumar
Buyya
Journal of
Sensors
11 2015
In this paper, we have investigated the challenging problem of joint optimization
on link scheduling and energy consumption for wireless sensor networks. By
considering route selection and link scheduling together, we have carried out the
analysis of energy consumption and link scheduling for wireless sensor network
and formulated it as a bicriteria-objective integer problem.

Sr.
No
.
e
Year
14 Local search-
based
heuristics for
the
multiobjective
multidimensio
nal knapsack
problem
Dalessandro
Soares Viannaa
*, Marcilene de
Fátima Dianin
Viannab
Journal on
multiobjective
knapsack
problem.
v. 23, n.
3,
July,2013
In this paper, we have proposed local search based algorithms, MGRASP and MILS, to
generate a good approximation of the set of efficient solution. They are applied for
solving the knapsack problem with r objectives and they are compared with MOTGA
algorithm, proposed by Alves and Almeida (2007).

Sr.
No
.
e
Year
15 Multi-
Objective Task
Scheduling in
Cloud
Computing
Using an
Imperialist
Competitive
Algorithm
Majid Habibi,
Nima Jafari
Navimipour
Journal on cloud
computing.
Vol. 7,
No. 5,
2016
In most of the algorithms provided for mapping the tasks to resources in cloud
environment, only attention is given to the time of performing tasks and bandwidth
features of resources and time of sending tasks are not considered as effective
parameters in producing the final answer. In this article for optimal timing and
increasing resource efficiency, a cost function aware of bandwidth is used for tasks
scheduling in cloud computing environment.

Sr.
No.
TITLE AUTHOR JOURNAL Volume Year
Pages
16 An efficient
multi-objective
optimization
algorithm based
on swarm
intelligence for
engineering
design
M. JANGA
REDDY and D.
NAGESH
KUMAR*
Engineering
Optimization
Vol. 39,
No. 1,
January
2007
20
An efficient procedure for solving multi-objective optimization problems using
swarm intelligence principles has been presented. The proposed algorithm for
multi-objective particleswarm optimization (MOPSO) combines Pareto dominance
criteria for nondomination selection, The proposed approach was tested on various
benchmark problems, having complexity in the search space with convex and non-
convex objective function.

Sr.
No
.
TITLE AUTHOR JOURNAL Volum
e
Year Page
17 A HYBRID
COA/ε-
CONSTRAINT
METHOD FOR
SOLVING
MULTI-
OBJECTIVE
PROBLEMS
Mahdi
parvizi,
Elham
Shadkam and
Niloofar
jahani
International
Journal in
Foundations of
Computer
Science &
Technology
(IJFCST
Vol.5,
No.5,
Septemb
er 2015
14
In this paper, a hybrid method for solving multi-objective problem has been
provided. The proposed method is combining the ε-Constraint and the Cuckoo
algorithm. First the multi objective problem transfers into a single-objective
problem using ε-Constraint, then the Cuckoo optimization algorithm will
optimize the problem in each task

Sr.
No
.
TITLE AUTHOR JOURNAL Year Page
18 A gradient-
based
multiobjective
optimization
technique
using an
adaptive
weighting
method
Kazuhiro
Izui,
Takayuki
Yamada and
Shinji
Nishiwaki
Congress on
Structural and
Multidisciplin
ary
Optimization
May 19 - 24,
2013,
6
While various multiobjective optimization methods based on metaheuristic
techniques have been proposed,these methods still encounter difficulties
when handling many variables, or numerous objectivesand constraints. This
paper proposes a new aggregative gradient-based multiobjective
optimization
method for obtaining a Pareto-optimal solution set.

Sr.
No
.
19 A multi-
objective hyper-
heuristic based
on choice
function
Mashael
Maashi a,⇑,
Ender Özcan
a, Graham
Kendall a,b,1
Expert Systems
with
Applications
2014 19
Hyper-heuristics are emerging methodologies that perform a search over the space of
heuristics in an attempt to solve difficult computational optimization problems. We
present a learning selection choice function based hyper-heuristic to solve multi-
objective optimization problems. This high level approach controls and combines the
strengths of three well-known multi-objective evolutionary algorithms (i.e. NSGAII,
SPEA2 and MOGA), utilizing them as the low level heuristics.

Sr.
No
.
e
Year Page
20 Evolutionary
Population
Dynamics and
Multi-
Objective
Optimisation
Problems
Andrew Lewis,
Sanaz
Mostaghim,
Marcus Randall
European
Journal of
Scientific
Research
Vol.121
No.3,
2014 11
The use of evolutionary population dynamics (EPD) as a controlling
metaheuristic for population-based optimisation algorithms is a recent
development. As briefly outlined in this chapter, there are a number of
open questions regarding the most effective means for its
application. This work has sought to describe some of the fundamental
concepts underpinning this novel paradigm, the rationale behind its
use, and some practical suggestions for its implementation.

Sr.
No
.
21 Combining Two
Search Paradigms
for
Multi-objective
Optimization:
Two-Phase and
Pareto Local
Search
Jérémie
Dubois-
Lacoste,
Manuel
López-
Ibáñez,
and Thomas
Stützle
Journal of
Electrical multi-
objective
optimization.
2013 21
In this chapter, we review metaheuristics for solving multi-objective combinatorial
optimization problems, when no information about the decision maker’s
preferences is available, that is, when problems are tackled in the sense of Pareto
optimization. Most of these metaheuristics follow one of the two main paradigms to
tackle such problems using metaheuristics. The first paradigm is to rely on Pareto
dominance when exploring the search space. The second paradigm is to tackle several
single-objective problems to find several solutions

Sr.
No.
TITLE AUTHOR JOURNAL
Page
22 Online Heuristic for
the Multi-objective
Generalized
Traveling Salesman
Problem
Joost van Pinxten,
Marc Geilen, Twan
Bastenz, Umar
Waqas, Lou Somers
Journals on traveling
salesman problem.
4
The Traveling Salesman Problem (TSP) is a combinatorial optimization
problem that aims to optimize a tour, visiting each of a set of cities exactly
once. Many scheduling problems requiring sequence optimization can be
defined as a variant of the TSP [1]. We explore online optimization
techniques for scheduling Flexible Manufacturing Systems (FMS) that
besides sequencing, involve selection optimization for multiple
objectives. These can be expressed as a Multi-Objective Generalized
TSP (MO-GTSP).

Sr.
No
.
TITLE
AUTHOR
JOURNAL
Volume
Year Page
23 A Hybrid SS-SA
Approach for
Solving Multi-
Objective
Optimization
Problems
Ramadan
A.
ZeinEldin
European
Journal of
Scientific
Research
Vol.121
No.3,
2014 11
Decision makers, nowadays, face complex real world problems having more
than one conflicting objective functions to be optimized at the same time. In
this paper, wedeveloped a hybrid approach based on scatter search and
simulated annealing for solvingthe multi-objective optimization problems. To
validate our approach, we solved some testproblems from the literature,
compared the results with other approaches, and found that our proposed
approach performs well.

Sr.
No
.
TITLE AUTHOR JOURNAL pages Year
24 A Multi-
objective
Evolutionary
Approach for
Integrated
Production-
Distribution
Planning
Problem in a
Supply Chain
Network
Keyvan
Sarrafhaa,*,
Abolfazl
Kazemib,
Alireza
Alinezhadb
Multiobjective
optimization
problems and its
solutions.
14 20
January
2016
Integrated production-distribution planning (PDP) is one of the most
important approaches in supply chain networks. We consider a supply
chain network (SCN) consististing of multi suppliers, plants, distribution
centers (DCs), and retailers. A bi-objective mixed integer linearprogramming
model for integrating production-distribution designed here aim to
simultaneously minimize total net costs in supply chainand transfer time of
products for retailers.

Sr.
No.
25 Multi-objective
optimization of
support vector
machines
Thorsten
Suttorp1
and
Christian
Igel2
Journals of
Multi-
objective
Machine
Learning
Vol. 16, 2006
Designing classiers is a multi-objective optimization (MOO) problem. The
application of true" MOO algorithms allows for visualizing trade-os, for
example between model complexity and learning accuracy or sensitivity
and specicity, for guiding the model selection process.

Sr.
No
.
26 Multi-Objective
Problem
Solving With
Offspring on
Enterprise
Clouds
Christian
Vecchiola,
Michael
Kirley, and
Rajkumar
Buyya
Journals on cloud
computing
2009 8
In this paper, we presented the approach proposed by Offspring for distributed
multi objective evolutionary algorithms on Enterprise Clouds. The aim of
Offspring is to minimize the code required to provide a distributed
implementation of a population based metaheuristics without requiring the
researchers to know distribution middleware APIs.

Sr.
No.
TITLE AUTHOR JOURNAL Pages Year
28 Hyperheuristic
Encoding Scheme
for
Multi-Objective
Guillotine Cutting
Problems
Jesica de
Armas, Gara
Miranda,
Coromoto
Le´on
Soft
computing
and its
problems
8 2012
Real-world multi-objective formulations of the 2DSPP and 2DCSP have been
presented. The two objectives considered for the 2DSPP were minimise the
overall length of the raw material and the total number of cuts needed to
obtain the complete set of demanded pieces. The two objectives considered for
the 2DCSP were maximise the total profit and minimise the total number of
cuts to obtain the total demanded pieces.

Sr.
No
.
e
Year
29 Local search-
based
heuristics for
the
multiobjective
multidimensio
nal knapsack
problem
Dalessandro
Soares Viannaa
*, Marcilene de
Fátima Dianin
Viannab
Journal on
multiobjective
knapsack
problem.
v. 23, n.
3,
July,2013
In this paper, we have proposed local search based algorithms, MGRASP and MILS, to
generate a good approximation of the set of efficient solution. They are applied for
solving the knapsack problem with r objectives and they are compared with MOTGA
algorithm, proposed by Alves and Almeida (2007).

Sr.
No
.
e
Year
30 Multi-
Objective Task
Scheduling in
Cloud
Computing
Using an
Imperialist
Competitive
Algorithm
Majid Habibi,
Nima Jafari
Navimipour
Journal on cloud
computing.
Vol. 7,
No. 5,
2016
In most of the algorithms provided for mapping the tasks to resources in cloud
environment, only attention is given to the time of performing tasks and bandwidth
features of resources and time of sending tasks are not considered as effective
parameters in producing the final answer. In this article for optimal timing and
increasing resource efficiency, a cost function aware of bandwidth is used for tasks
scheduling in cloud computing environment.

Sr.
No
.
31 An
optimization
algorithm for
multi-
objective
optimization
problem by
using
envelope-dual
method
X. H. Wanga,*,
C. H. Wana, C.
C. Suna, R. W.
Xiaa
on Aerospace
Technology and
Science,
10 2013
This paper, Envelope Dual Method (EDM) is utilized to solve the optimization
problem with multi-objective, multiconstraints
and multi-variable for the large complex engineering system optimization design. The
problem is first converted
into a single objective optimization based on the min-max algorithm, then into the
quasi-unconstrained optimization problem
with only one dual variable and its non-negative constraints based on the envelope
dual theory

Sr.
No
.
TITLE AUTHOR JOURNAL Pages
32 Solving Bilevel
Multi-
Objective
Optimization
Problems
Using
Evolutionary
Algorithms
Kalyanmoy Deb
and Ankur
Sinha
Journal on
optimization
technique
2016
Bilevel optimization problems require every feasible upperlevel
solution to satisfy optimality of a lower-level optimization problem.
These problems commonly appear in many practical problem solving
tasks including optimal control, process optimization, game-playing
strategy development, transportation problems,

Sr.
No
.
TITLE AUTHOR JOURNAL Year
33 Evolutionary
Multiobjective
Optimization:
A Short Survey
of the State-of-
the-art
.
M. Pil´at Journal on
multiobjective
optimization
2013
Many real-life problems have a natural representation in the framework of
multiobjective optimization. Evolutionary algorithms are generally considered one of
the most successful methods for solving the multiobjective optimization problems. In
this paper we present state-of-the-art multiobjective evolutionary algorithms and
briefly discuss their advantages and disadvantages.

Sr.
No
.
34 A Simulated
Annealing
Based Multi-
objective
Optimization
Algorithm:
AMOSA
Sanghamitra
Bandyopadhyay1
, Sriparna Saha1
, Ujjwal Maulik2
and Kalyanmoy
Deb3
Journal on cloud
computing.
15
In this article a simulated annealing based multi-objective optimization algorithm has
been proposed. The concept of amount of domination is used in solving the multi-
objective optimization problems.

Sr.
No
.
35 Bat Algorithm
for Multi-
objective
Optimisation
Journals on
Multi-objective
Optimisation
Journal on
optimization
technique
12
Engineering optimization is typically multiobjective and multidisciplinary with
complex constraints, and the solution of such complex problems requires efficient
optimization algorithms. Recently, Xin-She Yang proposed a bat-inspired algorithm
for solving nonlinear, global optimisation problems. In this paper, we extend this
algorithm to solve multiobjective optimisation problems.

LITERATURE SURVEY
Sr.
No
.
e
Year Page
36 Solving
Combinatorial
Optimization-
Using genetic
algorithm and
ant colony
optimization.
Gautham puttur
Rajappa
Tennessee
research and
creative
exchange
August
2012
16
An ant colony optimization based approach is presented to solve the split delivery vechile
routing problem. In this paper, the genetic algorithm and ant colony optimization was
applied to solve combinatorial optimization problems in the field of logistic and health
care staff scheduling. In this we used genetic algorithm used to solve the problems.

LITERATURE SURVEY
Sr.
No
.
e
Year Page
37 Markov
approximation
for
combinatorial
network
optimization
Minghua Chen,
Soung Chang
Liew,Ziyk Shao
and Caihong Kai
The Chinese
University of
Hong-Kong.
Department of
information
2008 14
Markov approximation framework studied in this paper is a general technique for
synthesizing distributed algorithms. We show that when using the log sum-exp function
to approximate the optimal value of any combinatorial problems. We end up with a
solution that can be interpreted as the stationary probability distribution of a class of
time reversible Markov chains.

LITERATURE SURVEY
Sr.
No
.
e
Year Page
38 knapsack
problem-non
fractional 0-1
knapsack
optimization
problem.
Vikas Thada,
Shivali dhaka
International
journals of
combinatorial
applications.
Volume
100,
No.15
August
2014
15
In this paper we solve the non fractional problem also known as 0-1 knapsack problem.It
is an optimization problem where we try to maximize the values that can be put into a
knapsack under the constraint of its weight. We solve this using genetic algorithm in
matlab using gatool.In this paper we concluded that, no. of items knapsack problem can
be easily solved without much complexity where as dynamic programming method takes
more amount of time and is less efficient.

LITERATURE SURVEY
Sr.
No
.
e
Year Page
39 Extremal
optimisation
applied to
constraint
combinatorial
multi objective
opt…problem
Pedro Sebstian
Gomez
Menesses
Information
technology
Septemb
er
2012
18
To incorporate a constraint handling mechanism that allows extremal optimisation to
deals with infeasible solutions. To provide a hybrid extremal optimisation framework to
solve single objective constrained combinatorial optimisation.

LITERATURE SURVEY
Sr.
No
.
e
Year Page
40 Packing
Problems in
combinatorial
optimisations
Probral Probral daad
CAPES
program
2012 19
IN this packing problem we are not investigate only theoretical ,but we find practically
like as cloth, glass, paper&wood industries. Also have applications in design of ULSI
circuits, warehouse storage, newspaper, paging,production of alcohol in sugar cane
industry.

LITERATURE SURVEY
Sr.
No.
TITTLE AUTHOR JOURNAL Year Page
41 HEURISTIC
SOLUTION
METHODS FOR
COMBINATORI
AL
OPTIMIZATION
PROBLEMS
HEURISTIC
SOLUTION
METHODS FOR
COMBINATORIA
L OPTIMIZATION
PROBLEMS
Frankfurt School
of Finance and
Management
March
2015
1-65
In this paper, transportation planning, bus and train scheduling, assignment of workers
to jobs such as drivers to buses and airline crew scheduling, and inventory control . All of
these problems share the trait that with growing problem size, the identification of good
solutions becomes more and more difficult. Consequently, all of these problems, which
come from different areas of business management, require heuristic solution approaches
such as the ones presented in this study. Future research should address the transfer of
NormANTS to other combinatorial optimization problems in order to further investigate
its potential

LITERATURE SURVEY
Sr
no
TITLE AUTHOR CONFERE
NCE
YEAR VOLUME PAGE
42 An Improved
Multi-State
Particle Swarm
Optimization
for Discrete
Optimization
Problems
Ismail
Ibrahim,
Zuwairie
Ibrahim,
Hamzah
Ahmad, and
Zulkifli Md.
Yusof
7th
Internatio
nal
Conferenc
e on
Computati
onal
Intelligenc
e
2015 6

LITERATURE SURVEY
Sr
no
TITLE AUTHO
R
JOURNAL YEAR VOLUME PAGE
43. BEE
COLONY
OPTIMIZA
TION
Dusan
Teodorov
ic
University of
Belgrade
2009 26
Resolution of a Combinatorial Problem using Cultural Algorithms

LITERATURE SURVEY
Sr
n
o
TITLE AUTHO
R
44
.
A
Decentralize
d Heuristic
for Multiple-
Choice
Combinatori
al
Optimizatio
n Problems
Christian
Hinrichs,
Sebastian
Lehnhoff
and
Michael
Sonnensc
hein
German
Operational
Research
Society
September
, 2012
6 6
A decentralized heuristic applicable to multi-agent systems (MAS), which is able to
solve multiple-choice combinatorial optimization problems (MC-COP). First, the MC-
COP problem class is introduced and subsequently a mapping to MAS is shown, in
which each class of elements in MC-COP corresponds to a single agent in MAS. The
proposed heuristic ”COHDA” is described in detail, including evaluation results from
the domain of decentralized energy management systems.

LITERATURE SURVEY
Sr
no
TITLE AUTHO
R
45. Approximati
on
Techniques
for
Stochastic
Combinator
ial
Optimizatio
n Problems
Ravishan
kar
Krishnas
wamy
Carnegie
Mellon
University
Research
Showcase @
CMU
5, 2012 136 136
The focus of this thesis is on the design and analysis of algorithms for basic problems in
Stochastic Optimization, specifically a class of fundamental combinatorial optimization
problems where there is some form of uncertainty in the input. Since many interesting
optimization problems are computationally intractable (NP-Hard), we resort to
designing approximation algorithms which provably output good solutions. However, a
common assumption in traditional algorithms.

LITERATURE SURVEY
Sr no TITLE AUTHOR JOURNAL YEAR PAGE
46. Binary Cockroach
Swarm
Optimization for
Combinatorial
Optimization
Problem
Ibidun
Christiana
Obagbuwa
Ademola
Philip
Algorithms :2
September,
2016
15
The Cockroach Swarm Optimization (CSO) algorithm is inspired by cockroach social
behaviour. It is a simple and efficient meta-heuristic algorithm and has been applied to
solve global optimization problems successfully. The original CSO algorithm and its
variants operate mainly in continuous search space and cannot solve binary-coded
optimization problems directly. Many optimization problems have their decision
variables in binary. Binary Cockroach Swarm Optimization (BCSO) is proposed in this
paper to tackle such problems and was evaluated on the popular Traveling Salesman
Problem (TSP), which is considered to be an NP-hard Combinatorial Optimization .

Sr.
No.
Pages
47 Swarm
Intelligence
based Soft
Computing
Techniques for
the
Solutions to
Multiobjective
Optimization
Problems
Hifza Afaq1
and Sanjay
Saini2
International
Journal of
Computer
Science Issues,
Vol. 8 May 2011 13
The multi objective optimization problems can be found in various fields such as
finance, automobile design, aircraft design, path optimization etc. This paper
reviews some of the existing literature on multi objective optimization problems
and some of the existing Swarm Intelligence (SI) based techniques to solve
these problems. The objective of this paper is to provide a starting point to the multi
objective optimization problems to the reader.

LITERATURE SURVEY
Sr
no
TITLE AUTHOR JOURNAL YEAR VOLUME PAGE
48. Approximate
Pareto
Optimal
Solutions of
Multiobjective
Optimal
Control
Problems by
Evolutionary
Algorithms
A. H.
Borzabadi1,
M.
Hasanabadi2
and N.
Sadjadi3
Journal of
Control
and
Optimizati
on in
Applied
Mathemati
cs
2015 1 21
In this paper an approach based on evolutionary algorithms to find Pareto optimal pair of state and
control for multiobjective optimal control problems (MOOCP)’s is introduced. In this approach, first a
discretized form of the time-control space is considered and then, a piecewise linear control and a
piecewise linear trajectory are obtained from the discretized time-control space using a numerical
method.

Sr.
No
.
e
Year Page
49 Ant Colony
Optimization for
Multi-objective
Optimization
Problems
In`es Alaya Journal on
Computer
Sciences
24 2010 8
We propose in this paper a generic algorithm based on Ant Colony Optimization to
solve multi-objective optimization problems. The proposed algorithm is parameterized
by the number of ant colonies and the number of pheromone trails. We compare
different variants of this algorithm on the multi-objective knapsack problem. We
compare also the obtained results with other evolutionary algorithms from
the literature.

SR.
NO
AUTHOR’S
NAME
TOPIC OF
RESEARCH
PAPER
NAME OF
JOURNAL
VOLUME PAGE NO. YEAR
50. Asha Gowda
Karegowda
Mithilesh
Prasad
A Survey of
Applications
of Glowworm
Swarm
Optimization
Algorithm
Internation
al Journal
of
Computer
Application
s (0975 –
8887)
Volume 3 39-42 2013
This paper presents a survey of applications of Glowworm swarm
optimization (GSO) algorithms designed in the various fields. Glowworm
Swarm Optimization (GSO) is a recent nature-inspired optimization
algorithm that simulates the behavior of the lighting worms. GSO algorithm
is suitable for a concurrent search of several solutions, having dissimilar or
equal objective function values

LITERATURE SURVEY
Sr
no
TITLE AUTHOR JOURNAL YEAR VOLUME PAGE
51 Adaptive
Based
Multiobjective
Optimization
Based Node
Placement
and Target
Detection in
Bistatic Radar
Network
M. Amala
Christy, Ms.
N.Anushya
Journal of
Engineerin
g Research
and
Applicatio
ns
1st March
2014
1 21
In this paper we studied the bistatic radar network that consists of multiple
separated radar transmitters (TXs) and receivers (RXs), aiming to detect a target
on a set of points of interest .

Sr.
No
.
52 Multiobjective
Optimization
in Health Care
Management.
A
metaheuristic
and simulation
approach
Cristina
Azc´arate,
Ferm´ın Mallor
and Aurora
Gafaro
Journal on
optimization
17 2005
In this paper we show how simulation can be used in combination with other
statistical and optimization tools to analyze health care management problems, and
obtain the “best configuration” when several objectives are simultaneously considered
(cost and patient satisfaction measures).

Sr.
No
.
53 Multiobjective
Optimization
Genetic
Algorithms for
Domestic
Airline Crew
Pairing
Problems
Tung-Kuan Liu Journal on
optimization
5 2005
Airline crew pairing problems involve assigning the required crew members to each
flight segment in a given time period, while complying with a variety of work
regulations and collective agreements. Traditional researches formulate the pairing
problems as integer programming problems, and use deterministic approaches to
optimize the solutions.

Sr.
No
.
54 INTEGRATING
MULTIOBJECTIV
E OPTIMIZATION
WITH THE SIX
SIGMA
METHODOLOGY
FOR ONLINE
PROCESS
CONTROL
EMAD H.
ABUALSAUO
D
Journal on
optimization
108 2005
In this section, the impact of the SSMO approach for Six Sigma-based online process
control is investigated. The investigation is conducted by a comparison between
EWMA- and CUSUM-based control charts results using DPMO as performance
measure. Results are examined when two different types of 6 -based control charts in
the case of implementing and 69 neglecting optimization results

LITERATURE SURVEY
Sr no TITLE AUTHOR JOURNAL PAGE
55 A fuzzy c-means bi-
sonar-based
Metaheuristic
Optimization
Algorithm
1Koffka Khan,
2Ashok Sahai,
Journals on
optimization
7
Fuzzy clustering is an important problem which is the subject of active research in
several real world applications. Fuzzy c-means (FCM) algorithm is one of the most
popular fuzzy clustering techniques because it is efficient, straightforward, and
easy to implement.

LITERATURE SURVEY
Sr no TITLE AUTHO
R
56. FUZZY
OPTIMIZAT
ION
MODELS
BASED
METAHEU
RISTICS
J.M.
Cadenas*
, J.L.
Verdegay
*
OPERACIO
NAL
2008 Vol. 29, No. 3 21
Fuzzy Linear Programming models and methods has been one of the most and well
studied topics inside the broad area of Soft Computing. Its applications as well as
practical realizations can be found in all the real world areas. In this paper a basic
introduction to the main models and methods in fuzzy mathematical programming.

Sr.
No
.
57 BEE COLONY
OPTIMIZATION
PART I: THE
ALGORITHM
OVERVIEW
Tatjana
DAVIDOVIC´,
Journal of
Operations
Research
Number
1
14
BCO is a simple, but efficient meta–heuristic technique that has been
successfully applied to many optimization problems, mostly in transport,
location and scheduling fields.

Sr.
No
.
TITLE AUTHOR JOURNAL Page
58 A Fuzzy Linear
Programming in
Optimizing Meat
Production
Lazim
Abdullah#1
and Nor
Hafizah
Abidin#´,
Journal of
optimization
9
This paper proposes optimal solutions and profits of red meat production
problem using fuzzy linear programming with single objective function

LITERATURE SURVEYSr no TITLE AUTHO
R
59 MULTI-
OBJECTIVE
CONCEPTU
AL DESIGN
OPTIMIZAT
ION OF A
DOMESTIC
UNMANNE
D AIRSHIP
Sasan
Amani,
Seid
Hossein
Pourtakd
oust,
Farshad
Pazooki*
JOURNAL OF
THEORETIC
AL AND
APPLIED
MECHANICS
2008 53 14
Multi objective optimization for the configuration design of a domestic airship is
performed considering static stability and flight performance merits. The role of other
objectives such as the aerodynamic drag, the airship hull surface as well as the helium
mass is also investigated

LITERATURE SURVEY
Sr.
No
.
TITTLE AUTHOR JOURNAL Year Page
60 Speed-
Constrained
Multi-
Objective PSO
for
Optimization
of Problem
Prachi Gupta1 ,
Dr.
Ramachandra
Pujeri 2
International
Journal of
Science and
Research (IJSR)
2013 14
In this work we represent a new algorithm multi-objective particle swarm optimization
algorithm (PSO) characterized by the use of a strategy to limit the velocity of the
particles. So that the Speed-constrained Multi-objective PSO (SMPSO) allows to produce
new effective particle positions in those cases where the velocity becomes too high or too
low

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