A DIRECTED MUTATION OPERATION FOR THE DIFFERENTIAL EVOLUTION ALGORITHM

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Oct 23, 2013 (3 years and 5 months ago)

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A DIRECTED MUTATION OPERATION FOR THE DIFFERENTIAL
EV
O
LUTION ALG
O
RITHM


Hui
-
Yuan Fan
, and Jouni Lampinen


Department of Information Technology

Lappeenranta University of Technology

P. O.
Box 20, FIN
-
53851

Lappeenranta, Finland

E
-
mail: {fan,
jlampine}@lut.fi



School of Energy and Power Engineering

Xi'an Jiaotong University

Xi'an, 710049, P R China

Email: huiyfan@xjtu.edu.cn


Corresponding author



A modification is proposed for the differential evolution algorithm which aims to improve its convergence performance.
The modification embeds an additional operation, directed mutation, into an original version o
f the differential evolution.
The aim of this operation is to increase the convergence velocity of the differential evolution and thereby to obtain an
acceptable solution with a lower number of objective function evaluations. Such an improvement can be use
ful in many
real
-
world problems where the evaluation of a candidate solution is a computationally expensive operation and
consequently finding the global optimum or a good sub
-
optimal solution with the algorithm is too time
-
consuming, or even
impossible wi
thin the time available. The modified version of the differential evolution was empirically examined with a
suite of six well
-
known test problems and compared with the original version of the differential evolution algorithm. The
obtained numerical simulat
ion results suggested drawing a preliminary conclusion that the modified version statistically
outperforms the orig
i
nal one.


Keywords:

Evolutionary algorithm, Differential evolution, Nonlinear optimization.


A SIMULATION

BASED GENETIC

ALGORITHM FOR
RISK
-
B
ASED
PA
R
TNER SELECTION IN
NEW PRODUCT DEVELOPM
ENT


Hongyi Cao

and Dingwei Wang


Collage
of
Information Science and Engineering

Northeastern University

Shenyang, Liaoning,

People’s Republic of China


In this paper, we investigate the problem of partner sele
ction in new product development.
First
,
w
e give a formal
description of the problem and model it
as

a 0
-
1integer programming
problem
with non
-
linear objective function and
stochastic constraints. Because of the complexity of the constraints,
a
Monte Carlo

method is used to measure the
probabilit
ies

of
stochastic
constraints satisfaction. Then, we develop a simulation based
genetic algorithm approach
to
find the optimal solution for partner selection
.
The approach is demonstrated by some numerical examples.

The

results show
that the suggested approach

has high efficiency and the

model
has potential to practical applications.


Keywords:

Genetic algorithm
,
Monte Carlo simulation
, Partner selection, Risk analysis,
New product
development
.


ADVANCED PROCESS
PLANNING AND
SCHEDULING

WITH PRECEDENCE
CONSTRAINTS AND MACH
INE SELECTION USING
A GENETIC ALGORITHM


Chiung Moon and Young Hae Lee

Department of
I
ndustrial Engineering

Hanyang University, Ansan

425
-
791
, Korea

Tel:
+82
-
31
-
400
-
5268

Fax:
+82
-
31
-
400
-
3843

cumoon@hanyang.ac.kr

or
cumoon@hanmail.net



ABSTRACT

This paper deals with integrated process planning and scheduling problems with minimizing makespan for a flexible
flow m
anufacturing where
alternative
operations sequences

with precedence constraints and alternative machines. The
problem is formulated as a mathematical model which includes operation sequencing, machine selection, and operation
scheduling. The integrated pla
nning of having more than one machine to perform the same operation and precedence
constraints for sequences increases the size of the solution space, and consequently, makes the problem even more complex.
We develop a new genetic algorithm approach using
topological sort to solve the
model
efficiently.

Schedules with
operations sequences and machine selections are currently decided by the proposed approach. Some experimental results
are presented for various problem sizes and parameter settings to describe

the performance of the proposed approach.


KEYWORDS

Integrated
planning

and
scheduling,
topological sort, traveling salesman problem,
genetic algorithm
s.


OPTIMIZING THE PRODUCTION SCHEDULING OF A PETROLEUM
REFINERY THROUGH GENETIC ALGORITHMS


Mayron Rod
rigues de Almeida

Sílvio Hamacher


Department of Industrial Engineering

Pontifícia Universidade Católica do Rio de Janeiro

Rua Marquês de São Vicente, 225, Gávea, Rio de Janeiro, RJ,
Brasil, 22453
-
900

{
Mayron@ons.
org.br

;
Hamacher@rdc.puc
-
rio.br}

Marco Aurélio Cavalcanti Pacheco

Marley Maria B.R. Vellasco


ICA


Laboratory of Applied Computational Intelligence

Department of Electrical Engineering

Pontifícia Universidade Católica do Rio de Janeiro

Rua Marquês de S
ão Vicente, 225, Gávea, Rio de Janeiro, RJ,
Brasil, 22453
-
900

{
Marco@ele.puc
-
rio.br

;
Marley@ele.puc
-
rio.br
}


This paper presents a method, based on Genetic Algorithms,
to optimize the production scheduling of the fuel oil and
asphalt area in a petroleum refinery. The fuel oil and asphalt area is a multi
-
product plant, with two machine stages


one mixer and a set of tanks


with no setup time and with resource constrain
s in continuous operation. Two genetic
algorithm models were developed to establish the sequence and size of all production shares. A special mutation
operator


Neighborhood Mutation
-

is proposed to minimize the number of changes in the production. A mul
ti
-
objective fitness evaluation technique, based on an energy minimization method, was also incorporated to the Genetic
Algorithm models. The results obtained confirm that the proposed Genetic Algorithm models, associated with the
multi
-
objective energy mi
nimization method, are able to solve the scheduling problem, optimizing the refinery
operational objectives.



Keywords:
Production Scheduling, Petroleum Refinery, Genetic Algorithms.


A Genetic Algorithm Approach to Solving a Multiple
-
Inventory Loading P
roblem


J. Cole Smith
*


Department of Systems and Industrial Engineering

University of Arizona

P.O. Box 210020

Tucson, AZ 85721

Email: cole@sie.arizona.edu


In this paper we consider a multiple
-
inventory loading problem involving a set of commodities that

must be
transported from a distributor to a retailer. The vehicle carrying out this distribution is divided into several
compartments, in which only one type of commodity may be loaded. The problem becomes one of determining
optimal assignments of vehicle

compartments to commodities in order to minimize a mix of transportation and
inventory costs. We first demonstrate the weakness of the underlying linear relaxation of a traditional mixed
-
integer
programming approach that must be solved in a branch
-
and
-
bou
nd framework. Instead of pursuing the development
of an exact algorithm, we instead recommend the use of a genetic algorithm to quickly provide good quality
solutions. Next, we introduce an additional strategy for defeating symmetry complications arising i
n certain specially
structured problems. Finally, the effectiveness of each of the proposed techniques is demonstrated on a test bed of
problems.


Keywords:

Genetic algorithms, Mathematical modeling, Inventory management, Logistics




*

Acknowledgement:

This resea
rch was supported by University of Arizona Foundation and by the Office of the Vice
President for Research and Graduate Studies. The author also gratefully acknowledges the comments provided by two
anonymous referees, the Guest Editor, and by Mr. Ashwin N
aik, which led to an improved presentation of this topic.


A NEURAL NETWORKS AP
PR
OACH FOR DUE
-
DATE ASSIGNMENT IN A

WAFER FABRICATION FA
CTORY


Pei
-
Chann Chang and Jih
-
Chang Hsieh

Department of Industrial Engineering

Yuan Ze University

Nei
-
Li, Taoyuan, Taiwan

Email:
iepchang@saturn.yzu.e
du.tw


The production processes in a wafer fabrication factory are very complicated and time
-
consuming. This presents a
challenging problem to the production planning and scheduling department for the due
-
date assignment of each
order. This research propo
ses a simulation model to mimic a real wafer fabrication factory and the flowtime of each
order is collected for the purpose of due
-
date assignment. Various influential variables related to the flowtime of
each order are identified through regression analy
sis. Accordingly, a neural network model is established to forecast
the due
-
date of each order. The system is very applicable in the real world and the experimental results show that
the proposed approach is very convincing when compared with the tradition
al approaches.



Keywords:

Wafer fabrication factory, Due
-
date assignment, Backpropagation neural networks




JOINT MONITORING OF THE MEAN AND VARIANCE OF A PROCESS BY
USING AN ARTIFICIAL NEURAL NETWORK APPROACH


Chuen
-
Sheng Cheng and Shin
-
Jia Chen


Depart
ment of Industrial Engineering and Management

Yuan
-
Ze University

135 Yuan
-
Tung Road, Taoyuan,Taiwan, R.O.C
.


In this paper we consider the joint control of process mean and variance using artificial neural network technology.

The
performance of the neural
network was evaluated by estimating the recognition accuracy. Extensive comparisons show that
the neural network appears to be a good control procedure for joint monitoring of the process mean and variance.



Keywords:

Shewhart Control Chart, Neural Networ
k.




COMPARISON OF NEURAL AND STATISTICAL ALGORITHMS FOR
SUPERVISED CLASSIFICATION OF MULTI
-
DIMENSIONAL DATA


Te
-
Sheng Li
1
, Chang
-
You Chen
2

and Chao
-
Ton Su
2


1
Department of Industrial Engineering and Management

Minghsin Institute of Technology,

Hsinchu, 3
00 Taiwan


2
Department of Industrial Engineering and Management

National Chiao Tung University,

Hsinchu, 300 Taiwan


Various algorithms for supervised classification of multi
-
dimensional data have been implemented in the past decades.
Among these algorithm
s, neural and statistical classifiers are two major methodologies used in the literature. In this paper, a

comparison of different neural networks and statistical algorithms used

for classification is presented. Three types of neural
classifiers are consid
ered: Back
-
propagation (BP) network, Radial Basis Function (RBF), and Learning Vector Quantization
(LVQ). Meanwhile, the k
-
nearest neighbor (KNN) statistical classifier and linear discriminant analysis (LDA) are also
discussed in order to compare the accur
acy of classification with those of using

neural network models. This paper includes
an introduction to the

theoretical background of the classifiers, their implementation procedures, and two case studies to
evaluate their performance in diagnosis of disea
ses and glass identification. Both

neural networks and statistical models are
demonstrated to be

efficient and effective methods for multi
-
dimensional data classification. For

neural classifiers, the type
of neural network, the type of data, and the parame
ters of the neural network may have considerable impact on

the

classification accuracy. For statistical models, the type of data distribution and the criteria used to determine

the

thresholds
are the two most important factors in classification. In the cas
es studied in this paper, the overall performance of neural
networks is better than that of statistical models.

Finally, the comparison and discussion of these approaches are presented
in view of practical and theoretical considerations.



Keywords:

Back
-
propagation, Radial Basis Function, Learning Vector Quantization, k
-
nearest neighbor,
Discriminant analysis


THE PREDICTION OF AIRPLANE LANDING GRAVITY USING CASE
-
BASED REASONING


Chaochang Chiu and
Nan
-
Hsing Chiu


Department of Information Management

Y
uan Ze University

Taoyuan, Taiwan, R. O. C.

Email:{C. C. Chiu, imchiu@saturn.yzu.edu.tw; N. H. Chiu,
s887720@mail.yzu.edu.tw
}


Most flight accidents
occurring
worldwide

are
due to
the
lack of
an appropriate approach

in
the
landing
phase.
Recent data mining

developments have provided aviation insights into the landing phase. Among
these methods, c
ase
-
b
ased
r
easoning

is a potential approach that can be applied for predicting landing
gravity. T
his research proposes
a

novel
model construction method that
consis
ts of

non
-
linear similarity
function
s and

dynamic weighting
mechanisms

to
optimize the
prediction

accuracy.
We illustrate our
approach with t
he data obtained directly from flight data recorders of Boeing 747
-
400 airplanes.

This
experiment also shows
that
n
on
-
linear similarity function
s demonstrate

better prediction accuracy

over
the
results from
other approaches.



Keywords
:

Case
-
based reasoning
,
G
enetic algorithms
, Feature weights, Similarity functions, Landing gravity.