Technical Sessions – Monday July 11

136-701, Seoul, Korea, Republic Of, fineyouth@korea.ac.kr,
Cheong Sool Park, Jun Seok Kim, Sung-Shick Kim, Jun-Geol
Baek
We propose a new method to improve the accuracy for multivariate classification
by integrating feature selection and classification. The method is
self-sufficient by using the properties of various existing algorithms to learn
NN(neural network) as training information. Each learned NN reflects those
properties via connecting weights. The connected NN can be learned again for
classification and to produce new features. We also study algorithm combinations
to determine how to use the method suitably for data characteristics. The
method can be applied to various areas requiring high accuracy.
4 - Cluster Validation Attitude via Information Distances
Zeev (Vladimir) Volkovich, Ort Braude Academic College,
Yehiam 6, 21823, Karmiel, Israel, zeev@actcom.co.il
Stability of cluster solutions reveals the reliability of the clustering process.
Partition’ readiness is characterized by the its clusters homogeneity represented
in our approach by the information distances between samples’ occurrences in
the clusters. In the framework of the Gaussian Mixture Model these distances
lead to the known probability metrics such as the weighed T-square statistic.
The appropriate cluster number can be deduced from the most concentrated
at the origin, distance’s empirical distribution, constructed for several possible
quantities of clusters.
� MB-14
Monday, 14:00-15:30
Meeting Room 207
Nonsmooth Optimization I
Stream: Continuous and Non-Smooth Optimization
Invited session
Chair: Adil Bagirov, School of Information Technology &
Mathematical Sciences, University of Ballarat, University Drive,
Mount Helen, P.O. Box 663, 3353, Ballarat, Victoria, Australia,
a.bagirov@ballarat.edu.au
1 - Epsilon-cleaning of Big Datasets
Andrew Yatsko, ITMS Dept., University of Ballarat, Mt. Helen
campus, 3350, Mount Helen, VIC, Australia,
andrewyatsko@students.ballarat.edu.au, Adil Bagirov
In applications of Cluster Analysis and Data Classification there is a performance
issue arising from the sheer multitude of instances. An existing technique
does clearing of the closely packed data, but what is discarded is arbitrary.
We address the issue of bias and suggest a way of preserving the cropped
data. A modified procedure of cleaning is proposed and tested on some known
examples. It is run recursively on reduced sets, their integrity being verified.
The staged out cleaning offers an update mode from simple to complex, able to
dramatically improve performance of some algorithms.
2 - Lipschitzian Properties of the Dual Pair in Infinite-
Dimensional Linear Optimization
Marco A. López-Cerdá, Statistics and Operations Research,
Alicante University, Ctra. San Vicente de Raspeig s/n, 3071,
Alicante, Spain, marco.antonio@ua.es, Andrea Beatriz Ridolfi,
Virginia N. Vera de Serio
We apply coderivatives and other tools from variational analysis to study the
stability of the feasible sets of both, the primal and the dual problem in infinitedimensional
linear optimization with infinitely many explicit constraints and
an additional conic constraint. After providing some specific duality results for
our dual pair, we study the Lipschitz-like property of both mappings and also
give bounds for the associated Lipschitz moduli.
3 - Novel Robust Regression Methods Based on Nonsmooth
and Derivative Free Optimization
Gleb Beliakov, School of Information Tecnology, Deakin
University, Melbourne, Victoria, Australia, gleb@deakin.edu.au,
Andrei Kelarev
Robust regression methods based on non-smooth optimization methods have
many important applications. Our experiments compare the performance of
several derivative free optimization algorithms computing robust multivariate
estimators, where the objective is non-smooth, non-convex and expensive to
calculate. It is shown that the existing algorithms often fail to deliver optimal
solutions. We introduce two new methods using Powell’s derivative free
algorithm. Extensive experimental results demonstrate that our new proposed
methods are reliable and can process very large data sets.
IFORS 2011 - Melbourne MB-15
4 - Algorithms for Optimization of Electricity Distribution
Systems when Upgraded by Renewable Energy
Sattar Seifollahi, School of Information Technology &
Mathematical Sciences, University of Ballarat, Australia,
s.seifollahi@ballarat.edu.au, Adil Bagirov
Distributed energy planning is a non-convex combinatorial problem, and in
many cases, it may contain integer variables. In this talk, we present nonsmooth
optimization based algorithms for solving such problems. The algorithms
can be applied to networks of large size and have advantages over
the existing algorithms in that they guarantee a globally optimal solution. An
overview on the use of some existing optimization algorithms in the distribution
systems is also provided. The results of the proposed methods are compared
with others, demonstrating the efficiency of the proposed algorithms.
� MB-15
Monday, 14:00-15:30
Meeting Room 208
Analyses for Air and Space Operations
Stream: Military, Defense and Security Applications
Invited session
Chair: Ariela Sofer, George Mason University, MS4A6 4400
University Drive, 22030, Fairfax, VA, United States, asofer@gmu.edu
1 - Predicting the Required Naval Combat Helicopter Fleet
Size
David Marlow, Air Operations Division, DSTO, 506 Lorimer St,
3207, Fishermans Bend, Vic, Australia,
david.marlow@dsto.defence.gov.au, Ana Novak
A discrete event simulation has been developed that models a fleet of naval
combat helicopters. The simulation includes detailed models of the ashore
and embarked flying program, and represents unscheduled, phased and deep
maintenance. The purpose of the model is to assist the Australian Government
in determining the size of the new fleet of naval combat helicopters. The
fleet must meet minimum requirements for the number of helicopters embarked
on ships, while simultaneously providing a minimum number of annual flying
hours ashore.
2 - Routing Optimisation for Air-to-Air Refuelling
Yue-Jin Wang, Joint Operations Division, Defence Science and
Technology Organisation, DSTO-Fairbairn, Canberra, ACT,
Australia, yue-jin.wang@dsto.defence.gov.au, Ian Brunskill
Air-to-Air refuelling (AAR) is the process of transferring fuel from one aircraft
(tanker) to another (receiver) during flight. Optimal use of limited tanker resources
is a major concern in military AAR planning. This paper formulates
the tanker assignment problem as a multiple-vehicle routing problem with time
windows (VRPTW). It aims at designing a set of minimum-cost routes for a
tanker fleet tasked to refuel a number of receiver aircraft at predefined locations
in the required time windows. A genetic algorithm-based technique is
developed for solving this complicated VRPTW problem.
3 - Analysis Tools in Swedish Air Force Studies
Anders Tavemark, Division of Defence Analysis, Swedish
Defence Research Agency, FOI, SE-172 90, Stockholm, Sweden,
tavemark@foi.se
Swedish operational analysts support military studies at the Swedish Armed
Forces. The studies range from system studies at the armed services level to
long term planning studies at the armed forces level. Different study questions
require different study approaches. One constant limitation is available resources.
This presentation describes experiences from using different analysis
tools in Swedish Air Force studies, ranging from spreadsheets to the Swedish
Air Force Combat Simulation Centre.
4 - Scheduling Optimization for Multi-Satellite Constellations
using Column Generation
Ariela Sofer, George Mason University, MS4A6 4400 University
Drive, 22030, Fairfax, VA, United States, asofer@gmu.edu
17