eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
17
10.4230/DagSemProc.04461.1
article
04461 Abstracts Collection – Practical Approaches to Multi-Objective Optimization
Branke, Jürgen
Kalyanmoy, Deb
Miettinen, Kaisa
Steuer, Ralph E.
From 07.11.04 to 12.11.04, the Dagstuhl Seminar 04461
``Practical Approaches to Multi-Objective Optimization'' was held
in the International Conference and Research Center (IBFI),
Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.1/DagSemProc.04461.1.pdf
Multi-objective optimization
evolutionary algorithms
decision support system
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
5
10.4230/DagSemProc.04461.2
article
04461 Summary – Practical Approaches to Multi-Criterion Optimization
Branke, Jürgen
Deb, Kalyanmoy
Miettinen, Kaisa
Steuer, Ralph E.
Summary of the Dagstuhl Seminar 04461. Motivation, proceedings, achievements and feedback, future seminars
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.2/DagSemProc.04461.2.pdf
Multi-criterion Optimization
Classical and Evolutionary Approaches
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
39
10.4230/DagSemProc.04461.3
article
A New Adaptive Algorithm for Convex Quadratic Multicriteria Optimization
Fliege, Jörg
Heermann, Christoph
Weyers, Bernd
We present a new adaptive algorithm for convex quadratic multicriteria
optimization. The algorithm is able to adaptively refine the approximation
to the set of efficient points by way of a warm-start interior-point
scalarization approach. Numerical results show that this technique is
an order of magnitude faster than a standard method used for this problem.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.3/DagSemProc.04461.3.pdf
Multicriteria optimization
warm-start methods
interior-point methods
primal-dual algorithms
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
15
10.4230/DagSemProc.04461.4
article
A New Approach on Many Objective Diversity Measurement
Mostaghim, Sanaz
Teich, Jürgen
In multi-objective particle swarm optimization (MOPSO) methods, selecting the best {it local guide} (the global best particle)
for each particle of the population from a set of Pareto-optimal solutions has a great impact on the
convergence and diversity of solutions, especially when optimizing problems with high number of objectives.
here, we introduce the Sigma method as a new method for finding best local guides for each particle of the population.
The Sigma method is implemented
and is compared with another method, which uses the strategy of an existing MOPSO method for
finding the local guides.
These methods are examined for different test functions and the results are compared with the results of a multi-objective
evolutionary algorithm (MOEA).
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.4/DagSemProc.04461.4.pdf
Multi-objective Optimization
Particle Swarm Optimization
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
2
10.4230/DagSemProc.04461.5
article
A Tutorial on Evolutionary Multi-Objective Optimization (EMO)
Deb, Kalyanmoy
Many real-world search and optimization problems are naturally posed
as non-linear programming problems having multiple objectives.
Due to lack of suitable solution techniques, such problems are
artificially converted into a single-objective problem and solved.
The difficulty arises because such problems give rise to a set
of Pareto-optimal solutions, instead of a single optimum solution.
It then becomes important to find not just one Pareto-optimal
solution but as many of them as possible. Classical methods are
not quite efficient in solving these problems because they require
repetitive applications to find multiple Pareto-optimal solutions
and in some occasions repetitive applications do not guarantee
finding distinct Pareto-optimal solutions. The population approach
of evolutionary algorithms (EAs) allows an efficient way to find
multiple Pareto-optimal solutions simultaneously in a single
simulation run.
In this tutorial, we discussed the following aspects related to
EMO:
1. The basic differences in principle of EMO with classical methods.
2. A gentle introduction to evolutionary algorithms with simple
examples. A simple method of handling constraints was also
discussed.
3. The concept of domination and methods of finding non-dominated
solutions in a population of solutions were discussed.
4. A brief history of the development of EMO is highlighted.
5. A number of main EMO methods (NSGA-II, SPEA and PAES) were
discussed.
6. The advantage of EMO methodologies was discussed by presenting
a number of case studies. They clearly showed the advantage of
finding a number of Pareto-optimal solutions simultaneously.
7. Three advantages of using an EMO methodology were stressed:
(i) For a better decision making (in terms of choosing a
compromised solution) in the presence of multiple solutions
(ii) For finding important relationships among decision variables
(useful in design optimization). Some case studies from engineering
demonstrated the importance of such studies.
(iii) For solving other optimization problems efficiently. For
example, in solving genetic programming problems, the so-called
`bloating problem of increased program size can be solved by using
a second objective of minimizing the size of the programs.
8. A number of salient research topics were highlighted. Some of
them are as follows:
(i) Development of scalable test problems
(ii) Development of computationally fast EMO methods
(iii) Performance metrics for evaluating EMO methods
(iv) Interactive EMO methodologies
(v) Robust multi-objective optimization procedures
(vi) Finding knee or other important solutions including partial
Pareto-optimal set
(vii) Multi-objective scheduling and other optimization problems.
It was clear from the discussions that
evolutionary search methods offers an alternate means of solving
multi-objective optimization problems compared to classical
approaches. This is why multi-objective optimization using EAs is
getting a growing attention in the recent years.
The motivated readers may explore
current research issues and other important studies from various
texts (Coello et al, 2003; Deb, 2001), conference proceedings
(EMO-01 and EMO-03 Proceedings) and numerous research papers
(http://www.lania.mx/~ccoello/EMOO/).
References:
----------
C. A. C. Coello, D. A. VanVeldhuizen, and G. Lamont.
Evolutionary Algorithms for Solving Multi-Objective Problems.
Boston, MA: Kluwer Academic Publishers, 2002.
K.Deb. Multi-objective optimization using evolutionary algorithms.
Chichester, UK: Wiley, 2001.
C. Fonseca, P. Fleming, E. Zitzler, K. Deb, and L. Thiele, editors.
Proceedings of the Second Evolutionary Multi-Criterion
Optimization (EMO-03) Conference
(Lecture Notes in Computer Science (LNCS) 2632).
Heidelberg: Springer, 2003.
E. Zitzler, K. Deb, L. Thiele, C. A. C. Coello, and D. Corne,
editors. Proceedings of the First Evolutionary Multi-Criterion
Optimization (EMO-01) Conference
(Lecture Notes in Computer Science (LNCS) 1993).
Heidelberg: Springer, 2001.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.5/DagSemProc.04461.5.pdf
Multi-objective optimization
multi-criterion optimization
Pareto-optimal solutions
Evolutionary methods
EMO
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
11
10.4230/DagSemProc.04461.6
article
An Adaptive Scheme to Generate the Pareto Front Based on the Epsilon-Constraint Method
Laumanns, Marco
Thiele, Lothar
Zitzler, Eckart
We discuss methods for generating or approximating the Pareto set of multiobjective optimization problems by solving a sequence of constrained single-objective problems.
The necessity of determining the constraint value a priori is shown to be a serious drawback of the original epsilon-constraint method. We therefore propose a new, adaptive scheme to generate appropriate constraint values during the run. A simple example problem is presented, where the running time (measured by the number of constrained single-objective sub-problems to be solved) of the original epsilon-constraint method is exponential in the problem size (number of decision variables), although the size of the Pareto set grows only linearly. We prove that --- independent of the problem or the problem size --- the time complexity of the new scheme is O(k^{m-1}), where k is the number of Pareto-optimal solutions to be found and m the number of objectives. Simulation results for the example problem as well as for different instances of the multiobjective knapsack problem demonstrate the behavior of the method, and links to reference implementations are provided.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.6/DagSemProc.04461.6.pdf
Multiple objective optimization
non-dominated set
Pareto set
epsilon-constraint method
generating methods
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
11
10.4230/DagSemProc.04461.7
article
Application Issues for Multiobjective Evolutionary Algorithms
Hanne, Thomas
In the talk, various issues of the design and application of multiobjective evolutionary algorithms for real-life optimization problems are discussed. In particular, questions on problem-specific data structures and evolutionary operators and the determination of method parameters are treated. Three application examples in the areas of constrained global optimization (electronic circuit design), semi-infinite programming (design centering problems), and discrete optimization (project scheduling) are discussed.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.7/DagSemProc.04461.7.pdf
Multiobjective optimization
Pareto set
evolutionary algorithm
discrete optimization
continuous optimization
electronic circuit design
semi-infin
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
17
10.4230/DagSemProc.04461.8
article
Approximation and Visualization of Pareto Frontier in the Framework of Classical Approach to Multi-Objective Optimization
Lotov, Alexander
This paper is devoted to a Pareto frontier generation technique, which is aimed at subsequent visualization of the Pareto frontier in an interaction with the user. This technique known as the Interactive Decision Maps technique was initiated about 30 years ago. Now it is applied for decision support in both convex and non-convex decision problems in various fields, from machinery design to environmental planning. The number of conflicting criteria explored with the help of the Interactive Decision Maps technique is usually between three and seven, but some users manage to apply the technique in the case of a larger number of criteria. Here we outline the main ideas of the technique, concentrating at nonlinear problems.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.8/DagSemProc.04461.8.pdf
Multi-objective optimization
Pareto frontier
visualization
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
5
10.4230/DagSemProc.04461.9
article
Current Status of the EMOO Repository, Including Current and Future Research Trends
Coello Coello, Carlos A.
In this talk, Ill present some statistics of the EMOO repository (delta.cs.cinvestav.mx/~ccoello/EMOO/), emphasizing some of the trends that have been detected in terms of basic research and applications of multi-objective evolutionary algorithms. For example, Ill discuss the remarkable increase in PhD theses related to EMOO, as well as the number of journal papers and exposure of the area in evolutionary computation conferences. Finally, some (potential) future research trends will also be discussed.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.9/DagSemProc.04461.9.pdf
Evolutionary multiobjective optimization
multiobjective optimization
repository
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
8
10.4230/DagSemProc.04461.10
article
Effects of Crossover Operations on the Performance of EMO Algorithms
Ishibuchi, Hisao
This paper visually demonstrates the effect of crossover operations on the performance of EMO algorithms through computational experiments on multi-objective 0/1 knapsack problems. In our computational experiments, we use the NSGA-II algorithm as a representative EMO algorithm. First we compare the performance of the NSGA-II algorithm between two cases: NSGA-II with/without crossover. Experimental results show that the crossover operation has a positive effect on the convergence of solutions to the Pareto front and a negative effect on the diversity of solutions. That is, the crossover operation decreases the diversity of solutions while it improves the convergence of solutions to the Pareto front. Next we examine the effects of recombining similar or dissimilar parents using a similarity-based mating scheme. Experimental results show that the performance of the NSGA-II algorithm is improved by recombining similar parents and degraded by recombining dissimilar ones. Finally we show that the recombination of extreme and similar parents using the similarity-based mating scheme drastically improves the diversity of obtained non-dominated solutions without severely degrading their convergence to the Pareto front. An idea of dynamically controlling the selection pressure toward extreme and similar parents is also illustrated through computational experiments.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.10/DagSemProc.04461.10.pdf
Evolutionary Multiobjective Optimization
Multiobjective 0/1 Knapsack Problems
Crossover Operations
Mating Restriction
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
0
10.4230/DagSemProc.04461.11
article
Hybrid Representations for Composition Optimization and Parallelizing MOEAs
Streichert, Felix
Ulmer, Holger
Zell, Andreas
We present a hybrid EA representation suitable to optimize composition optimization problems ranging from optimizing recipes for catalytic materials to cardinality constrained portfolio selection. On several problem instances we can show that this new representation performs better than standard repair mechanisms with Lamarckism.
Additionally, we investigate the a clustering based parallelization scheme for MOEAs. We prove that typical "divide and conquer'' approaches are not suitable for the standard test functions like ZDT 1-6. Therefore, we suggest a new test function based on the portfolio selection problem and prove the feasibility of "divide and conquer'' approaches on this test function.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.11/DagSemProc.04461.11.pdf
Multi-objective Evolutionary Algorithms (MOEAs)
Solution Representation
Constrained Portfolio Selection Problem
Parallelizing MOEAs
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
4
10.4230/DagSemProc.04461.12
article
Multi-criteria ranking of a finite set of alternatives using ordinal regression and additive utility functions - a new UTA-GMS method
Slowinski, Roman
Greco, Salvatore
Mousseau, Vincent
UTA-GMS is a new method for assessment of strong or weak outranking relation in a problem of multi-criteria ranking, proposed by the authors. The ranking concerns a finite but relatively large set of alternatives A. We assume indirect preference information supplied by the decision maker (DM) in form of a complete preorder on a subset of reference alternatives R, called reference preorder. The preference model build from this information is an additive value function. The technique of passing from reference preorder to compatible additive value functions is called ordinal regression and it is well known from the UTA method proposed by Jacquet-Lagreze and Siskos in 1982. Unlike in the UTA method, we take into account all compatible value functions (instead of one or several most characteristic) at the stage of ranking the whole set A of alternatives. Moreover, we do not impose the additive value function to have piecewise-linear components but we accept any additive form. The resulting relations in A are twofold: strong outranking (if alternative x has greater value than y for all compatible value functions) and weak outranking (if alternative x has greater value than y for at least one compatible value function). Strong outranking is a partial preorder and weak outranking is a complete preorder in A. The strong outranking is of particular interest for the DM – it corresponds to dominance relation when the set of reference alternatives is empty, and to a complete preorder relation when the reference ranking is compatible with a single value function only. This approach has several interesting extensions useful for practical applications. The method has been implemented for a PC and will be presented together with an example of application.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.12/DagSemProc.04461.12.pdf
Multiple-criteria ranking
ordinal regression
partial preorder
UTA-like method
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
13
10.4230/DagSemProc.04461.13
article
Multi-objective Optimization and its Engineering Applications
Nakayama, Hirotaka
Many practical optimization problems usually have several conflicting objectives. In those multi-objective optimization, no solution optimizing all objective functions simultaneously exists in general. Instead, Pareto optimal solutions, which are ``efficient" in terms of all objective functions, are introduced. In general we have many Pareto optimal solutions. Therefore, we need to decide a final solution among Pareto optimal solutions taking into account the balance among objective functions, which is called ``trade-off analysis". It is no exaggeration to say that the most important task in multi-objective optimization is trade-off analysis. Consequently, the methodology should be discussed in view of how it is easy and understandable for trade-off analysis.
In cases with two or three objective functions, the set of Pareto optimal solutions in the objective function space (i.e., Pareto frontier) can be depicted relatively easily. Seeing Pareto frontiers, we can grasp the trade-off relation among objectives totally. Therefore, it would be the best way to depict Pareto frontiers in cases with two or three objectives. (It might be difficult to read the trade-off relation among objectives with three dimension, though). In cases with more than three objectives, however, it is impossible to depict Pareto forntier. Under this circumstance, interactive methods can help us to make local trade-off analysis showing a ``certain" Pareto optimal solution. A number of methods differing in which Pareto optimal solution is to be shown, have been developed. This paper discusses critical issues among those methods for multi-objective optimization, in particular applied to engineering design problems.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.13/DagSemProc.04461.13.pdf
Multi-Objective Optimization
Interactive Multi-Objective Optimization
Evolutionary Algorithms
Pareto Frontier
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
2
10.4230/DagSemProc.04461.14
article
Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms
Fonseca, Carlos M.
Fleming, Peter J.
In this talk, fitness assignment in multiobjective evolutionary algorithms
is interpreted as a multi-criterion decision process. A suitable decision
making framework based on goals and priorities is formulated in terms of a
relational operator, characterized, and shown to encompass a number of
simpler decision strategies, including constraint satisfaction,
lexicographic optimization, and a form of goal programming. Then, the
ranking of an arbitrary number of candidates is considered, and the effect
of preference changes on the cost surface seen by an evolutionary algorithm
is illustrated graphically for a simple problem.
The formulation of a multiobjective genetic algorithm based on the proposed
decision strategy is also discussed. Niche formation techniques are used to
promote diversity among preferable candidates, and progressive articulation
of preferences is shown to be possible as long as the genetic algorithm can
recover from abrupt changes in the cost landscape.
Finally, an application to the optimization of the low-pressure spool speed
governor of a Pegasus gas turbine engine is described, which illustrates how
a technique such as the Multiobjective Genetic Algorithm can be applied, and
exemplifies how design requirements can be refined as the algorithm runs.
The two instances of the problem studied demonstrate the need for preference
articulation in cases where many and highly competing objectives lead to a
non-dominated set too large for a finite population to sample effectively.
It is shown that only a very small portion of the non-dominated set is of
practical relevance, which further substantiates the need to supply
preference information to the GA.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.14/DagSemProc.04461.14.pdf
Evolutionary algorithms
multiobjective optimization
preference articulation
interactive optimization.
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
22
10.4230/DagSemProc.04461.15
article
NBI and MOGA-II, two complementary algorithms for Multi-Objective optimizations
Rigoni, Enrico
Poles, Silvia
The NBI-NLPQLP optimization method is tested on several multi-objective optimization problems. Its performance is compared to that of MOGA-II: since NBI-NLPQLP is based on the classical gradient-based NLPQLP, it is fast and accurate, but not as robust, in comparison with the genetic algorithm. Furthermore a discontinuous Pareto frontier can give rise to problems in the NBIÃ‚Â´s convergence. In order to overcome this problem, a hybridization technique coupled with a partitioning method is proposed.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.15/DagSemProc.04461.15.pdf
Genetic Algorithms
Normal-Boundary Intersection
Designs optimizations
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
15
10.4230/DagSemProc.04461.16
article
On Continuation Methods for the Numerical Treatment of Multi-Objective Optimization Problems
Schütze, Oliver
Dell'Aere, Alessandro
Dellnitz, Michael
In this report we describe how continuation methods can be used for the numerical treatment of multi-objective optimization problems (MOPs): starting with a given Karush-Kuhn-Tucker point (KKT-point) x of an MOP, these techniques can be applied to detect further KKT-points in the neighborhood of x. In the next step, again further points are computed starting with these new-found KKT-points, and so on. In order to maintain a good spread of these solutions we use boxes for the representation of the computed parts of the solution set. Based on this background, we propose a new predictor-corrector variant, and show some numerical results indicating the strength of the method, in particular in higher dimensions. Further, the data structure allows for an efficient computation of MOPs with more than two objectives, which has not been considered so far in most existing continuation methods.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.16/DagSemProc.04461.16.pdf
multi-objective optimization
continuation
k-manifolds
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
5
10.4230/DagSemProc.04461.17
article
On Properly Pareto Optimal Solutions
Shukla, Pradyumn Kumar
Dutta, Joydeep
Deb, Kalyanmoy
In this paper we study epsilon-proper efficiency in multiobjective optimization. We introduce various new definitions of epsilon-proper efficiency, relate them with existing ones, study various concepts and develop very general necessary optimality conditions for a few of them.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.17/DagSemProc.04461.17.pdf
Proper efficiency
epsilon solutions
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
12
10.4230/DagSemProc.04461.18
article
Optimizing Surface Profiles during Hot Rolling: A Genetic Algorithms based Multi-objective Analysis
Chakraborti, Nirupam
Kumar, Barrenkala Siva
Babu, Satish V.
Moitra, Sri Subhrangshu
Mukhopadhyay, Ananya
A hot rolled strip produced by any integrated steel plant would require satisfying some stringent requirements of its surface profile. Crown and Flatness are two industrially accepted quantifiers that relate to the geometric tolerances in the rolled strips.
This study attempts to regulate both crown and flatness within an acceptable limit, satisfying more than one objective at a time. Mathematically, this leads to a multi-objective optimization problem where the solution is no longer unique and a family of equally feasible solutions leads to the so called Pareto-Front, where each member is simply as good as the others. To implement this concept in the present context, one needs to realize that the surface deformation, which is ultimately imparted to the rolled sheets, comes from more than one source. The wear of the rolls, their thermal expansion, bending, and also deformation, contribute significantly towards the crown and flatness that is ultimately observed. During this study a detailed mathematical model has been worked out for this process incorporating all of these phenomena. Computation for the Pareto-optimality has been carried out using different forms of biologically inspired Genetic Algorithms, often integrated with an Ant Colony Optimization Scheme. Ultimately the model has been fine tuned for the hot rolling practice in a major integrated steel plant and tested against their actual operational data.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.18/DagSemProc.04461.18.pdf
Rolling
Hot Rolling
Crown
Flatness
Genetic Algorithms
Ant Colony Optimization
Multi-objective Optimization
Pareto Front
Multi-objective Evolut
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
4
10.4230/DagSemProc.04461.19
article
Runtime Analysis of a Simple Multi-Objective Evolutionary Algorithm
Giel, Oliver
Practical knowledge on the design and
application of multi-objective evolutionary
algorithms (MOEAs) is available but well-founded
theoretical analyses of the runtime are rare.
Laumanns, Thiele, Zitzler, Welzel and Deb (2002)
have started such an analysis for two simple
mutation-based algorithms including SEMO.
These algorithms search locally in the
neighborhood of their current population by
selecting an individual and flipping one
randomly chosen bit. Due to its local search
operator, SEMO cannot escape from local optima,
and, therefore, has no finite expected runtime
in general.
In this talk, we investigate the runtime of
a variant of SEMO whose mutation operator
flips each bit independently. It is proven
that its expected runtime is O(n^n) for all
objective functions f: {0,1}^n -> R^m, and
that there are bicriteria problems among the
hardest problem for this algorithm. Moreover,
for each d between 2 and n, a bicriteria
problem with expected runtime Theta(n^d) is
presented. This shows that bicriteria problems
cover the full range of potential runtimes of
this variant of SEMO. For the problem LOTZ
(Leading-Ones-Trailing Zeroes), the runtime
does not increase substantially if we use the
global search operator. Finally, we consider
the problem MOCO (Multi-Objective-Counting-Ones).
We show that the conjectured bound O((n^2)log n)
on the expected runtime is wrong for both
variants of SEMO. In fact, MOCO is almost a
worst case example for SEMO if we consider
the expected runtime; however, the runtime is
O((n^2)log n) with high probability. Some
ideas from the proof will be presented.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.19/DagSemProc.04461.19.pdf
Runtime analysis
multi-objecive evolutionary algorithms
eng
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Dagstuhl Seminar Proceedings
1862-4405
2005-08-10
4461
1
14
10.4230/DagSemProc.04461.20
article
Visualization of Global Trade-Offs in Aerodynamic Problems by ARMOGAs
Sasaki, Daisuke
Obayashi, Shigeru
Trade-offs is one of important elements for engineering design problems characterized by multiple conflicting design objectives to be simultaneously improved.
In many design problems such as aerodynamic design, due to computational reasons, only a limited number of evaluations can be allowed for industrial use.
Efficient MOEAs, Adaptive Range Multi-Objective Genetic Algorithms (ARMOGAs), to identify trade-offs using a small number of function evaluations have been developed.
In this study, ARMOGAs are applied to aerodynamic designs problems to identify trade-offs efficiently.
In addition to identify trade-offs, trade-off analysis is also important to obtain useful knowledge about the design problem.
To analyze the high-dimensional data of aerodynamic optimization problem, Self-Organizing Maps are applied to understand the trade-offs.
https://drops.dagstuhl.de/storage/16dagstuhl-seminar-proceedings/dsp-vol04461/DagSemProc.04461.20/DagSemProc.04461.20.pdf
Aerodynamic optimization
MOEA
SOM
trade-off analysis