Christian Blume

Pareto Optimization or Cascaded Weighted Sum: A Comparison of Concepts

Looking at articles or conference papers published since the turn of the century, Pareto optimization is the dominating assessment method for multi-objective nonlinear optimization problems. However, is it always the method of choice for real-world applications, where either more than four objectives have to be considered, or the same type of task is repeated again and again with only minor modifications, in an automated optimization or planning process? This paper presents a classification of application scenarios and compares the Pareto approach with an extended version of the weighted sum, called cascaded weighted sum, for the different scenarios. Its range of application within the field of multi-objective optimization is discussed as well as its strengths and weaknesses.

Concept of Action

It is essential that part of a robot program deals with the execution of physical actions. That is to say that a program must not only do something with its data, but also control the robot moves and gripper actions. The traditional program flow control in Pascal is done using statements, while robot and gripper control is performed by system procedure calls.

Towards a Generally Applicable Self-Adapting Hybridization of Evolutionary Algorithms

Practical applications of Evolutionary Algorithms (EA) frequently use some sort of hybridization by incorporating domain-specific knowledge, which turns the generally applicable EA into a problem-specific tool. To overcome this limitation, the new method of HyGLEAM was developed and tested extensively using eight test functions and three real-world applications. One basic kind of hybridization turned out to be superior and the number of evaluations was reduced by a factor of up to 100.

GLEAM - General Learning Evolutionary Algorithm and Method : ein Evolutionärer Algorithmus und seine Anwendungen

Nach einer grundlegenden Einfuhrung wird der Evolutionare Algorithmus GLEAM ausfuhrlich vorgestellt. Das breite Anwendungspotential dieses Optimierungs- und Planungsverfahrens wird durch eine Reihe von Anwendungsbeispielen aus den Bereichen Robotik, Scheduling, Bauindustrie und Designoptimierung unterstrichen. Dabei werden auch Weiterentwicklungen behandelt, die Heuristiken und lokale Suche in das evolutionare Verfahren integrieren, so dass ein hybrider oder memetischer Algorithmus entsteht.

Verbesserte Planung und Optimierung mit Hilfe eines erweiterten Genetischen Algorithm

In Produktion und Fertigung gibt es eine Vielzahl von Aufgabenstellungen, fur die eine Optimierung unter wirtschaftlichen Gesichtspunkten notwendig oder zumindest wunschenswert ist. Andererseits sind die Randbedingungen und Anforderungen an den Einsatz von Werkzeugen zur Planung und Optimierung gerade in der industriellen Praxis sehr hoch, sodas vor allem neue, kombinierte Techniken einen erfolgversprechenden Ansatz darstellen. Das hier vorgestellte Verfahren gelangt durch die Verbindung von Simulation, Genetischen Algorithmen und Parallelverarbeitung zu einer neuen Qualitat. Zwei Anwendungsbeispiele aus dem Gebiet der Resourcenplanung demonstrieren die Tauglichkeit der neuen Methode: In einem industriellen Anwendungsfall konnten Spareffekte von uber 40% im Vergleich zur state-of-the-art-Planung erreicht werden.

How to Use the PASRO-System

The the user should first of all be familar with his own Pascal system before becoming acquainted with some additional rules in order to write down a PASRO program and link it to produce an executable program.

Concept of Data

The information a program is working on is called data, in very general term. On a basic machine level, data are represented by a sequence of binary gigits, called bits. To distinguish values associated with the same bit string, additional information is required. This is called the data type definition and describes the set of values the variable of a certain type can hold. For example, the 16 bit string

Input/Output in PASRO/C

0000000000110101