This paper investigates how intrusion detection system (IDS) sensors should best be placed on a network when there are several competing evaluation criteria. This is a computationally difficult problem and we show how Multi-Objective Genetic Algorithms provide an excellent means of searching for optimal placements.

Redundancy allocation is a widely used method for the reliability improvement of complex embedded systems in the automotive domain. The allocation of redundant components can affect other nonfunctional quality attributes of the system, which are as important as reliability and very often conflicting with each other, such as cost and response time. Consequently, to find the good system design op...

Several time delay estimates have been reported for the quasar Q0957+561. They come from distinct data sets and published separately. This paper presents a methodology to estimate a single time delay given several data sets by using multi-objective optimisation. We use General Regression Neural Networks (GRNN) to estimate the time delay, which is one of the most accurate time delay estimators –...

The dividing wall column is experiencing a growing interest in industry. The concern about difficult controllability vanishes further and this leads to growing demand of approved guidelines for the design which is indeed time consuming if no expert knowledge is available. In this contribution the design task of a dividing wall column is treated as multi objective optimisation. Backbone of this ...

This paper proposes a new multi-objective approach for the problem of power restoration in (n-1) contingency situations. It builds on a previous, mono-objective approach introduced in Mendes et al. (2010) [14]. Power restoration normally relies on network reconfiguration, and typically involves re-switching and adjustment of tap-changers and capacitor banks. In this work, we focus on re-switchi...

This paper describes a new Evolutionary Programming optimisation algorithm and a method of its application to multi-objective optimisation problems. Computational results are presented demonstrating the algorithm’s ability to find Paretooptimal solutions for a real-world problem in radiofrequency component design.

Many real-world engineering design problems involve the satisfaction of multiple conflicting objectives. In this case it is unlikely that a single ideal solution will exist. Instead, the solution of an Multi-Objective Optimisation problem will lead to a family of Pareto optimal points, where any improvement in one objective will result in the degradation of one or more of the other objectives. ...

A large number of methods for solving multiobjective optimisation (MOO) problems have been developed. To compare these methods rigorously, or to measure the performance of a particular MOO algorithm quantitatively, a variety of performance indices (PIs) have been proposed. This paper provides an overview of the various PIs and attempts to categorise them into a certain number of classes accordi...

As the name suggests, multi-objective optimization involves optimizing a number of objectives simultaneously. The problem becomes challenging when the objectives are of conflict to each other, that is, the optimal solution of an objective function is different from that of the other. In solving such problems, with or without the presence of constraints, these problems give rise to a set of trad...

The paper presents the development of a multi-objective optimisation framework to study the effects that preliminary design choices have on the demisability and the survivability of a spacecraft. Building a spacecraft such that most of it will demise during the re-entry through design-for-demise strategies may lead to design that are more vulnerable to space debris impacts, thus compromising th...