Complexity and variety of modern multiobjective optimisation problems result in the
emergence of numerous search techniques, from traditional mathematical programming to …

In recent years interest in multiobjective optimization has flourished. Many Quality Measures
(QM) have been developed to allow comparison of results gained by many methods …

The present chapter aims to serve as a brief introduction for the rest of the chapters in this
volume. The main goal is to provide a general overview of multi-objective combinatorial …

Multiobjective optimization deals with solving problems having not only one, but multiple,
often conflicting, criteria. Such problems can arise in practically every field of science …

Multi-objective optimization procedures usually proceed by iteratively producing new
solutions. For this purpose, a key issue is to determine and efficiently update the search …

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 …

In almost no other field of computer science, the idea of using bio-inspired search paradigms
has been so useful as in solving multiobjective optimization problems. The idea of using a …

An important issue in multiobjective optimization is the quantitative comparison of the
performance of different algorithms. In the case of multiobjective evolutionary algorithms, the …

Measuring the performance of an algorithm for solving multiobjective optimization problem
has always been challenging simply due to two conflicting goals, ie, convergence and …

Given a finite set N of feasible points of a multi-objective optimization (MOO) problem, the
search region corresponds to the part of the objective space containing all the points that are …