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Metaheuristics for Multiobjective Optimisation Xavier Gandibleux

Metaheuristics for Multiobjective Optimisation By Xavier Gandibleux

Metaheuristics for Multiobjective Optimisation by Xavier Gandibleux

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The success of metaheuristics on hard single-objective optimization problems is well recognized today. However, many real-life problems require taking into account several conflicting points of view corresponding to multiple objectives.

Metaheuristics for Multiobjective Optimisation Summary

Metaheuristics for Multiobjective Optimisation by Xavier Gandibleux

A large number of real-life optimisation problems can only be realistically modelled with several~often conflicting~objectives. This fact requires us to abandon the concept of "optimal solution" in favour of vector optimization notions dealing with "efficient solution" and "efficient set". To solve these challenging multiobjective problems, the metaheuristics community has put forward a number of techniques commonly referred to as multiobjective meta- heuristics (MOMH). By its very nature, the field of MOMH covers a large research area both in terms of the types of problems solved and the techniques used to solve these problems. Its theoretical interest and practical applicability have attracted a large number of researchers and generated numerous papers, books and spe- cial issues. Moreover, several conferences and workshops have been organised, often specialising in specific sub-areas such as multiobjective evolutionary op- timisation. The main purpose of this volume is to provide an overview of the current state-of-the-art in the research field of MOMH. This overview is necessar- ily non-exhaustive, and contains both methodological and problem-oriented contributions, and applications of both population-based and neighbourhood- based heuristics. This volume originated from the workshop on multiobjective metaheuristics that was organised at the Carre des Sciences in Paris on November 4-5, 2002. This meeting was a joint effort of two working groups: ED jME and PM20.

Table of Contents

I Methodology.- A Tutorial on Evolutionary Multiobjective Optimization.- 2 Bounded Pareto Archiving: Theory and Practice.- 3 Evaluation of Multiple Objective Metaheuristics.- 4 An Introduction to Multiobjective Metaheuristics for Scheduling and Timetabling.- II Problem-oriented Contributions.- 5 A Particular Multiobjective Vehicle Routing Problem Solved by Simulated Annealing.- 6 A Dynasearch Neighborhood for the Bicriteria Traveling Salesman Problem.- 7 Pareto Local Optimum Sets in the Biobjective Traveling Salesman Problem: An Experimental Study.- 8 A Genetic Algorithm for Tackling Multiobjective Job-shop Scheduling Problems.- 9 RPSGAe - Reduced Pareto Set Genetic Algorithm: Application to Polymer Extrusion.

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Metaheuristics for Multiobjective Optimisation by Xavier Gandibleux
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
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