Finding good solutions to large scale, hard, global optimization problems, is a demanding task with many relevant applications. It is well known that, in order to tackle a difficult problem, an algorithm has to incorporate all of the available information on the problem domain. However, as we will show in this paper, some general purpose methods and the ideas on which they are built can provide guidance towards the efficient solution of difficult instances. Most of this paper will be devoted to heuristic techniques applied to the prob- lem of finding a minimum energy configuration of a cluster of atoms in R3 . This is a very relevant problem with a large set of applications which has trig- gered considerable research efforts in the last decade. We will show how some relatively simple ideas can be used to generate fairly efficient methods which have been employed to discover many new cluster structures. In this paper we will introduce some of the main algorithmic ideas which have proven to be particularly successful in the field of global optimization applied to atomic cluster conformation problems. We will mainly discuss Basin Hopping meth- ods, as well as their population–based variant, and some specific technique based on “direct moves”. These methods, although designed for the specific problem, have a much wider applicability. In fact, one of the aims of this paper is also that of suggesting that similar ideas can be employed in order to design innovative methods even for totally different global optimization problems, like, e.g., circle packing and space trajectory planning.

Local search based heuristics for global optimization: atomic clusters and beyond / Marco Locatelli; Fabio Schoen. - In: EUROPEAN JOURNAL OF OPERATIONAL RESEARCH. - ISSN 0377-2217. - STAMPA. - 222:(2012), pp. 1-9. [10.1016/j.ejor.2012.04.010]

Local search based heuristics for global optimization: atomic clusters and beyond

SCHOEN, FABIO
2012

Abstract

Finding good solutions to large scale, hard, global optimization problems, is a demanding task with many relevant applications. It is well known that, in order to tackle a difficult problem, an algorithm has to incorporate all of the available information on the problem domain. However, as we will show in this paper, some general purpose methods and the ideas on which they are built can provide guidance towards the efficient solution of difficult instances. Most of this paper will be devoted to heuristic techniques applied to the prob- lem of finding a minimum energy configuration of a cluster of atoms in R3 . This is a very relevant problem with a large set of applications which has trig- gered considerable research efforts in the last decade. We will show how some relatively simple ideas can be used to generate fairly efficient methods which have been employed to discover many new cluster structures. In this paper we will introduce some of the main algorithmic ideas which have proven to be particularly successful in the field of global optimization applied to atomic cluster conformation problems. We will mainly discuss Basin Hopping meth- ods, as well as their population–based variant, and some specific technique based on “direct moves”. These methods, although designed for the specific problem, have a much wider applicability. In fact, one of the aims of this paper is also that of suggesting that similar ideas can be employed in order to design innovative methods even for totally different global optimization problems, like, e.g., circle packing and space trajectory planning.
2012
222
1
9
Marco Locatelli; Fabio Schoen
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/612543
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