نتایج جستجو برای: multi objective optimisation

تعداد نتایج: 1003519  

2010
Juan Pedro Castro Gutiérrez Dario Landa Silva José A. Moreno-Pérez

There is a variety of methods for ranking objectives in multiobjective optimization and some are difficult to define because they require information a priori (e.g. establishing weights in a weighted approach or setting the ordering in a lexicographic approach). In manyobjective optimization problems, those methods may exhibit poor diversification and intensification performance. We propose the...

2011
Lijie Cui Jakin Ravalico George Kuczera Graeme Dandy Holger Maier

2005
David Brown François Maréchal Georges Heyen Jean Paris

Multi-objective optimisation (MOO) has been used with an equation solver data reconciliation software to develop a tool for sensor system design based on modifying the sensitivity matrix of a simulated process. MOO enables searching for the best tradeoff between two conflicting objectives: the cost of the system and the precision of key performance indicators (KPI) (variables that have to be me...

2004
Daisuke Sasaki Shahrokh Shahpar Shigeru Obayashi

Adaptive Range Multi-Objective Genetic Algorithm (ARMOGA) has been developed to obtain trade-offs more efficiently than conventional Multi-Objective Evolutionary Algorithms. In this paper, the performance of ARMOGA is demonstrated through a multiobjective design optimisation of Bypass Fan Outlet Guide Vanes as part of the Low Pressure Compression (LPC) system. In the present optimisation, the o...

Journal: :CoRR 2004
Martin Josef Geiger

Various local search approaches have recently been applied to machine scheduling problems under multiple objectives. Their foremost consideration is the identification of the set of Pareto optimal alternatives. An important aspect of successfully solving these problems lies in the definition of an appropriate neighbourhood structure. Unclear in this context remains, how interdependencies within...

2003
Simon Huband Luigi Barone

Evolutionary algorithms have been applied with great success to the difficult field of multi-objective optimisation. Nevertheless, the need for improvements in this field is still strong. We present a new evolutionary algorithm, ESP (the Evolution Strategy with Probabilistic mutation). ESP extends traditional evolution strategies in two principal ways: it applies mutation probabilistically in a...

2011
Andrew Lewis Sanaz Mostaghim

Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multiobjective problems have also been the subject of intensive investigation and development recently for metaheuristic search algorithms such as ant colony optimisation, particle swarm optimisation and extremal optimisation. In this chapter, a unifying framework...

2009
Noël-Ann Bradshaw Constantinos Ierotheou Kevin Parrott

The use of heuristic evolutionary algorithms to address the problem of portfolio optimisation has been well documented. In order to decide which assets to invest in and how much to invest, one needs to assess the potential risk and return of different portfolios. This problem is ideal for solving using a Multi-Objective Evolutionary Algorithm (MOEA) that maximises return and minimises risk. We ...

2003
Tatsuya Okabe Yaochu Jin Bernhard Sendhoff

For tackling multi-objective optimisation (MOO) problem, many methods are available in the field of evolutionary computation (EC). To use the proposed method(s), the choice of the representation should be considered first. In EC, often binary representation and real-valued representation are used. In this paper, we propose a hybrid representation, composed of binary and real-valued representati...

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