نتایج جستجو برای: multi objective optimisation
تعداد نتایج: 1003519 فیلتر نتایج به سال:
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...
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...
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...
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...
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...
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...
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 ...
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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