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Surrogate modelling and model management are key points for evolutionary optimization of chemical processes. This paper proposes an evolutionary algorithm with the help of adaptive surrogate functions (EASF), in which approximate models' establishment and management are combined to search the optimal result. To construct an appropriate surrogate model, a new hybrid modelling framework with adaptive...
The problem of determining simultaneously the model order and coefficient of an Autoregressive Moving Average (ARMA) model is examined in this paper. An Evolutionary Algorithm (EA) comprising two-level Differential Evolution (DE) optimization scheme is proposed. The first level searches for the appropriate model order while the second level computes the optimal/sub-optimal corresponding parameters...
This work focuses on the development of a parallel framework method to improve the effectiveness and the efficiency of the obtained solutions by Multi-objective Evolutionary Algorithms. Specifically, a parallel architecture based on JavaSpaces technology and an island paradigm model is proposed and tested on two important and complex computational problems: The Protein Structure Prediction and the...
Advanced planning and scheduling (APS) refers to a manufacturing management process by which raw materials and production capacity are optimally allocated to meet demand. How to formulate the complex problems of APS and find satisfactory solutions play an important role in manufacturing systems. In this paper, we propose a scheduling formulation method by combining the graph theory and combinatorial...
Modeling tools used to analyze power supply systems usually rely on detailed and complex models, thus taking a long simulation time. Due to the acceleration of time to market of today's computing platforms, it is required reach at a feasible solution in a short amount of time. This paper describes a toolkit developed to improve the design flow power systems with a large number of loads. The main characteristic...
Estimation of Distribution Algorithm is a new population based evolutionary optimization method and it generates new population from probability distribution model. Like most evolutionary algorithms, it is easy to trap into local optimums. In order to avoid this shortcoming, Gaussian and Cauchy probability density function are mixed as probability distribution model. For continuous problems, a new...
Gaussian process model is an effective and efficient method for approximating a continuous function. However, its computational cost increases exponentially with the size of training data set. A very popular way to alleviate this shortcoming is to cluster the whole training data set into a number of small clusters and then a local model is built for each cluster. However, widely used crisp clustering...
Much of the computational complexity in employing evolutionary algorithms as optimization tool is due to the fitness function evaluation that may either not exist or be computationally very expensive. With the proposed approach, the expensive fitness evaluation step is replaced by an approximate model. An intelligent guided technique via an adaptive fuzzy similarity analysis for fitness granulation...
Evolutionary optimization of expensive functions typically uses a metamodel, i.e. a computationally cheaper but inaccurate approximation of the objective function. The success of the optimization search depends on the accuracy of the metamodel hence an integral part of the metamodelling framework is assessing the metamodel accuracy. In this paper we survey a range of accuracy assessment methods such...
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