A new self-adaptive algorithm based on the frequency signal of the moving transmitter was proposed and tested by the experiment, which could identify automatically the dynamic multi-interfaces of gas/oil and oil/water...
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A new self-adaptive algorithm based on the frequency signal of the moving transmitter was proposed and tested by the experiment, which could identify automatically the dynamic multi-interfaces of gas/oil and oil/water in the crude oil tank, and solve three tough problems to affect accurate measurement. The first, the separate process of the crude oil and water is dynamic, the interface changes inconstantly in that the input time of the crude oil with water is random and the separate process is inconstant and real-time. The second, the composition of higher density crude oil with demulsifiers is complex. The third, the demand of safety is very strict in oil field. The new self-adaptive algorithm based on the least square method is linear and recursive. The processing result of the algorithm is decided by the relative signal of the transmitter without the relationship to the absolute signal, which avoids the dependence on the absolute performance of the transmitter and the effect of the transmitter covered with the crude oil. The simple and low-cost transmitter based on the parallel plate capacitance sensor was chosen;the algorithm also can distinguish the multi-interfaces of oil/water and the pseudo-interface. The experimental data shows the interface position identified by the algorithm is accurate.
Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These pr...
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Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.
In a multimodal optimization task, the main purpose is to find multiple optimal solutions, so that the user can have a better knowledge about different optimal solutions in the search space and as and when needed, the...
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ISBN:
(纸本)9783642172977
In a multimodal optimization task, the main purpose is to find multiple optimal solutions, so that the user can have a better knowledge about different optimal solutions in the search space and as and when needed, the current solution may be replaced by another optimum solution. Recently, we proposed a novel and successful evolutionary multi-objective approach to multimodal optimization. Our work however made use of three different parameters which had to be set properly for the optimal performance of the proposed algorithm. In this paper, we have eliminated one of the parameters and made the other two self-adaptive. This makes the proposed multimodal optimization procedure devoid of user specified parameters (other than the parameters required for the evolutionary algorithm). We present successful results on a number of different multimodal optimization problems of upto 16 variables to demonstrate the generic applicability of the proposed algorithm.
Among the penalty based approaches for constrained optimization, Augmented Lagrangian (AL) methods are better in at least three ways: (i) they have theoretical convergence properties, (ii) they distort the original ob...
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ISBN:
(纸本)9783642175626
Among the penalty based approaches for constrained optimization, Augmented Lagrangian (AL) methods are better in at least three ways: (i) they have theoretical convergence properties, (ii) they distort the original objective function minimally to allow a better search behavior, and (iii) they can find the optimal Lagrange multiplier for each constraint as a by-product of optimization. Instead of keeping a constant penalty parameter throughout the optimization process, these algorithms update the parameters adaptively so that the corresponding penalized function dynamically changes its optimum from the unconstrained minimum point to the constrained minimum point with iterations. However, the flip side of these algorithms is that the overall algorithm is a serial implementation of a number of optimization tasks, a process that is usually time-consuming. In this paper, we devise a genetic algorithm based parameter update strategy to a particular AL method. The strategy is self-adaptive in order to make the overall genetic algorithm based augmented Lagrangian (GAAL) method parameter-free. The GAAL method is applied to a number of constrained test problems taken from the EA literature. The function evaluations required by GAAL in many problems is an order or more lower than existing methods.
Accurate day-ahead electricity price forecasting (DEPF) has significant meanings in deregulated electrical power market due to its profitable function for all the participants to make reasonable decisions during the m...
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ISBN:
(纸本)9781424428946
Accurate day-ahead electricity price forecasting (DEPF) has significant meanings in deregulated electrical power market due to its profitable function for all the participants to make reasonable decisions during the market business activities. However, the DEPF with satisfactory precision is difficult to be gained because of the violent volatility of electricity price caused by many factors. In this study, a multilayer perceptron artificial neural networks model is constructed for the DEPF in spot market of Nord Pool which is one of the most successful electrical power markets in the world. The major influencing factors are chosen by statistical methods called auto correlation function (ACF) and cross correlation function (CCF), and the standard error back-propagation algorithm is improved by using self-adaptive learning rate and self-adaptive momentum coefficient algorithm to make the training process more efficient both in global optimization and time saving. The most suitable structure of the network is determined by a trial-and-error experiment minimizing MAPE and MSRE of the network and several commonly used error indicators are employed to evaluate the goodness of fit performance of the model. The case study indicates that the DEPF of the proposed model is more reasonable and accurate, comparing with that of traditional ARIMA model.
This paper presents a new scheme of time stepping for solving non-linear viscoelastic problems with a two-level expanding technique. By expanding variables at a discretized time interval, a non-linear coupled space/ti...
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This paper presents a new scheme of time stepping for solving non-linear viscoelastic problems with a two-level expanding technique. By expanding variables at a discretized time interval, a non-linear coupled space/time domain problem with initial and boundary values can be converted into a series of recursive linear boundary value problems, the variations of variables can be described more precisely via a self-adaptive computing procedure, and the non-linear iteration can be avoided. FEM is employed to solve recursive linear boundary value problems, and numerical comparisons are made to validate the proposed algorithm. (C) 2004 Elsevier Ltd. All rights reserved.
This paper presents an attempt to transform PSO into a self-adaptive algorithm based on specific swarm-inspired operators. New features are introduced: spatial expansion intended to overcome premature convergence (an ...
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ISBN:
(纸本)0780393635
This paper presents an attempt to transform PSO into a self-adaptive algorithm based on specific swarm-inspired operators. New features are introduced: spatial expansion intended to overcome premature convergence (an algorithm called Improved PSO, EPSO) and auto-adaptation (an algorithm called adaptive PSO, APSO). Experiments show that APSO and IPSO outperform the basic PSO on benchmark problems, proving their efficiency especially on multimodal functions.
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