Rayleigh wave dispersion curves and refraction travel times are jointly inverted through a procedure based on a multi-objective Evolutionary Algorithm (MOEA) technique. The proposed procedure aims at improving the rec...
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Rayleigh wave dispersion curves and refraction travel times are jointly inverted through a procedure based on a multi-objective Evolutionary Algorithm (MOEA) technique. The proposed procedure aims at improving the reconstruction of subsurface structure by exploiting the complementary information attainable by refraction seismics and surface-wave dispersion and by overcoming in this way the problems related to non-uniqueness of the solution (surface waves and refraction seismics) and hidden layers (refraction). The proposed scheme allows the joint inversion of the data and the validation of the provisional interpretation. In fact, Pareto front symmetry proves to be a valuable tool to verify the coherency of the adopted interpretation as an incorrect number of layers, refractor attribution or assumed Poisson values reflect in non-symmetric Pareto front as well as in wider model distribution in the objective space. Methodology is initially tested using synthetic data and successfully applied to a field dataset resulting from a single standard seismic survey with vertical geophones and vertically-incident seismic source (sledgehammer). (C) 2008 Elsevier B.V. All rights reserved.
Due to the character of seismic energy generation and propagation, shallow high-resolution seismic-reflection surveys often fail in the identification of the shallowest horizons and, due to the limited offsets, accura...
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Due to the character of seismic energy generation and propagation, shallow high-resolution seismic-reflection surveys often fail in the identification of the shallowest horizons and, due to the limited offsets, accuracy of velocity analyses is often not very high. In recent years, Rayleigh wave dispersion analysis have proved to have good potential also for near-surface applications but dispersion curve inversion and related uncertainty evaluation pose serious problems to a completely stand-alone application. In order to overcome these problems a joint inversion scheme is proposed, which is based on the identification of the Pareto front, performed in the framework of a multi-objective Evolutionary Algorithm (MOEA). Seismic data considered to design the two objectives are the Rayleigh wave dispersion curve and reflection travel times. We initially analyse a set of synthetic cases and evaluate the obtained results. A significant improvement of the retrieved models is observed as long as reflection travel times are added to the dispersion curve alone. Furthermore, the proposed methodology also provides relevant indications about the consistency of the overall inversion process. In fact, the distribution of the models in the objective space, the trend of the objectives over the passing generations and the evolution of the Pareto front can provide useful information to evaluate the provisional tentative interpretation (number of strata and reflector identification) inherently adopted for the data inversion. On the basis of the results obtained from the tests on the synthetic datasets, the analyses of a field dataset are interpreted as possible evidence of lateral heterogeneities. (C) 2006 Elsevier B.V All rights reserved.
"Finite Resource"has a long story,and it is also a hard-solving problem in each type of *** to schedule production effectively and efficiently and make sure of using resources properly is very important for ...
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"Finite Resource"has a long story,and it is also a hard-solving problem in each type of *** to schedule production effectively and efficiently and make sure of using resources properly is very important for companies to keep competitive *** the real situation, senior managers and decision makers often have to face multi-objective *** a famous knit fabric manufacturer in Taiwan for *** managers schedule production within some limited conditions including capacity balance,delivery time,and consumers' *** is usually not easy to find a solution which can satisfy multiple objectives *** the past,senior managers usually schedule production by their own experiences and this is generally *** also often makes some mistakes if decision makers are influenced by some uncontrollable ***,they need a useful method to help them to schedule production properly and efficiently. The aim of this paper is to efficiently find good solutions to production scheduling of knit *** production of knit fabrics is characterized by multiple knit machines,which are divided into several types and each type includes many machines,processing a variety of different jobs arriving at the machine *** important characterization is that an additional setup time is required if a wrong sequence in which lighter-colored fabrics are produced followed by darker-colored ones *** colors of knit fabrics are categorized into 5 main *** objectives in this study include minimum production cost and minimum makespan. In this paper,we use Self-Adaptive Genetic Algorithm (SAGA)as an analytic tool because SAGA is a useful tool for analyzing complex *** comparison,we will also use some other heuristic rules or approximation algorithms to solve the multi-objective *** the end,we will use visualized charts and tables to show the results so that users who do not have professional knowledge can sch
multi-objective optimization is generally a time consuming step of the design process. In this paper, a Pareto based multi-objective genetic algorithm is proposed, which enables a faster convergence without degrading ...
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multi-objective optimization is generally a time consuming step of the design process. In this paper, a Pareto based multi-objective genetic algorithm is proposed, which enables a faster convergence without degrading the estimated set of solutions. Indeed, the population diversity is correctly conserved during the optimization process;moreover, the solutions belonging to the frontier are equally distributed along the frontier. This improvement is due to an extension function based on a natural phenomenon, which is similar to a cyclical epidemic which happens every N generations (eN-MOGA). The use of this function enables a faster convergence of the algorithm by reducing the necessary number of generations. (c) 2006 Elsevier Ltd. All rights reserved.
At present, the most commonly used method for multiobjective linear, programming (MOLP) is goal programming (GP) based methods but these methods do not always generate efficient solutions. Recently, an efficient GP-ba...
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ISBN:
(纸本)0780376579
At present, the most commonly used method for multiobjective linear, programming (MOLP) is goal programming (GP) based methods but these methods do not always generate efficient solutions. Recently, an efficient GP-based method, which is called reference goal programming (RGP), has been proposed. However, it is limited to only a certain target point preference, which is too rigid. The more flexible preferences are preferred in many practical problems. In this research, an effective linear combination method for MOLP problems with convex polyhedral preference functions is proposed. The concept of the convex cone is used to formulate convex polyhedral preference functions and the existing reference point method (RPM) is integrated to ensure the efficiency of the solution of the problem. The formulated model can be solved by existing linear programming solvers and can find the satisfactory efficient solution. The convex polyhedral function enriches the existing preferences for efficient methods and increases the flexibility in designing preferences for decision makers. For some situations, it is difficult for the decision maker to state the certain aspiration level for each objective function. Fuzzy goals, which can be considered as convex polyhedral preference functions, can be used to represent aspiration levels with respect to linguistic terms.
The hybridization of genetic algorithms(GAs) and Tabu Search(TS) is one of the traditional problems in function optimization in the GA literature. However,most proposed methods so far have utilized GAs to explore glob...
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The hybridization of genetic algorithms(GAs) and Tabu Search(TS) is one of the traditional problems in function optimization in the GA literature. However,most proposed methods so far have utilized GAs to explore global candidates and TS to exploit local *** such methods,this paper discusses new algorithms to directly store individuals into multiple tabu lists during GA-iterations. The paper describes the basic idea,algorithms,experimental results, and their practical applications for social simulation and electric equipments design.
The concept of conditional proper efficiency has been incorporated to develop the duality theory for nonsmooth constrained multiobjective optimization problems where the objective functions. and the constraints are co...
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