The proceedings contain 127 papers from the 2000 IEEE Congress on evolutionary Computation, IEEE CEC 2005. Proceedings: Volume - 2. The topics discussed include: an evolutionary algorithm to design diesel engines;evol...
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ISBN:
(纸本)0780393635
The proceedings contain 127 papers from the 2000 IEEE Congress on evolutionary Computation, IEEE CEC 2005. Proceedings: Volume - 2. The topics discussed include: an evolutionary algorithm to design diesel engines;evolving an agent collective for cooperative mine sweeping;search in linked document space by social topology agents;real parameter optimization using mutation step co-evolution;a grid ant colony algorithm for the orienteering problem;coevolution of neural go players in a cultural environment;genetic programming in economic modelling;a memetic model of product invention;a hybrid approach to parameter tuning in genetic algorithm;and a genetic word clustering algorithm.
Many clustering techniques have been widely developed in order to retrieve, filter, and categorize documents available in the database or even on the Web. The issue to appropriately organize and store the information ...
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ISBN:
(纸本)9789604742011
Many clustering techniques have been widely developed in order to retrieve, filter, and categorize documents available in the database or even on the Web. The issue to appropriately organize and store the information in terms of documents clustering becomes very crucial for the purpose of knowledge discovery and management. In this research, a hybrid intelligent approach has been proposed to automate the clustering process based on the characteristics of each document represented by the fuzzy concept networks. Through the proposed approach, the useful knowledge can be clustered and then utilized effectively and efficiently. In literature, artificial neural network have been widely applied for the document-clustering applications. However, the number of documents is huge so that it is hard to find the most appropriate ANN parameters in order to get the most appropriate clustering results. Traditionally, these parameters are adjusted manually by the way of trial and error so that it is time consuming and doesn't guarantee an optimum result. Therefore, a hybrid approach incorporating an evolutionary computation (EC) approach and a Fuzzy Adaptive Resonance Theory (Fuzzy-ART) neural network has been proposed to adjust the Fuzzy-ART parameters automatically so that the best results of the document clustering can be obtained. The proposed approach is tested by using ninety articles in three different fields. The experimental results show that the proposed hybrid approach could generate the most appropriate parameters of Fuzzy-ART for getting the most desired clusters as expected.
Numerous land surface models exist for predicting water and energy fluxes in the terrestrial environment. These land surface models have different conceptualizations (i.e., process or physics based), together with str...
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Numerous land surface models exist for predicting water and energy fluxes in the terrestrial environment. These land surface models have different conceptualizations (i.e., process or physics based), together with structural differences in representing spatial variability, alternate empirical methods, mathematical formulations and computational approach. These inherent differences in modeling approach, and associated variations in outputs make it difficult to compare and contrast land surface models in a straight-forward manner. While model intercomparison studies have been undertaken in the past, leading to significant progress on the improvement of land surface models, additional framework towards identification of model weakness is needed. Given that land surface models are increasingly being integrated with satellite based estimates to improve their prediction skill, it is practical to undertake model intercomparison on the basis of soil moisture data assimilation. Consequently, this study compares two land surface models: the Joint UK Land Environment Simulator (JULES) and the Community Atmosphere Biosphere Land Exchange (CABLE) for soil moisture estimation and associated assessment of model uncertainty. A retrieved soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission was assimilated into both models, with their updated estimates validated against in-situ soil moisture in the Yanco area, Australia. The findings show that the updated estimates from both models generally provided a more accurate estimate of soil moisture than the open loop estimate based on calibration alone. Moreover, the JULES output was found to provide a slightly better estimate of soil moisture than the CABLE output at both near-surface and deeper soil layers. An assessment of the updated membership in decision space also showed that the JULES model had a relatively stable, less sensitive, and more highly convergent internal dynamics than the CABLE model. Crown Cop
Geometric objects are often represented approximately in terms of a finite set of points in three-dimensional euclidean space. In this paper, we extend this representation to what we call labeled point clouds. A label...
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Geometric objects are often represented approximately in terms of a finite set of points in three-dimensional euclidean space. In this paper, we extend this representation to what we call labeled point clouds. A labeled point cloud is a finite set of points, where each point is not only associated with a position in three-dimensional space, but also with a discrete class label that represents a specific property. This type of model is especially suitable for modeling biomolecules such as proteins and protein binding sites, where a label may represent an atom type or a physico-chemical property. Proceeding from this representation, we address the question of how to compare two labeled points clouds in terms of their similarity. Using fuzzy modeling techniques, we develop a suitable similarity measure as well as an efficient evolutionary algorithm to compute it. Moreover, we consider the problem of establishing an alignment of the structures in the sense of a one-to-one correspondence between their basic constituents. From a biological point of view, alignments of this kind are of great interest, since mutually corresponding molecular constituents offer important information about evolution and heredity, and can also serve as a means to explain a degree of similarity. In this paper, we therefore develop a method for computing pairwise or multiple alignments of labeled point clouds. To this end, we proceed from an optimal superposition of the corresponding point clouds and construct an alignment which is as much as possible in agreement with the neighborhood structure established by this superposition. We apply our methods to the structural analysis of protein binding sites.
evolutionary algorithms are heuristic methods that have yielded promising results for solving nonlinear, nondifferentiable, and multi-modal optimization problems in the power systems area. The differential evolution (...
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evolutionary algorithms are heuristic methods that have yielded promising results for solving nonlinear, nondifferentiable, and multi-modal optimization problems in the power systems area. The differential evolution (DE) algorithm is an evolutionary algorithm that uses a rather greedy and less stochastic approach to problem solving than do classical evolutionary algorithms, such as genetic algorithms, evolutionary programming, and evolution strategies. DE also incorporates an efficient way of self-adapting mutation using small populations. The potentialities of DE are its simple structure, easy use, convergence property, quality of solution, and robustness. This paper proposes a new approach for solving economic load dispatch problems with valve-point effect. The proposed method combines the DE algorithm with the generator of chaos sequences and sequential quadratic programming (SQP) technique to optimize the performance of economic dispatch problems. The DE with chaos sequences is the global optimizer, and the SQP is used to fine-tune the DE run in a sequential manner. The combined methodology and its variants are validated for two test systems consisting of 13 and 40 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. The proposed combined method outperforms other state-of-the-art algorithms in solving load dispatch problems with the valve-point effect. [ABSTRACT FROM AUTHOR]
The main aim of this research article is a parametric demonstration of irreversible Stirling cryogenic refrigerator cycles that includes irreversibilities such as external and internal irreversibilities. In addition, ...
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The main aim of this research article is a parametric demonstration of irreversible Stirling cryogenic refrigerator cycles that includes irreversibilities such as external and internal irreversibilities. In addition, through this study, finite heat capacities of external reservoirs are considered accordingly. To reach the addressed goal of this research, three objective functions that include the input power of the Stirling refrigerator, the coefficient of performance (COP) and cooling load (R-L) have been involved in optimization process simultaneously. The first aforementioned objective function has to minimize;the rest objective functions, on the other hand, have to maximize in parallel optimization process. Developed multi objective evolutionary approaches (MOEAs) based on NSGA-II algorithm is implemented throughout this work. Moreover, cold-side's effectiveness of the heat exchanger, hot-side's effectiveness of the heat exchanger, heat source's heat capacitance rate, heat sink's capacitance rate, temperature ratio (T-h/T-c), temperature of cold side are assigned as decision variables for decision making procedure. To gain a robust decision, different decision making approaches that include TOPSIS, LINMAP and fuzzy Bellman-Zadeh are used. Pareto optimal frontier was determined precisely and then three final outputs have been gained by means of the mentioned decision making approaches. (C) 2014 Elsevier Ltd. All rights reserved.
This paper investigates the Periodic Capacitated Arc Routing Problem (PCARP), which is often encountered in the waste collection application. PCARP is an extension of the well-known Capacitated Arc Routing Problem (CA...
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This paper investigates the Periodic Capacitated Arc Routing Problem (PCARP), which is often encountered in the waste collection application. PCARP is an extension of the well-known Capacitated Arc Routing Problem (CARP) from a single period to a multi-period horizon. PCARP is a hierarchical optimization problem which has a primary objective (minimizing the number of vehicles mnv) and a secondary objective (minimizing the total cost tc). An important factor that makes PCARP challenging is that its primary objective mnv is little affected by existing operators and thus difficult to improve. We propose a new Memetic Algorithm (MA) for solving PCARP. The MA adopts a new solution representation scheme and a novel crossover operator. Most importantly, a Route-Merging (RM) procedure is devised and embedded in the algorithm to tackle the insensitive objective mnv. The MA with RM (MARM) has been compared with existing meta-heuristic approaches on two PCARP benchmark sets and a real-world data set. The experimental results show that MARM obtained better solutions than the compared algorithms in much less time, and even updated the best known solutions of all the benchmark instances. Further study reveals that the RM procedure plays a key role in the superior performance of MARM.
Differential evolution (DE) is a simple but powerful evolutionary optimization algorithm with continually outperforming many of the already existing stochastic and direct search global optimization techniques. DE algo...
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Differential evolution (DE) is a simple but powerful evolutionary optimization algorithm with continually outperforming many of the already existing stochastic and direct search global optimization techniques. DE algorithm is a new optimization method that can handle non-differentiable, non-linear, and multi-modal objective functions. This paper presents an efficient modified differential evolution (MDE) algorithm for solving optimal power flow (OPF) with non-smooth and non-convex generator fuel cost curves. Modifications in mutation rule are suggested to the original DE algorithm, that enhance its rate of convergence with a better solution quality. A six-bus and the IEEE 30 bus test systems with three different types of generator cost curves are used for testing and validation purposes. Simulation results demonstrate that MDE algorithm provides very remarkable results compared to those reported recently in the literature. (C) 2008 Elsevier Ltd. All rights reserved.
Artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligence algorithms that is widely used for optimization purposes in static environments. However, numerous real-world problems are dynam...
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Artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligence algorithms that is widely used for optimization purposes in static environments. However, numerous real-world problems are dynamic and uncertain, which could not be solved using static approaches. The contribution of this paper is twofold. First, a novel AFSA algorithm, so called NAFSA, has been proposed in order to eliminate weak points of standard AFSA and increase convergence speed of the algorithm. Second, a multi-swarm algorithm based on NAFSA (mNAFSA) was presented to conquer particular challenges of dynamic environment by proposing several novel mechanisms including particularly modified multi-swarm mechanism for finding and covering potential optimum peaks and diversity increase mechanism which is applied after detecting an environment change. The proposed approaches have been evaluated on moving peak benchmark, which is the most prominent benchmark in this domain. This benchmark involves several parameters in order to simulate different configurations of dynamic environments. Extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of the tested dynamic environments modeled by moving peaks benchmark. (C) 2014 Elsevier B.V. All rights reserved.
作者：
Tian, JLNASA
Langley Res Ctr Sci Appl Int Corp Hampton VA 23681 USA Georgia Inst Technol
Sch Elect & Comp Engn Atlanta GA 30332 USA
We propose an algorithm for reconstructing nonuniformly sampled interferograms when sampling locations are unknown. An optimization problem with multiple objective and constraint functions is designed based on the spa...
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We propose an algorithm for reconstructing nonuniformly sampled interferograms when sampling locations are unknown. An optimization problem with multiple objective and constraint functions is designed based on the spatial and spectral characteristics of the data measurement. This problem is solved using an evolutionary approach, in which potential solutions are competing to be the fittest individual in a simulated natural environment. (c) 2005 Society of Photo-Optical Instrumentation Engineers.
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