Shuffled Complex Evolution Approach Research Articles

OPTIMIZING THE CONTAINER TRUCK PATHS WITH UNCERTAIN TRAVEL TIME IN CONTAINER PORTS

The yard template problem in container ports determines the assignment of space to store containers for the vessels, which could impact container truck paths. Actually, the travel time of container truck paths is uncertain. This paper considers the uncertainty from two perspectives: (1) the yard congestion in the context of yard truck interruptions, (2) the correlation among adjacent road sections (links). A mixed-integer programming model is proposed to minimize the travel time of container trucks. The reliable shortest path, which takes the correlation among links into account is firstly discussed. To settle the problem, a Shuffled Complex Evolution Approach (SCE-UA) algorithm is designed to work out the assignment of yard template, and the A* algorithm is presented to find the reliable shortest path according to the port operator’s attitude. In our case study, one yard in Dalian (China) container port is chosen to test the applicability of the model. The result shows the proposed model can save 9% of the travel time of container trucks, compared with the model without considering the correlation among adjacent links.

Shuffled Complex Evolution Approach for Load Balancing of Gateways in Wireless Sensor Networks

Energy consumption is one of the important factor of Wireless Sensor Networks (WSN). It has much attention in many fields. From recent studies, it is observed that energy consumption in WSN is challenging task as the energy is limited resource. This energy is needed for sensor nodes operation. In order to maximize the network lifetime, energy consumption should be mitigated. In cluster based WSN, cluster head i.e., the leader of cluster performs various activities, such as data collection from its member nodes, data aggregation and data exchange with base station. Hence, load balancing in WSNs is one of the challenging task to maximize network lifetime. In order to address this problem, in this paper, Shuffled Complex Evolution (SCE) algorithm is used. A novel fitness function is also designed to evaluate fitness of solutions produced by SCE algorithm. In SCE, the solutions with best and worst fitness value exchange their information to produce new off-spring. We have simulated proposed load balancing algorithm along with other state-of-the-art load balancing algorithms, namely Node Local Density Load Balancing, Score Based Load Balancing, Simple Genetic Algorithm based load balancing, Novel Genetic Algorithm based Load Balancing. It is observed from experimental results that proposed load balancing algorithm outperforms state-of-the-art load balancing algorithms in terms of load balancing, energy consumption, execution time, number of sensor nodes and number of heavy loaded sensor nodes.

A water balance approach for reconstructing streamflow using tree-ring proxy records

• Seasonal water balance models performed well in reproducing instrumental streamflow. • Seasonal PRISM and proxy precipitation and temperature inputs were used with models. • Annual reconstructions did not reproduce observations as well as seasonal ones. • The method also enabled reconstruction of streamflows, SWE and evapotranspiration. • A water balance method is useful for assessing uncertainty in proxy reconstructions. Tree-ring data have been used to augment limited instrumental records of climate and provide a longer view of past variability, thus improving assessments of future scenarios. For streamflow reconstructions, traditional regression-based approaches cannot examine factors that may alter streamflow independently of climate, such as changes in land use or land cover. In this study, seasonal water balance models were used as a mechanistic approach to reconstruct streamflow with proxy inputs of precipitation and air temperature. We examined a Thornthwaite water balance model modified to have seasonal components and a simple water balance model with a snow component. These two models were calibrated with a shuffled complex evolution approach using PRISM and proxy seasonal temperature and precipitation to reconstruct streamflow for the upper reaches of the West Walker River basin at Coleville, CA. Overall, the modified Thornthwaite model performed best during calibration, with R 2 values of 0.96 and 0.80 using PRISM and proxy inputs, respectively. The modified Thornthwaite model was then used to reconstruct streamflow during AD 1500–1980 for the West Walker River basin. The reconstruction included similar wet and dry episodes as other regression-based records for the Great Basin, and provided estimates of actual evapotranspiration and of April 1 snow water equivalence. Given its limited input requirements, this approach is suitable in areas where sparse instrumental data are available to improve proxy-based streamflow reconstructions and to explore non-climatic reasons for streamflow variability during the reconstruction period.

Shuffled complex evolution approach for effective and efficient surface wave analysis

Inversion of Rayleigh wave dispersion curves is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this paper, we proposed and tested a new Rayleigh wave dispersion curve inversion scheme called the shuffled complex evolution (SCE) approach, which is based on a synthesis of four global optimization strategies that have proved successful for global optimization: (a) combination of probabilistic and deterministic approaches; (b) the strategy of clustering; (c) systematic evolution of a complex of points spanning the space, in the direction of global improvement; and (d) competitive evolution. Incorporating these four global optimization strategies into the inverse procedure greatly enhances the performance of the SCE method because these steps not only can effectively locate the promising areas in the solution space for a global minimum but also avoid its wandering near the global minimum in the final stage of search.The proposed inverse procedure was applied to nonlinear inversion of fundamental-mode Rayleigh wave dispersion curves for near-surface shear (S)-wave velocity profiles. The calculation efficiency and stability of the inversion scheme are tested on four synthetic models and a real-world example from a waste disposal site in NE Italy. A comparative analysis with genetic algorithms (GA), marginal posterior probability density (MPPD) estimation, and simulated annealing (SA) is also made in the present study to further evaluate the performance of the proposed approach.Results from both synthetic and actual field data demonstrate that shuffled complex evolution algorithm promises to be robust, effective, and efficient for high-frequency surface wave analysis.

A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization

The difficulties associated with using classical mathematical programming methods on complex optimization problems have contributed to the development of alternative and efficient numerical approaches. Recently, to overcome the limitations of classical optimization methods, researchers have proposed a wide variety of meta-heuristics for searching near-optimum solutions to problems. Among the existing meta-heuristic algorithms, a relatively new optimization paradigm is the Shuffled Complex Evolution at the University of Arizona (SCE-UA) which is a global optimization strategy that combines concepts of the competition evolution theory, downhill simplex procedure of Nelder–Mead, controlled random search and complex shuffling. In an attempt to reduce processing time and improve the quality of solutions, particularly to avoid being trapped in local optima, in this paper is proposed a hybrid SCE-UA approach. The proposed hybrid algorithm is the combination of SCE-UA (without Nelder–Mead downhill simplex procedure) and a pattern search approach, called SCE-PS, for unconstrained optimization. Pattern search methods are derivative-free, meaning that they do not use explicit or approximate derivatives. Moreover, pattern search algorithms are direct search methods well suitable for the global optimization of highly nonlinear, multiparameter, and multimodal objective functions. The proposed SCE-PS method is tested with six benchmark optimization problems. Simulation results show that the proposed SCE-PS improves the searching performance when compared with the classical SCE-UA and a genetic algorithm with floating-point representation for all the tested problems. As evidenced by the performance indices based on the mean performance of objective function in 30 runs and mean of computational time, the SCE-PS algorithm has demonstrated to be effective and efficient at locating best-practice optimal solutions for unconstrained optimization.

A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization

Numerous optimization methods have been proposed for the solution of the unconstrained optimization problems, such as mathematical programming methods, stochastic global optimization approaches, and metaheuristics. In this paper, a metaheuristic algorithm called Modified Shuffled Complex Evolution (MSCE) is proposed, where an adaptation of the Downhill Simplex search strategy combined with the differential evolution method is proposed. The efficiency of the new method is analyzed in terms of the mean performance and computational time, in comparison with the genetic algorithm using floating-point representation (GAF) and the classical shuffled complex evolution (SCE-UA) algorithm using six benchmark optimization functions. Simulation results and the comparisons with SCE-UA and GAF indicate that the MSCE improves the search performance on the five benchmark functions of six tested functions.

Evaluation of optimization schemes and determination of solid fuel properties for CFD fire models using bench-scale pyrolysis tests

CFD fire modeling tools are continuously developed and improved to increase their predictive capability of phenomena observed in practical applications. Such models require that “effective” material properties be provided so that the pyrolysis codes used in the models can properly estimate the thermal degradation of solid fuels involved in a fire situation. This paper presents analyses aimed at evaluating the plausibility of obtaining material properties numerically from pyrolysis data collected in a Fire Propagation Apparatus (FPA). A theoretical pyrolysis model is used to simulate the experimental data and the input parameters (i.e. the material properties) are adjusted to provide the best possible agreement between simulations and experiments. This is done via the application of evolutionary optimization methodologies. First, available optimization techniques are evaluated using synthetic data and it is shown that the Shuffled Complex Evolution approach ( [17]) can recover the input parameters with high accuracy, efficiency, and robustness. Second, the algorithm is applied to experimental FPA pyrolysis data of practical materials: polymethyl methacrylate (PMMA), single-wall corrugated board, and chlorinated polyvinyl chloride (CPVC).

An ensemble conditional nonlinear optimal perturbation approach: Formulation and applications to parameter calibration

The conditional nonlinear optimal perturbation (CNOP) method proposed by Mu et al. (2003) has been a useful tool for studying predictability dynamics. Its further applications are, however, hampered by the need of an adjoint model. Aiming to solve this problem, an ensemble conditional nonlinear optimal perturbation (EnCNOP) approach is proposed in this paper by merging the Monte Carlo method and the proper orthogonal decomposition (POD) technique into the CNOP to transform an implicit optimization problem into an explicit one. This approach is further formulated for parameter calibration. Numerical experiments with a 1‐D model of the soil water equation show that the EnCNOP approach outperforms a dual‐pass optimization framework on the basis of the ensemble Kalman filter (EnKF) and a shuffled complex evolution approach (SCE‐UA) in terms of both increasing the calibration precision and reducing the computational costs. In particular, the EnCNOP approach outperforms the SCE‐UA when only an approximate value range of the input parameters is known. Our EnCNOP method is further implemented within a complicated highly nonlinear land surface model (namely, NCAR CLM3) and evaluated through a case study which illustrates its applicability to complex, highly nonlinear problems.

Comparison of genetic algorithms and shuffled complex evolution approach for calibrating distributed rainfall–runoff model

AbstractThree methods, Shuffled Complex Evolution (SCE), Simple Genetic Algorithm (SGA) and Micro‐Genetic Algorithm (µGA), are applied in parameter calibration of a grid‐based distributed rainfall–runoff model (GBDM) and compared by their performances. Ten and four historical storm events in the Yan‐Shui Creek catchment, Taiwan, provide the database for model calibration and verification, respectively. The study reveals that the SCE, SGA and µGA have close calibration results, and none of them are superior with respect to all the performance measures, i.e. the errors of time to peak, peak discharge and the total runoff volume, etc. The performances of the GBDM for the verification events are slightly worse than those in the calibration events, but still quite satisfactory. Among the three methods, the SCE seems to be more robust than the other two approaches because of the smallest influence of different initial random number seeds on calibrated model parameters, and has the best performance of verification with a relatively small number of calibration events. Copyright © 2009 John Wiley & Sons, Ltd.

A dual‐pass variational data assimilation framework for estimating soil moisture profiles from AMSR‐E microwave brightness temperature

To overcome the difficulties in determining the optimal parameters needed for a radiative transfer model (RTM), which acts as the observational operator in a land data assimilation system, we have designed a dual‐pass assimilation (DP‐En4DVar) framework to optimize the model state (volumetric soil moisture content) and model parameters simultaneously using the gridded Advanced Microwave Scanning Radiometer–EOS (AMSR‐E) satellite brightness temperature data. This algorithm embeds a dual‐pass (the state assimilation pass and the parameter optimization pass) optimization technique based on an ensemble‐based four‐dimensional variational assimilation method and a shuffled complex evolution approach (SCE‐UA). The SCE‐UA method optimizes the parameters using observational information, thereby leading to improved simulations. The RTM is used to estimate brightness temperature from surface temperature and soil moisture. This algorithm is implemented differently in two phases: the parameter calibration phase and the pure assimilation phase. Both passes are applied in each assimilation time window during the parameter calibration phase. However, only the state assimilation pass is used in the pure assimilation phase after the parameters are determined during the parameter calibration phase. Several experiments conducted using this framework coupled partially with a land surface model (the NCAR CLM3) show that volumetric soil moisture content can be significantly improved to be comparable with in situ observations by assimilating only daily satellite brightness temperature. Furthermore, the improvement in surface soil moisture also propagates to lower layers where no observations are available.

Shuffled complex evolution approach for effective and efficient global minimization

The degree of difficulty in solving a global optimization problem is in general dependent on the dimensionality of the problem and certain characteristics of the objective function. This paper discusses five of these characteristics and presents a strategy for function optimization called the shuffled complex evolution (SCE) method, which promises to be robust, effective, and efficient for a broad class of problems. The SCE method is based on a synthesis of four concepts that have proved successful for global optimization: (a) combination of probabilistic and deterministic approaches; (b) clustering; (c) systematic evolution of a complex of points spanning the space, in the direction of global improvement; and (d) competitive evolution. Two algorithms based on the SCE method are presented. These algorithms are tested by running 100 randomly initiated trials on eight test problems of differing difficulty. The performance of the two algorithms is compared to that of the controlled random search CRS2 method presented by Price (1983, 1987) and to a multistart algorithm based on the simplex method presented by Nelder and Mead (1965).