Meta-Modeling by Symbolic Regression and Pareto Simulated Annealing

The subject of this paper is a new approach to Symbolic Regression.Other publications on Symbolic Regression use Genetic Programming.This paper describes an alternative method based on Pareto Simulated Annealing.Our method is based on linear regression for the estimation of constants.Interval arithmetic is applied to ensure the consistency of a model.In order to prevent over-fitting, we merit a model not only on predictions in the data points, but also on the complexity of a model.For the complexity we introduce a new measure.We compare our new method with the Kriging meta-model and against a Symbolic Regression meta-model based on Genetic Programming.We conclude that Pareto Simulated Annealing based Symbolic Regression is very competitive compared to the other meta-model approachesapproximation;meta-modeling;pareto simulated annealing;symbolic regression

Contextualized property market models vs. Generalized mass appraisals: An innovative approach

The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016-2017. The ability to generate a "unique" functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies

Improving the multi-objective evolutionary optimization algorithm for hydropower reservoir operations in the California Oroville-Thermalito complex

This study demonstrates the application of an improved Evolutionary optimization Algorithm (EA), titled Multi-Objective Complex Evolution Global Optimization Method with Principal Component Analysis and Crowding Distance Operator (MOSPD), for the hydropower reservoir operation of the Oroville-Thermalito Complex (OTC) - a crucial head-water resource for the California State Water Project (SWP). In the OTC's water-hydropower joint management study, the nonlinearity of hydropower generation and the reservoir's water elevation-storage relationship are explicitly formulated by polynomial function in order to closely match realistic situations and reduce linearization approximation errors. Comparison among different curve-fitting methods is conducted to understand the impact of the simplification of reservoir topography. In the optimization algorithm development, techniques of crowding distance and principal component analysis are implemented to improve the diversity and convergence of the optimal solutions towards and along the Pareto optimal set in the objective space. A comparative evaluation among the new algorithm MOSPD, the original Multi-Objective Complex Evolution Global Optimization Method (MOCOM), the Multi-Objective Differential Evolution method (MODE), the Multi-Objective Genetic Algorithm (MOGA), the Multi-Objective Simulated Annealing approach (MOSA), and the Multi-Objective Particle Swarm Optimization scheme (MOPSO) is conducted using the benchmark functions. The results show that best the MOSPD algorithm demonstrated the best and most consistent performance when compared with other algorithms on the test problems. The newly developed algorithm (MOSPD) is further applied to the OTC reservoir releasing problem during the snow melting season in 1998 (wet year), 2000 (normal year) and 2001 (dry year), in which the more spreading and converged non-dominated solutions of MOSPD provide decision makers with better operational alternatives for effectively and efficiently managing the OTC reservoirs in response to the different climates, especially drought, which has become more and more severe and frequent in California

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

A Genetic Algorithm with Design of Experiments Approach to Predict the Optimal Process Parameters for FDM

This paper describes a Genetic Algorithm (GA) with Design of Experiments (DoE) approach to predict the optimized surface roughness and porosity characteristics of the parts produced using ABS material on stratasys FDM 2000 machine. The Mathematical Model (MM) was developed by using Response Surface Methodology (RSM). It is to predict and investigate the influence of selected process parameters namely slice thickness, road width, liquefier temperature and air gap and their interactions on the surface roughness and porosity. The developed MM is the fitness function in GA in order to find out the optimal sets of process parameters and to predict the corresponding surface quality characteristics. These results have been validated and the experimental results after GA are found to be in conformance with the predicted process parameters.Mechanical Engineerin

Development of an experiment-based robust design paradigm for multiple quality characteristics using physical programming

The well-known quality improvement methodology, robust design, is a powerful and cost-effective technique for building quality into the design of products and processes. Although several approaches to robust design have been proposed in the literature, little attention has been given to the development of a flexible robust design model. Specifically, flexibility is needed in order to consider multiple quality characteristics simultaneously, just as customers do when judging products, and to capture design preferences with a reasonable degree of accuracy. Physical programming, a relatively new optimization technique, is an effective tool that can be used to transform design preferences into specific weighted objectives. In this paper, we extend the basic concept of physical programming to robust design by establishing the links of experimental design and response surface methodology to address designers’ preferences in a multiresponse robust design paradigm. A numerical example is used to show the proposed procedure and the results obtained are validated through a sensitivity study

Optimal irrigation water allocation using a genetic algorithm under various weather conditions

Growing water scarcity, due to growing populations and varying natural conditions, puts pressure on irrigation systems, which often are the main consumptive water users. Therefore, water resources management to improve the allocation of limited water supplies is essential. In this study, a non-linear programming optimization model with an integrated soil/water balance is developed to determine the optimal reservoir release policies and the optimal cropping pattern around Doroudzan Dam in the South-West of Iran. The proposed model was solved using a genetic algorithm (GA). Four weather conditions were identified by combining the probability levels of rainfall, evapotranspiration and inflow. Moreover, two irrigation strategies, full irrigation and deficit irrigation were modeled under each weather condition. The results indicate that for all weather conditions the total farm income and the total cropped area under deficit irrigation were larger than those under full irrigation. In addition, our results show that when the weather conditions and the availability of water changes the optimal area under corn and sugar beet decreases sharply. In contrast, the change in area cropped with wheat is small. It is concluded that the optimization approach has been successfully applied to Doroudzan Dam region. Thus, decision makers and water authorities can use it as an effective tool for such large and complex irrigation planning problems

Polynomial Response Surface Approximations for the Multidisciplinary Design Optimization of a High Speed Civil Transport

Surrogate functions have become an important tool in multidisciplinary design optimization to deal with noisy functions, high computational cost, and the practical difficulty of integrating legacy disciplinary computer codes. A combination of mathematical, statistical, and engineering techniques, well known in other contexts, have made polynomial surrogate functions viable for MDO. Despite the obvious limitations imposed by sparse high fidelity data in high dimensions and the locality of low order polynomial approximations, the success of the panoply of techniques based on polynomial response surface approximations for MDO shows that the implementation details are more important than the underlying approximation method (polynomial, spline, DACE, kernel regression, etc.). This paper surveys some of the ancillary techniques—statistics, global search, parallel computing, variable complexity modeling—that augment the construction and use of polynomial surrogates