New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach

This paper presents an innovative machine learning approach for the formulation of load carrying capacity of castellated steel beams (CSB). New design equations were developed to predict the load carrying capacity of CSB using linear genetic programming (LGP), and an integrated search algorithm of genetic programming and simulated annealing, called GSA. The load capacity was formulated in terms of the geometrical and mechanical properties of the castellated beams. An extensive trial study was carried out to select the most relevant input variables for the LGP and GSA models. A comprehensive database was gathered from the literature to develop the models. The generalization capabilities of the models were verified via several criteria. The sensitivity of the failure load of CSB to the influencing variables was examined and discussed. The employed machine learning systems were found to be effective methods for evaluating the failure load of CSB. The prediction performance of the optimal LGP model was found to be better than that of the GSA model.     

A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems

Complexity of analysis of geotechnical behavior is due to multivariable dependencies of soil and rock responses. In order to cope with this complex behavior, traditional forms of engineering design solutions are reasonably simplified. Incorporating simplifying assumptions into the development of the traditional methods may lead to very large errors. This paper presents an endeavor to exploit a robust multi-gene genetic programming (MGGP) method for the analysis of geotechnical and earthquake engineering systems. MGGP is a modified genetic programming approach for model structure selection combined with a classical technique for parameter estimation. To justify the abilities of MGGP, it is systematically employed to formulate the complex geotechnical engineering problems. Different classes of the problems analyzed include the assessment of (i) undrained lateral load capacity of piles, (ii) undrained side resistance alpha factor for drilled shafts, (iii) settlement around tunnels, and (iv) soil liquefaction. The validity of the derived models is tested for a part of test results beyond the training data domain. Numerical examples show the superb accuracy, efficiency, and great potential of MGGP. Contrary to artificial neural networks and many other soft computing tools, MGGP provides constitutive prediction equations. The MGG-based solutions are particularly valuable for pre-design practices.     

A Multi-criteria Decision-making Optimization Model for Flood Management in Reservoirs

Water Resources Management - Flood management in a reservoir-outlet system is a multi-criterion decision-making (MCDM) issue, in which preventing flood damage and flood overtopping, as well as...     

HYBRID GENETIC PROGRAMMING-BASED SEARCH ALGORITHMS FOR ENTERPRISE BANKRUPTCY PREDICTION

Bankruptcy is an extremely significant worldwide problem that affects the economic well- being of all countries. The high social costs incurred by various stakeholders associated with bankrupt firms imply the need to search for better theoretical understanding and prediction quality. The main objective of this paper is to apply genetic programming with orthogonal least squares (GP/OLS) and with simulated annealing (GP/SA) algorithms to build models for bankruptcy prediction. Utilizing the hybrid GP/OLS and GP/SA techniques, generalized relationships are obtained to classify samples of 136 bankrupt and nonbankrupt Iranian corporations based on financial ratios. Another important contribution of this paper is to identify the effective predictive financial ratios based on an extensive bankruptcy prediction literature review and a sequential feature selection (SFS) analysis. A comparative study on the classification accuracy of the GP/OLS- and GP/SA-based models is also conducted. The observed agreement between the predictions and the actual values indicates that the proposed models effectively estimate any enterprise with regard to the aspect of bankruptcy. According to the results, the proposed GP/SA model has better performance than the GP/OLS model in bankruptcy prediction.     

A computational intelligence‐based approach for short‐term traffic flow prediction

This paper proposes alternative approaches for the prediction of short‐term traffic flow using three branches of computational intelligence techniques, namely linear genetic programming (LGP), multilayer perceptron (MLP) and fuzzy logic (FL). Different LGP, MLP and FL models are developed for estimating the 5‐ and 30‐min traffic flow rates. New LGP‐ and MLP‐based prediction equations are derived for the traffic flow rates in the 5‐ and 30‐min time intervals. The models are established upon extensive databases of the traffic flow records obtained from Iran's Rasht‐Qazvin highway. The results indicate that the proposed models are effectively capable of predicting the target values. The LGP‐based models are found to be simple, straightforward and more practical for predictive purposes compared with the other derived models.     

Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization

The performance of an optimization tool is largely determined by the efficiency of the search algorithm used in the process. The fundamental nature of a search algorithm will essentially determine its search efficiency and thus the types of problems it can solve. Modern metaheuristic algorithms are generally more suitable for global optimization. This paper carries out extensive global optimization of unconstrained and constrained problems using the recently developed eagle strategy by Yang and Deb in combination with the efficient differential evolution. After a detailed formulation and explanation of its implementation, the proposed algorithm is first verified using twenty unconstrained optimization problems or benchmarks. For the validation against constrained problems, this algorithm is subsequently applied to thirteen classical benchmarks and three benchmark engineering problems reported in the engineering literature. The performance of the proposed algorithm is further compared with various, state-of-the-art algorithms in the area. The optimal solutions obtained in this study are better than the best solutions obtained by the existing methods. The unique search features used in the proposed algorithm are analyzed, and their implications for future research are also discussed in detail.     

Combined heat and power economic dispatch by mesh adaptive direct search algorithm

The optimal utilization of multiple combined heat and power (CHP) systems is a complex problem. Therefore, efficient methods are required to solve it. In this paper, a recent optimization technique, namely mesh adaptive direct search (MADS) is implemented to solve the combined heat and power economic dispatch (CHPED) problem with bounded feasible operating region. Three test cases taken from the literature are used to evaluate the exploring ability of MADS. Latin hypercube sampling (LHS), particle swarm optimization (PSO) and design and analysis of computer experiments (DACE) surrogate algorithms are used as powerful SEARCH strategies in the MADS algorithm to improve its effectiveness. The numerical results demonstrate that the utilized MADS–LHS, MADS–PSO, MADS–DACE algorithms have acceptable performance when applied to the CHPED problems. The results obtained using the MADS–DACE algorithm are considerably better than or as well as the best known solutions reported previously in the literature. In addition to the superior performance, MADS–DACE provides significant savings of computational effort.     

Interior search algorithm (ISA): A novel approach for global optimization

This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune.     

Effect of autochthonous starter cultures isolated from Siahmazgi cheese on physicochemical,microbiological and volatile compound profiles and sensorial attributes of sucuk,a Turkish dry-fermented sausage

The effect of adding autochthonous starter cultures isolated from Siahmazgi cheese, on the physicochemical parameters and microbial counts of sucuk was investigated during the ripening period. SPME–GC/MS was used in volatile compound analysis and a trained group of panelists carried out sensory analysis of the final product. After preliminary screening, three strains of Lactobacillus plantarum, which possess desirable technological properties, were used to prepare three starter cultures: LBP7, LBP10 and LBP14. The addition of LBP7 and LBP14 starter cultures had a significant effect (P < 0.05) on lightness, leading to higher L values compared to control sausages during the ripening period. Both LBP7 and LBP14 sausages showed higher counts of lactic acid bacteria, lower growth of Enterobacteriaceae and Gram-positive catalase-positive cocci and greatly lowered the pH value compared to control sausages throughout the ripening process. At the end of the ripening process, lactic acid bacteria counts were affected (P < 0.05) by the addition of starter culture since higher counts were observed in sausages prepared with LBP7 (9.14 log CFU/g) and LBP14 (8.96 log CFU/g) batches. The decrease of water activity during the ripening of sausages was not affected by the various starters. The texture profiles of all sausages were similar except for LBP10, which showed lower hardness and gumminess during ripening. Under the conditions of the study, volatile compounds were mainly from spices, and no marked differences were found among inoculated sausages. However, sensory evaluation revealed that most of the sensory attributes were scored higher for inoculated sausages than for the control ones. Therefore, LBP7 and LBP14 could be promising candidates for inclusion as starter cultures for the manufacture of sucuk.