A Novel Enhanced Differential Evolution Algorithm for Outbound Logistics of the Poultry Industry in Thailand

Outbound logistics is a crucial field of logistics management. This study considers a planning distribution for the poultry industry in Thailand. The goal of the study is to minimize the transportation cost for the multi-depot vehicle-routing problem (MDVRP). A novel enhanced differential evolution algorithm (RI-DE) is developed based on a new re-initialization mutation formula and a local search function. A mixed-integer programming formulation is presented in order to measure the performance of a heuristic with GA, PSO, and DE for small-sized instances. For large-sized instances, RI-DE is compared to the traditional DE algorithm for solving the MDVRP using published benchmark instances. The results demonstrate that RI-DE obtained a near-optimal solution of 99.03% and outperformed the traditional DE algorithm with a 2.53% relative improvement, not only in terms of solution performance, but also in terms of computational time.

ELECTRICAL PARAMETERS ESTIMATION OF SINGLE DIODE PV MODULE MODEL USING HYBRID METAHEURISTIC ALGORIHM

In this paper, an improved hybrid algorithm called differential evolution with integrated mutation per iteration (DEIM) is proposed to extract five parameters of single-diode PV module model obtained by combining differential evolution (DE) algorithm and electromagnetic-like (EML) algorithm. The EML algorithm's attraction-repulsion idea is employed in DEIM in order to enhance the mutation process of DE. The proposed algorithm is validated with other methods using experimental I-V data. The results of presented method reveal that simulated I-V characteristics have a high degree of agreement with experimental ones. The proposed model has an average root mean square error of 0.062A, an absolute error of 0.0452A, a mean bias error of 0.006A, a coefficient of determination of 0.992, a standard test deviation around 0.04540, and 15.33sec as execution time. The results demonstrate that the proposed method is better in terms of the accuracy and execution time (convergence) when compared with other methods where provide less errors.

Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model

This work shown as the fuzzy-EGARCH-ANN (fuzzy-exponential generalized autoregressive conditional heteroscedastic-artificial neural network) model does not require continuous model calibration if the corresponding DE algorithm is used appropriately, but other models such as GARCH, EGARCH, and EGARCH-ANN need continuous model calibration and validation so they fit the data and reality very well up to the desired accuracy. Also, a robust analysis of volatility forecasting of the daily S&P 500 data collected from Yahoo Finance for the daily spanning period 1/3/2006 to 20/2/2020. To our knowledge, this is the first study that focuses on the daily S&P 500 data using high-frequency data and the fuzzy-EGARCH-ANN econometric model. Finally, the research finds that the best performing model in terms of one-step-ahead forecasts based on realized volatility computed from the underlying daily data series is the fuzzy-EGARCH-ANN (1,1,2,1) model with Student’s t-distribution.

Weight optimization of steel lattice transmission towers based on Differential Evolution and machine learning classification technique

Transmission towers are tall structures used to support overhead power lines. They play an important role in the electrical grids. There are several types of transmission towers in which lattice towers are the most common type. Designing steel lattice transmission towers is a challenging task for structural engineers due to a large number of members. Therefore, discovering effective ways to design lattice towers has attracted the interest of researchers. This paper presents a method that integrates Differential Evolution (DE), a powerful optimization algorithm, and a machine learning classification model to minimize the weight of steel lattice towers. A classification model based on the Adaptive Boosting algorithm is developed in order to eliminate unpromising candidates during the optimization process. A feature handling technique is also introduced to improve the model quality. An illustrated example of a 160-bar tower is conducted to demonstrate the efficiency of the proposed method. The results show that the application of the Adaptive Boosting model saves about 38% of the structural analyses. As a result, the proposed method is 1.5 times faster than the original DE algorithm. In comparison with other algorithms, the proposed method obtains the same optimal weight with the least number of structural analyses.

Optimal solution of engineering design problems through differential gradient evolution plus algorithm: a hybrid approach

It is very necessary and applicable to optimize all disciplines. In practical engineering problems the optimization has been a significant component. This article presents the hybrid approach named as differential gradient evolution plus (DGE+) algorithm which is the combination of differential evolution (DE) algorithm and gradient evolution (GE) algorithm. DE was used to diversify and GE was used for intensification with a perfect equilibrium between exploration and exploitation with an improvised distribution of dynamic probability and offers a new shake-off approach to prevent premature convergence to local optimum. To describe the success, the proposed algorithm is compared to modern meta-heuristics. To see the accuracy, robustness, and reliability of DGE+ it has been implemented on eight complex practical engineering problems named as: pressure vessel, belleville spring, tension/compression spring, three-bar truss, welded beam, speed reducer, gear train and rolling element bearing design problem, the results revealed that DGE+ algorithm can deliver highly efficient, competitive and promising results.

Optimization of steel roof trusses using machine learning-assisted differential evolution

A steel truss is a preferred solution in large-span roof structures due to its good attributes such as lightweight, durability. However, designing steel trusses is a challenging task for engineers due to a large number of design variables. Recently, optimization-based design approaches have demonstrated the great potential to effectively support structural engineers in finding the optimal designs of truss structures. This paper aims to use the AdaBoost-DE algorithm for optimizing steel roof trusses. The AdaBoost-DE employed in this study is a hybrid algorithm in which the AdaBoost classification technique is used to enhance the performance of the Differential Evolution algorithm by skipping unnecessary fitness evaluations during the optimization process. An example of a duo-pitch steel roof truss with a span of 24 meters is carried out. The result shows that the AdaBoost-DE achieves the same optimal design as the original DE algorithm, but reduces the computational cost by approximately 36%.

Differential evolution algorithm-based multiple-factor optimization methods for data assimilation

The methods of searching for optimized parameters have substantial effects on the forecast accuracy of ensemble data assimilation systems. The selection of these factors is usually performed using trial-and-error methods, and poor parameterizations may lead to filter divergence. Combined with the local ensemble transform Kalman filtering method (LETKF), a technique for an automated search of the best configuration (parameters) of a data assimilation system is proposed. To obtain better assimilation, a differential evolution (DE) algorithm-based multiple-factor parameterization method results in the corresponding circumstances. By combining with fast-searching DE algorithms, we may retrieve the most ideal parameter combinations. Several numerical experiments performed with the Lorenz-96 model show that new methods performed better than the original one-parameter optimization methods. As the basis of DE methods, the best combinations of the local radius and the covariance inflation parameter, which can guarantee the best DA performances in the corresponding circumstances, are retrieved. It is found that the new method is capable of outperforming previous search algorithms under both perfect and imperfect model scenarios, and the calculation cost in Lorenz-96 model is lower. However, how to apply the new proposed method to more complex atmospheric or land surface models requires further verification.

A Master-Slave Binary Grey Wolf Optimizer for Optimal Feature Selection in Biomedical Data Classification

A new master-slave binary grey wolf optimizer (MSBGWO) is introduced. A master-slave learning scheme is introduced to the grey wolf optimizer (GWO) to improve its ability to explore and get better solutions in a search space. Five high-dimensional biomedical datasets are used to test the ability of MSBGWO in feature selection. The experimental results of MSBGWO are superior in terms of classification accuracy, precision, recall, F -measure, and number of features selected when compared to those of the binary grey wolf optimizer version 2 (BGWO2), binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), differential evolution (DE) algorithm, and sine-cosine algorithm (SCA).

Artificial Jellyfish Search Algorithm-Based Selective Harmonic Elimination in a Cascaded H-Bridge Multilevel Inverter

This paper used an artificial jellyfish search (AJFS) optimizer suitable for selective harmonic elimination-based modulation for multilevel inverter (MLI) voltage control application. The main objective was to remove the undesired lower-order harmonics in the output voltage waveform of an MLI. This algorithm was motivated by the behavior of jellyfish in the ocean. Jellyfish have the ability to find the global best position where a large quantity of nutritious food is available. The paper applied AJFS algorithm on five, seven, and nine levels of CHB-MLI. The optimum switching angle was calculated for the entire modulation range for the desired lower-order harmonics elimination. The problem formulated to achieve the objective was solved in a MATLAB environment. The total harmonic distortion (THD) values of five-, seven-, and nine-level inverters for various modulation indexes were computed using AJFS and compared with the powerful differential evolution (DE) algorithm. The comparison of THD results clearly demonstrated superior THD in the output of CHB-MLI of the AJFS algorithm over DE and GA algorithm for low and medium values of modulation index. The experimental results further validated the better performance of the AJFS algorithm.