Differential evolution algorithm
Dotaz
Zobrazit nápovědu
Differential evolution (DE) belongs to the most usable optimization algorithms, presented in many improved and modern versions in recent years. Generally, the low convergence rate is the main drawback of the DE algorithm. In this article, the gray wolf optimizer (GWO) is used to accelerate the convergence rate and the final optimal results of the DE algorithm. The new resulting algorithm is called Hunting Differential Evolution (HDE). The proposed HDE algorithm deploys the convergence speed of the GWO algorithm as well as the appropriate searching capability of the DE algorithm. Furthermore, by adjusting the crossover rate and mutation probability parameters, this algorithm can be adjusted to pay closer attention to the strengths of each of these two algorithms. The HDE/current-to-rand/1 performed the best on CEC-2019 functions compared to the other eight variants of HDE. HDE/current-to-best/1 is also chosen as having superior performance to other proposed HDE compared to seven improved algorithms on CEC-2014 functions, outperforming them in 15 test functions. Furthermore, jHDE performs well by improving in 17 functions, compared with jDE on these functions. The simulations indicate that the proposed HDE algorithm can provide reliable outcomes in finding the optimal solutions with a rapid convergence rate and avoiding the local minimum compared to the original DE algorithm.
Identifying the parameters of a solar photovoltaic (PV) model optimally, is necessary for simulation, performance assessment, and design verification. However, precise PV cell modelling is critical for design due to many critical factors, such as inherent nonlinearity, existing complexity, and a wide range of model parameters. Although different researchers have recently proposed several effective techniques for solar PV system parameter identification, it is still an interesting challenge for researchers to enhance the accuracy of the PV system modelling. With the above motivation, this article suggests a stage-specific mutation strategy for the proposed enhanced differential evolution (EDE) that adopts a better search process to arrive at optimal solutions by adaptively varying the mutation factor and crossover rate at different search stages. The optimal identification of PV systems is formulated as a single objective function. It appears in the form of the Root Mean Square Error (RMSE) between the PV model current from the experimental data and the current calculated using the identified parameters considering the parameter constraints (limits). The I-V (current-voltage) characteristics/data with identified parameters are validated with the experimental data to justify the proposed approach's accuracy and efficacy for different cells and modules. Extensive simulation has been demonstrated considering two different PV cells (RTC France & PVM-752-GaAs) and three different PV modules (ND-R250A5, STM6 40/36 & STP6 120/36). The results obtained from the proposed EDE technique show Root Mean Square Errors (RMSE) of 7.730062e-4, 7.419648e-4, and 7.33228e-4 respectively, in parameter identification of RTC France PV cell models based on single, double, and triple diodes. Also, the RMSE involved in parameter identification of PVM-752-GaAs PV cell models based on single, double, and triple diodes are 1.59256e-4, 1.408989e-4, and 1.30181e-4, respectively. The parameters identification of ND-R250A5, STM6 40/36 and STP6 120/36 PV modules involve RMSE values of 7.697716e-3, 1.772095e-3, and 1.224258e-2, respectively. All these RMSE values obtained with proposed EDE are the least as compared to other well-accepted algorithms, thereby justifying its higher accuracy.
- Klíčová slova
- Enhanced differential evolution, Metaheuristic algorithm, Optimization technique, PV model, Parameter identification,
- Publikační typ
- časopisecké články MeSH
The whale optimization algorithm (WOA) is a widely used metaheuristic optimization approach with applications in various scientific and industrial domains. However, WOA has a limitation of relying solely on the best solution to guide the population in subsequent iterations, overlooking the valuable information embedded in other candidate solutions. To address this limitation, we propose a novel and improved variant called Pbest-guided differential WOA (PDWOA). PDWOA combines the strengths of WOA, particle swarm optimizer (PSO), and differential evolution (DE) algorithms to overcome these shortcomings. In this study, we conduct a comprehensive evaluation of the proposed PDWOA algorithm on both benchmark and real-world optimization problems. The benchmark tests comprise 30-dimensional functions from CEC 2014 Test Functions, while the real-world problems include pressure vessel optimal design, tension/compression spring optimal design, and welded beam optimal design. We present the simulation results, including the outcomes of non-parametric statistical tests including the Wilcoxon signed-rank test and the Friedman test, which validate the performance improvements achieved by PDWOA over other algorithms. The results of our evaluation demonstrate the superiority of PDWOA compared to recent methods, including the original WOA. These findings provide valuable insights into the effectiveness of the proposed hybrid WOA algorithm. Furthermore, we offer recommendations for future research to further enhance its performance and open new avenues for exploration in the field of optimization algorithms. The MATLAB Codes of FISA are publicly available at https://github.com/ebrahimakbary/PDWOA.
The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.
Evolutionary technique differential evolution (DE) is used for the evolutionary tuning of controller parameters for the stabilization of set of different chaotic systems. The novelty of the approach is that the selected controlled discrete dissipative chaotic system is used also as the chaotic pseudorandom number generator to drive the mutation and crossover process in the DE. The idea was to utilize the hidden chaotic dynamics in pseudorandom sequences given by chaotic map to help differential evolution algorithm search for the best controller settings for the very same chaotic system. The optimizations were performed for three different chaotic systems, two types of case studies and developed cost functions.
- MeSH
- algoritmy * MeSH
- nelineární dynamika * MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
This study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based optimization methods such as ADAM and SGDM, as well as with another evolutionary algorithm, differential evolution (DE). Experiments are conducted on the skin dataset, which consists of 245,057 samples with skin and non-skin labels. The results show that the neural network trained by SOMA achieves the highest accuracy (93.18%), outperforming ADAM (84.87%), SGDM (84.79%), and DE (91.32%). The visual evaluation also reveals the SOMA-trained neural network's accurate and reliable segmentation capabilities in most cases. These findings highlight the potential of incorporating evolutionary optimization algorithms like SOMA into the training process of artificial neural networks, significantly improving performance in image segmentation tasks.
- Klíčová slova
- Artificial neural networks, Computer vision, Optimization algorithm, SOMA, Skin segmentation, Swarm intelligence,
- MeSH
- algoritmy * MeSH
- kůže * diagnostické zobrazování MeSH
- lidé MeSH
- neuronové sítě * MeSH
- počítačové zpracování obrazu metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating a large number of PHEVs with advanced control and storage capabilities can enhance the flexibility of the distribution grid. This study proposes an innovative energy management strategy (EMS) using an Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids with renewable energy sources (RESs) and PHEVs. The goal is to optimize multi-objective scheduling for a microgrid with wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, and batteries to balance power and store excess energy. The aim is to minimize microgrid operating costs while considering environmental impacts. The optimization problem is framed as a multi-objective problem with nonlinear constraints, using fuzzy logic to aid decision-making. In the first scenario, the microgrid is optimized with all RESs installed within predetermined boundaries, in addition to grid connection. In the second scenario, the microgrid operates with a wind turbine at rated power. The third case study involves integrating plug-in hybrid electric vehicles (PHEVs) into the microgrid in three charging modes: coordinated, smart, and uncoordinated, utilizing standard and rated RES power. The SaCryStAl algorithm showed superior performance in operation cost, emissions, and execution time compared to traditional CryStAl and other recent optimization methods. The proposed SaCryStAl algorithm achieved optimal solutions in the first scenario for cost and emissions at 177.29 €ct and 469.92 kg, respectively, within a reasonable time frame. In the second scenario, it yielded optimal cost and emissions values of 112.02 €ct and 196.15 kg, respectively. Lastly, in the third scenario, the SaCryStAl algorithm achieves optimal cost values of 319.9301 €ct, 160.9827 €ct and 128.2815 €ct for uncoordinated charging, coordinated charging and smart charging modes respectively. Optimization results reveal that the proposed SaCryStAl outperformed other evolutionary optimization algorithms, such as differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, and genetic algorithm, as confirmed through test cases.
Abrasive Waterjet (AWJ) is a promising non-traditional method for precision cutting of aerospace materials like AL7075 T6. This work explores AL7075 T6 AWJ-based Deep Hole Drilling (DHD) using a full factorial design with accurate modeling and optimization performed through machine learning and evolutionary algorithms. The objective is to investigate the influence of process parameters and to model, and predict the optimal AWJ-DHD settings, such as waterjet pressure, standoff distance, and abrasive mass flow rate, on drilling qualities including geometrical and dimensional precision (kerf angle, kerf ratio), surface roughness, and drilling efficiency. Four machine learning models, Adaptive Boosted Regression (ABR), Extreme Gradient Boosting (XGB), Decision Tree (DT), and Random Forest (RF) were developed with experimental data to enhance prediction accuracy and process efficiency. Among the developed models, RF had the lowest testing error value for all responses with root mean square values of 0.046 (kerf angle), 0.0078 (kerf ratio), 0.044 (surface roughness), and 0.027 (drilling rate). Moth-Flame Optimization (MFO), Differential Evolution (DE), and Sine Cosine Algorithm (SCA) were used for multi-response optimization of AWJ deep hole drilling parameters. The optimal algorithm for each response was selected using Deng's similarity-based ranking. The ranking revealed SCA algorithm outperformed MFO and DE. The SCA algorithm discovered optimal parameter setting for AWJ-DHD as a water pressure of 350 MPa, standoff distance of 1.5 mm, and an abrasive mass flow rate of 300 g/min. Under these conditions, the predicted responses were a kerf angle of 0.048⁰, kerf ratio of 0.011, a surface roughness of 1.438 μm, and a drilling rate 0.769 mm/s. The validation trials using optimized parameters yielded a kerf angle of 0.047⁰, a kerf ratio of 0.066, a surface roughness of 1.40 μm, and a drilling rate of 0.769 mm/s, with percentage variations of 2.08%, 3.03%, 2.14%, and 2.65%, respectively, thereby demonstrating the efficiency of the developed machine learning model and optimization technique. The integrated machine learning and evolutionary algorithm framework improved drilling efficiency and hole quality by minimizing surface roughness.
- Klíčová slova
- Abrasive waterjet, Algorithm, Deep hole drilling, Machine learning, Optimization,
- Publikační typ
- časopisecké články MeSH
The Roma represents a transnational ethnic group, with a current European population of 8-10 million. The evolutionary process that had the greatest impact on the gene pool of the Roma population is called the founder effect. Renal hypouricemia (RHUC) is a rare heterogenous inherited disorder characterized by impaired renal urate reabsorption. The affected individuals are predisposed to recurrent episodes of exercise-induced nonmyoglobinuric acute kidney injury and nephrolithiasis. To date, more than 150 patients with a loss-of-function mutation for the SLC22A12 (URAT1) gene have been found, most of whom are Asians. However, RHUC 1 patients have been described in a variety of ethnic groups (e.g., Arab Israelis, Iraqi Jews, Caucasians, and Roma) and in geographically noncontiguous countries. This study confirms our previous findings regarding the high frequency of SLC22A12 variants observed. Frequencies of the c.1245_1253del and c.1400C>T variants were found to be 1.92% and 5.56%, respectively, in a subgroup of the Roma population from five regions in three countries: Slovakia, Czech Republic, and Spain. Our findings suggested that the common dysfunction allelic variants of URAT1 exist in the general Roma population and thus renal hypouricemia should be kept in differential diagnostic algorithm on Roma patients with defect in renal tubular urate transport. This leads to confirm that the genetic drift in the Roma have increased the prevalence of hereditary disorders caused by very rare variants in major population.
- Klíčová slova
- Roma population, SLC22A12, URAT1, prevalent variants, renal hypouricemia,
- MeSH
- efekt zakladatele MeSH
- frekvence genu MeSH
- genetické asociační studie MeSH
- heterozygot MeSH
- lidé MeSH
- močové kameny epidemiologie genetika MeSH
- molekulární evoluce MeSH
- přenašeče organických aniontů genetika MeSH
- prevalence MeSH
- proteiny přenášející organické kationty genetika MeSH
- Romové genetika MeSH
- sekvenční delece MeSH
- vrozené poruchy tubulárního transportu epidemiologie genetika MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- přenašeče organických aniontů MeSH
- proteiny přenášející organické kationty MeSH
- SLC22A12 protein, human MeSH Prohlížeč
In this paper, a permanent magnet synchronous machine (PMSM) with an auxiliary winding (AW) on the rotor is analyzed by two-dimensional approach. This PMSM with AW (AWPMSM) can be used in many applications such as propulsion system, aircraft and traction because it includes rotor flux control capability. First, the magnetic field in different parts of AWPMSM is calculated based on Maxwell equations. Then, as a consequence of the magnetic field, the torque components, including cogging, reluctance, electromagnetic and instantaneous torque are computed. Next, torque-speed characteristic has been investigated. This AWPMSM can be located in the flux weakening mode in two ways, first one is to attenuate the rotor field by changing the direction of the AW field and the other one is to adjust the armature current angle, both of them have been investigated. After that, the overload capability and temperature effects have been analyzed. Finally, using the meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, differential evolution and teaching learn base optimization the dimensions of AWPMSM and the initial angle of the rotor are determined in such a way that the torque-to-volume ratio is maximized. The influences of the type of armature winding and the magnetization patterns have also been investigated. The results obtained by the two-dimensional method have been confirmed numerically.
- Klíčová slova
- Armature reaction, Auxiliary winding, Excitation coil, Meta-heuristic algorithms, Permanent magnet,
- Publikační typ
- časopisecké články MeSH
