Combination Of Differential Evolution Research Articles

Intelligent mutation based evolutionary optimization algorithm for genomics and precision medicine.

In this paper, genomics and precision medicine have witnessed remarkable progress with the advent of high-throughput sequencing technologies and advances in data analytics. However, because of the data's great dimensionality and complexity, the processing and interpretation of large-scale genomic data present major challenges. In order to overcome these difficulties, this research suggests a novel Intelligent Mutation-Based Evolutionary Optimization Algorithm (IMBOA) created particularly for applications in genomics and precision medicine. In the proposed IMBOA, the mutation operator is guided by genome-based information, allowing for the introduction of variants in candidate solutions that are consistent with known biological processes. The algorithm's combination of Differential Evolution with this intelligent mutation mechanism enables effective exploration and exploitation of the solution space. Applying a domain-specific fitness function, the system evaluates potential solutions for each generation based on genomic correctness and fitness. The fitness function directs the search toward ideal solutions that achieve the problem's objectives, while the genome accuracy measure assures that the solutions have physiologically relevant genomic properties. This work demonstrates extensive tests on diverse genomics datasets, including genotype-phenotype association studies and predictive modeling tasks in precision medicine, to verify the accuracy of the proposed approach. The results demonstrate that, in terms of precision, convergence rate, mean error, standard deviation, prediction, and fitness cost of physiologically important genomic biomarkers, the IMBOA consistently outperforms other cutting-edge optimization methods.

Evolving support vector regression based on improved grey wolf optimization for predicting settlement during construction of high-filled roadbed

The reconstruction and expansion of highways have become the dominant focus of current infrastructure development. However, this process often results in significant differential settlement between the new and old roadbed, leading to various pavement issues. Machine learning methods undoubtedly provide a new approach for predicting roadbed settlement. However, most of the current research on predicting roadbed settlement focuses on post-construction settlement, and there is relatively little involvement in predicting settlement during roadbed filling. Therefore, it is necessary to accurately predict the settlement during the roadbed construction period. This study establishes an improved settlement prediction model for roadbed construction using a combination of differential evolution (DE), grey wolf optimization (GWO), and support vector regression (SVR). The model incorporates cumulative filling time, cumulative filling height, and daily rainfall as input parameters, while the corresponding cumulative settlement serves as the output. To evaluate the effectiveness of the proposed DE-GWO-SVR model, the training set consists of two weeks' data of sample data, while the latter week's data is used as the test set to predict settlement. Meanwhile, the predictive performance is assessed using various evaluation metrics. The coefficients of determination (R2) for the settlement prediction results were all higher than 0.95. The findings indicate that the settlement predictions made by the model align well with the measured values. Moreover, the conventional SVR and artificial neural network (ANN) models for predicting roadbed settlement have been established. The R2 for the conventional SVR model is 0.7793, while that for the DE-GWO-SVR model is 0.9977. The results demonstrate that the optimized DE-GWO-SVR model outperforms the conventional approaches across all evaluation criteria. This affirms the high prediction accuracy and strong generalization ability of the established DE-GWO-SVR settlement prediction model. Therefore, the DE-GWO-SVR settlement prediction model can realize high-precision prediction of settlement during roadbed construction. This is conducive to the timely adjustment of the filling scheme during the roadbed construction to ensure the safety and stability of the high-filled roadbed.

An optimal design of current conveyors using a hybrid-based metaheuristic algorithm

<span lang="EN-US">This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing a hybrid algorithm named DE-ACO, which is derived from the combination of differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea of this hybridization is to apply the DE algorithm for the ACO algorithm’s initialization stage. Benchmark test functions were used to evaluate the proposed algorithm’s performance regarding the quality of the optimal solution, robustness, and computation time. Furthermore, the DE-ACO has been applied to optimize the CCII+ performances. SPICE simulation is utilized to validate the achieved results, and a comparison with the standard DE and ACO algorithms is reported. The results highlight that DE-ACO outperforms both ACO and DE.</span>

An enhanced decomposition-based multi-objective evolutionary algorithm with a self-organizing collaborative scheme

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multi-objective optimization problem (MOP) into multiple single-objective subproblems using an aggregation function and optimizes them together using a collaborative approach. MOEA/D exhibits extraordinary optimization ability in solving MOPs. However, the algorithm’s performance is hampered by the single evolutionary operator, fixed control parameters, and fixed neighborhood architecture used in primal MOEA/D. To obtain the balance between convergence and diversity, a self-organizing collaborative scheme (SCS) is designed to achieve the best combination of differential evolution (DE) operator, control parameters, and neighborhood size in this paper. The SCS is integrated into MOEA/D to form the MOEA/D-SCS. In MOEA/D-SCS, firstly, the evolutionary process is separated into three stages: early, middle, and late, and different indicators are utilized to screen elite and inferior solutions for the early and late stages. Secondly, different evolutionary strategies are developed for the three stages and the various types of solutions to help boost the algorithm’s efficiency. Then, to enhance convergence and diversity in different evolutionary stages, a neighborhood reconstruction strategy is proposed to assign different neighborhood sizes according to the neighborhood information. Finally, the capability of MOEA/D-SCS was tested using three standard test suites, ZDT, DTLZ, and UF, and a MOP of sewage treatment. The simulation results demonstrate that the proposed MOEA/D-SCS can achieve better optimization results and show stronger optimization ability in most cases compared to state-of-the-art evolutionary algorithms, such as MOEA/D-DE, MOEA/D-STM, MOEA/D-FRRMAB, MOEA/D-DRA, NSGA-II, and dMOPSO, especially in problems with multiple peaks, complex Pareto fronts, and practical engineering optimization problems.

Intelligent parameter identification and prediction of variable time fractional derivative and application in a symmetric chaotic financial system

Identifying parameters of financial and economic models with chaotic dynamics is an important, yet daunting challenge because of the complexities there exist in these chaotic systems. Although several studies have been devoted to understanding the mechanism of financial systems, the application of most state-of-the-art methods to these systems is completely ignored. To the best of our knowledge, no study identifies and predicts fractional derivatives of economic models. The current study has been motivated by this issue. Employing the Differential Evolution algorithm, Gaussian process regression, and neural networks, we propose a novel algorithm to identify and predict parameters of a symmetric chaotic fractional financial model. In the first step, a combination of Differential Evolution and the Gaussian process is utilized to identify time-varying fractional-order derivatives. Then, through numerical simulation, it is demonstrated that although this method provides bright results for estimation and interpolation purposes, it fails to extrapolate and predict time-varying parameters. Hence, in the next step, by taking advantage of a recurrent neural network, the proposed method is promoted and modified for extrapolation. Numerical simulations firmly confirm the excellent performance of the improved algorithm for both interpolation and extrapolation purposes.

Brain Tumor Detection Based on Multilevel 2D Histogram Image Segmentation Using DEWO Optimization Algorithm

Brain tumor detection from magnetic resonance (MR)images is a tedious task but vital for early prediction of the disease which until now is solely based on the experience of medical practitioners. Multilevel image segmentation is a computationally simple and efficient approach for segmenting brain MR images. Conventional image segmentation does not consider the spatial correlation of image pixels and lacks better post-filtering efficiency. This study presents a Renyi entropy-based multilevel image segmentation approach using a combination of differential evolution and whale optimization algorithms (DEWO) to detect brain tumors. Further, to validate the efficiency of the proposed hybrid algorithm, it is compared with some prominent metaheuristic algorithms in recent past using between-class variance and the Tsallis entropy functions. The proposed hybrid algorithm for image segmentation is able to achieve better results than all the other metaheuristic algorithms in every entropy-based segmentation performed on brain MR images.

Position-only synthesis of uniformly excited elliptical antenna arrays with minimum element spacing constraint

Pattern synthesis of non-uniform elliptical antenna arrays is presented in this paper. Only the element positions of the antenna arrays are optimized by the combination of differential evolution (DE) and invasive weed optimization (IWO) to reduce the peak side lobe level (PSLL) of the radiation pattern. In order to avoid the overlap of the array elements, the minimum spacing of the adjacent elements is constrained. Also, the beam width of the radiation pattern can be constrained effectively. Three elliptical antenna arrays that have 8, 12, and 20 elements are investigated. The synthesis results show that the introduced method can present a good side lobe reduction for the radiation pattern. Compared with other optimization methods, the method proposed in this paper can obtain better performance.

A novel nature-inspired optimization based neural network simulator to predict coal grindability index

PurposeHardgrove grindability index (HGI) is an important physical parameter used to demonstrate the relative hardness of coal particles. Modeling of HGI based on coal conventional properties is a quite complicated procedure. The paper aims to develop a new accurate model for prediction of HGI that is called optimized evolutionary neural network (OPENN).Design/methodology/approachThe procedure for generation of the proposed OPENN predictive model was performed in two stages. In the first stage, as the high dimensionality involved in the input space, a correlation-based feature selection (CFS) algorithm was used to select the most important influencing variables for HGI prediction. In the second stage, a combination of differential evolution (DE) and biography-based optimization (BBO) algorithms as a global search method were applied to evolve weights of a multi-layer perception neural network.FindingsThe proposed OPENN was examined and compared with other typical models using a wide range of Kentucky coal samples. The testing results showed that the accuracy of the proposed OPENN model is significantly better than the other typical models and can be considered as a promising alternative for HGI prediction.Originality/valueAs HGI test is relatively expensive procedure, there is an economical interest on HGI modeling based on coal conventional properties (proximate, ultimate and petrography); the proposed OPENN model to estimate HGI would be a valuable and practical tool for coal industry.

Optimization with a novel hybrid algorithm and applications

A novel hybrid algorithm (DPD) by combination of Differential Evolution (DE) and Particle Swarm Optimization (PSO) is proposed for solving unconstrained and constrained optimization problems. It is based on ‘tri-population’ environment. Initially, the whole population (in increasing order of fitness) is divided into three groups—Inferior Group, Mid Group and Superior Group. DE is employed in the inferior and superior groups, whereas PSO is used in the mid-group. Two strategies namely Elitism (to retain the best obtained values so far) and Non-redundant search (to maintain diversity in the population) have been employed in DPD cycle. Since the performance of DE is sensitive to the choice of the mutation strategy. Therefore suitable mutation strategy for both DEs used in DPD is investigated over a set of 8 popular mutation strategies. Combination of 8 mutation strategies is generated 64 different variants of DPD. Top 4 DPDs are investigated through 20 benchmark function. Based on the ‘performance’ analysis best DPD is reported using 20 unconstrained benchmark functions, 13 constrained benchmark functions, 6 unconstrained and 3 constrained engineering design problems. To show superiority and effectiveness, best DPD is compared with various state-of-the-art approaches in terms of quality of solutions.

Evaluating the Multiple Offspring Sampling framework on complex continuous optimization functions

In this contribution we present a study on the combination of Differential Evolution and the IPOP-CMA-ES algorithms. The hybrid algorithm has been constructed by using the Multiple Offspring Sampling framework, which allows the seamless combination of multiple metaheuristics in a dynamic algorithm capable of adjusting the participation of each of the composing algorithms according to their current performance. In this study we analyze the existing synergies, if any, emerging from the combination of the two algorithms. For this purpose, the COCO suite used in BBOB 2009 and 2010 Workshops has been used. The experimental results on the noiseless testbed show a robust behavior of the algorithm and a good scalability as the dimensionality increases. In the noisy testbed, the algorithm shows a good performance on functions with moderate to severe noise.

Speed Control of Switched Reluctance Motor Using New Hybrid Particle Swarm Optimization

Problem statement: The main objective of this research is to obtain the speed control of switched reluctance motor with minimum settling time and without overshoot. Approach: A new algorithm has been developed with the combination of differential evolution and particle swarm optimization and applied for speed control of switched reluctance motor under sudden change in speed. Also speed control of switched reluctance motor was obtained by other artificial intelligence methods such as fuzzy logic controller, fuzzy PI controller and particle swarm optimization based tuning of fuzzy PI controller. Matlab/Simulink environment was used for the simulation. Results: Results are discussed and tabulated based on the performance of the controllers. Conclusion: From the comparison of all above methods, the algorithm has given better results in speed response than other controllers.

Pattern recognition of overnight intracranial pressure slow waves using morphological features of intracranial pressure pulse

This study aimed to develop a new approach to detect intracranial pressure (ICP) slow waves based on morphological changes of ICP pulse waveforms. A recently proposed Morphological Clustering and Analysis of ICP Pulse (MOCAIP) algorithm was utilized to calculate a set of metrics that characterize ICP pulse morphology. A regularized linear quadratic classifier was used to test the hypothesis that classification between ICP slow wave and flat ICP could be achieved using features composed of mean values and dispersion of 24 MOCAIP metrics. To optimize the classification performance, three feature selection techniques (differential evolution, discriminant analysis and analysis of variance) were applied to find an optimal set of MOCAIP metrics under different criteria. In addition, we selected three sets of metrics common to those found by combination of two selection methods, to be used as classification features (differential evolution and analysis of variance, discriminant analysis and analysis of variance, and combination of differential evolution and discriminant analysis). To test the approach, a total of 276 selections of ICP recordings corresponding to two patterns without waves and containing slow waves were obtained from overnight ICP studies of 44 hydrocephalus patients performed at the UCLA Adult Hydrocephalus Center. Our results showed that the best classification performance of differentiation of slow waves from the ICP recording without slow waves was obtained using the combination of metrics common to both differential evolution and analysis of variance methods; achieving an accuracy of 89%, specificity 96%, and sensitivity 83%.

Advanced design and optimization of single mode photonic crystal fibers

This paper proposes a combination of differential evolution (DE) and estimation of distribution algorithm (EDA) to design photonic crystal fiber structures with desired properties over the C communication band. In order to determine the properties of PCFs such as dispersion, dispersion slope and loss, an artificial intelligence method, the Nero-Fuzzy system, is applied. In addition, a special cost function which simultaneously includes the confinement loss, dispersion and its slope is used in the proposed design approach. The results revealed that the proposed method is a powerful tool for solving this optimization problem. The optimized PCF exhibits an ultra low confinement loss and low dispersion at 1.55 µm wavelength with a nearly zero dispersion slope over the C communication band.

Design of ultra-low and ultra-flattened dispersion single mode photonic crystal fiber by DE/EDA algorithm

This paper proposes a combination of differential evolution (DE) and estimation of distribution algorithm (EDA) to design photonic crystal fiber structures with desired properties over the C communication band. In order to determine the effective index of propagation of the mode and then, the other properties of structure, a finite difference frequency domain (FDFD) solver is applied. The results revealed that the proposed method is a powerful tool for solving this optimization problem. The optimized PCF exhibits a dispersion of 0.22 ps nm−1 km−1 at 1.55 µm wavelength with a variance of ±0.4 ps nm−1 km−1 over the C communication band and a nearly zero dispersion slope.