Advanced Differential Evolution Research Articles

Advanced differential evolution for gender-aware English speech emotion recognition

Speech emotion recognition (SER) technology involves feature extraction and prediction models. However, recognition efficiency tends to decrease because of gender differences and the large number of extracted features. Consequently, this paper introduces a SER system based on gender. First, gender and emotion features are extracted from speech signals to develop gender recognition and emotion classification models. Second, according to gender differences, distinct emotion recognition models are established for male and female speakers. The gender of speakers is determined before executing the corresponding emotion model. Third, the accuracy of these emotion models is enhanced by utilizing an advanced differential evolution algorithm (ADE) to select optimal features. ADE incorporates new difference vectors, mutation operators, and position learning, which effectively balance global and local searches. A new position repairing method is proposed to address gender differences. Finally, experiments on four English datasets demonstrate that ADE is superior to comparison algorithms in recognition accuracy, recall, precision, F1-score, the number of used features and execution time. The findings highlight the significance of gender in refining emotion models, while mel-frequency cepstral coefficients are important factors in gender differences.

ADE: advanced differential evolution

ADE: advanced differential evolution

A DiffeRential Evolution Adaptive Metropolis (DREAM)-based inverse model for continuous release source identification in river pollution incidents: Quantitative evaluation and sensitivity analysis

The identification of continuous pollution sources for rivers is of great concern for emergency response. Most studies focused on instantaneous river pollution sources and associated incidents. There is a dire need to address continuous pollution sources, as pollutant discharge may impose a major impact on the water ecosystem. Therefore, in this study, a novel inverse model is proposed to identify the continuous point sources in river pollution incidents that would estimate the source strength, location, release time, and spill time. The proposed inverse model combines the advanced DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm and the forward transport advection-dispersion equation to infer the posterior probability distribution of source parameters for quantifying uncertainties. In addition, the performance of the DREAM-based model is compared with those of the Metropolis-Hastings (MH)-based and genetic algorithm (GA)-based models. The results show that the DREAM-based model performs accurately for both the hypothetical and the field tracer cases. The comparative analysis shows that the DREAM-based model performs better in saving computation time, improving the accuracy of results, and reconstructing pollutant concentrations. Observation errors significantly influence the accuracy of the identification results from the DREAM-based model. In addition, a comprehensive sensitivity analysis of the DREAM-based model is conducted. The identification results from the DREAM-based model are sensitive to the dispersion coefficient and river velocity. The accuracy of the inverse model could be improved by increasing the monitoring number and by monitoring locations closer to the spill site. The findings of this study can improve decision-making during emergency responses to sudden river pollution incidents.

IFA-EO: An improved firefly algorithm hybridized with extremal optimization for continuous unconstrained optimization problems

As one of the evolutionary algorithms, firefly algorithm (FA) has been widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and is easily trapped into local optimum. To tackle these defects, this paper proposes an improved FA combined with extremal optimization (EO), named IFA-EO, where three strategies are incorporated. First, to balance the tradeoff between exploration ability and exploitation ability, we adopt a new attraction model for FA operation, which combines the full attraction model and the single attraction model through the probability choice strategy. In the single attraction model, small probability accepts the worse solution to improve the diversity of the offspring. Second, the adaptive step size is proposed based on the number of iterations to dynamically adjust the attention to the exploration model or exploitation model. Third, we combine an EO algorithm with powerful ability in local-search into FA. Experiments are tested on two group popular benchmarks including complex unimodal and multimodal functions. Our experimental results demonstrate that the proposed IFA-EO algorithm can deal with various complex optimization problems and has similar or better performance than the other eight FA variants, three EO-based algorithms, and one advanced differential evolution variant in terms of accuracy and statistical results.

Parameter identification and state estimation for nuclear reactor operation digital twin

Reactor Operation Digital Twin (RODT) is now receiving increasing attention and investment in nuclear engineering domain. A prototype of a RODT was first brought out by Gong et al. at Nuclear Power Institute of China. The RODT contains a forward solver for online real-time simulation and an inverse problem solver for parameter identification and state estimation. To further improve the efficiency and accuracy of RODT and promote the practical deployment of RODT (i) we first propose an advanced differential evolution algorithm to upgrade the inverse solver; (ii) then we bring out a systematical uncertainty quantification of RODT considering assimilation noisy observations. The accuracy and validity of the proposed RODT are tested along one cycle operating stage of HPR1000, at various operating temperatures, control rod steps, general power level and the inlet temperature of a practical nuclear reactor core. Numerous numerical results confirm its potential for practical engineering applications for on-line parameter identification and state estimation.

Engineering Design Optimization Using an Advanced Hybrid Algorithm

An advanced hybrid algorithm (haDEPSO) proposed in this paper for engineering design optimization problems. It integrated with suggested advanced differential evolution (aDE) and particle swarm optimization (aPSO). In aDE introduced a novel mutation, crossover and selection strategy, to avoiding premature convergence. And aPSO consists of novel gradually varying parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also, in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. Proposed hybrid haDEPSO as well as its integrating component aDE and aPSO has been applied to five engineering design optimization problems. Numerical, statistical and graphical experiments (best, worst, mean and standard deviation plus convergence analysis) confirm the superiority of the proposed algorithms over many state-of-the-art algorithms.

Solving systems of nonlinear equations with real world problems using an advanced hybrid algorithm

Systems of nonlinear equations (SNLEs) existed in various disciplines like engineering and applied mathematics. Finding solutions to SNLES is one of the most challenging problems. An advanced hybrid algorithm (haDEPSO) is proposed in this paper for finding the solution of SNLEs and real world problems, based on multi-population approach. Suggested advanced differential evolution (aDE) and particle swarm optimisation (aPSO) integrated with haDEPSO where in aDE a novel mutation strategy, crossover probability and slightly changed selection scheme is introduced (to avoid premature convergence) and novel gradually varying parameters familiarised in aPSO (to escape stagnation). Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. Performance of proposed haDEPSO as well as its integrating component aDE and aPSO are used to solve three SNLEs and three complex real world problems. Comparative analysis confirms superiority of the proposed algorithms.

Engineering Design Optimization Using an Advanced Hybrid Algorithm

An advanced hybrid algorithm (haDEPSO) proposed in this paper for engineering design optimization problems. It integrated with suggested advanced differential evolution (aDE) and particle swarm optimization (aPSO). In aDE introduced a novel mutation, crossover and selection strategy, to avoiding premature convergence. And aPSO consists of novel gradually varying parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also, in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. Proposed hybrid haDEPSO as well as its integrating component aDE and aPSO has been applied to five engineering design optimization problems. Numerical, statistical and graphical experiments (best, worst, mean and standard deviation plus convergence analysis) confirm the superiority of the proposed algorithms over many state-of-the-art algorithms.

An advanced hybrid algorithm for nonlinear function optimization with real world applications

SummaryThis article presents an advanced hybrid algorithm (haDEPSO) for nonlinear constrained function optimization problem. It consists of the advised advanced differential evolution, that is, aDE (wherein a novel mutation, crossover, and selection strategy is introduced, to avoid premature convergence) and particle swarm optimization, namely, aPSO (in which novel gradually varying parameters is utilized, to escape stagnation). The proposed haDEPSO achieve better results as aDE and aPSO provides diverse convergence characteristic to the solution space. Moreover, in haDEPSO distinct population is merged with other in a predefined way, to balance between exploration and exploitation. Efficiency of the proposed hybrid haDEPSO along with its integrating component aDE and aPSO are inspected on IEEE CEC'2006 constrained benchmark functions and IEEE CEC'2011 real world problems. According to the comparison results, the proposed methods achieved better results than traditional DE and PSO with their hybrids as well as over many state‐of‐the‐art algorithms.

An adaptive differential evolution with decomposition for photovoltaic parameter extraction.

Photovoltaic (PV) parameter extraction plays a key role in establishing accurate and reliable PV models based on the manufacturer's current-voltage data. Owning to the characteristics such as implicit and nonlinear of the PV model, it remains a challenging and research-meaningful task in PV system optimization. Despite there are many methods that have been developed to solve this problem, they are often consuming a great deal of computing resources for more satisfactory results. To reduce computing resources, in this paper, an advanced differential evolution with search space decomposition is developed to effectively extract the unknown parameters of PV models. In proposed approach, a recently proposed advanced differential evolution algorithm is used as a solver. In addition, a search space decomposition technique is introduced to reduce the dimension of the problem, thereby reducing the complexity of the problem. Three different PV cell models are selected for verifying the performance of proposed approach. The experimental result is firstly compared with some representative differential evolution algorithms that do not use search space decomposition technique, which demonstrates the effectiveness of the search space decomposition. Moreover, the comparison results with some reported well-established parameter extraction methods suggest that the proposed approach not only obtains accurate and reliable parameters, but also uses the least computational resources.

Hybridizing Particle Swarm Optimization with JADE for continuous optimization

As a population-based random search optimization technique, particle swarm optimization (PSO) has become an important branch of swarm intelligence (SI). To utilizing the advantage of operations in different SI, this study proposed a hybrid of multi-crossover operation and adaptive differential evolution with optional external archive (JADE), named PSOJADE, to balance the global and local search capabilities. In the experiments, the proposed algorithm is compared with six other advanced differential evolution (DE), PSO, and hybrid of DE and PSO techniques using 30 benchmark functions in CEC2017. To evaluate the effectiveness of the proposed PSOJADE more comprehensively, the experiments were implemented on 10-D, 30-D, and 50-D respectively. The experimental results indicate that the proposed algorithm yields better solution accuracy than the other techniques on 10-D, 30-D, and 50-D meanwhile.

Markov chain Monte Carlo algorithms for target‐oriented and interval‐oriented amplitude versus angle inversions with non‐parametric priors and non‐linear forward modellings

ABSTRACTIn geophysical inverse problems, the posterior model can be analytically assessed only in case of linear forward operators, Gaussian, Gaussian mixture, or generalized Gaussian prior models, continuous model properties, and Gaussian‐distributed noise contaminating the observed data. For this reason, one of the major challenges of seismic inversion is to derive reliable uncertainty appraisals in cases of complex prior models, non‐linear forward operators and mixed discrete‐continuous model parameters. We present two amplitude versus angle inversion strategies for the joint estimation of elastic properties and litho‐fluid facies from pre‐stack seismic data in case of non‐parametric mixture prior distributions and non‐linear forward modellings. The first strategy is a two‐dimensional target‐oriented inversion that inverts the amplitude versus angle responses of the target reflections by adopting the single‐interface full Zoeppritz equations. The second is an interval‐oriented approach that inverts the pre‐stack seismic responses along a given time interval using a one‐dimensional convolutional forward modelling still based on the Zoeppritz equations. In both approaches, the model vector includes the facies sequence and the elastic properties of P‐wave velocity, S‐wave velocity and density. The distribution of the elastic properties at each common‐mid‐point location (for the target‐oriented approach) or at each time‐sample position (for the time‐interval approach) is assumed to be multimodal with as many modes as the number of litho‐fluid facies considered. In this context, an analytical expression of the posterior model is no more available. For this reason, we adopt a Markov chain Monte Carlo algorithm to numerically evaluate the posterior uncertainties. With the aim of speeding up the convergence of the probabilistic sampling, we adopt a specific recipe that includes multiple chains, a parallel tempering strategy, a delayed rejection updating scheme and hybridizes the standard Metropolis–Hasting algorithm with the more advanced differential evolution Markov chain method. For the lack of available field seismic data, we validate the two implemented algorithms by inverting synthetic seismic data derived on the basis of realistic subsurface models and actual well log data. The two approaches are also benchmarked against two analytical inversion approaches that assume Gaussian‐mixture‐distributed elastic parameters. The final predictions and the convergence analysis of the two implemented methods proved that our approaches retrieve reliable estimations and accurate uncertainties quantifications with a reasonable computational effort.

Optimal Control of DFIG Wind Energy System in Multi-machine Power System using Advanced Differential Evolution

The Vector controlled doubly fed induction generator (DFIG) wind energy system in a multi-machine power system is very sensitive to faults on transmission lines and thus affects the stability of the system. The performance of vector controlled DFIG highly depends on PI controller parameters. The objective of this paper is to optimize the performance of vector controlled DFIG in multi-machine power systems under faulty conditions by tuning the parameters using advanced differential evolution algorithm. An objective function is formulated, which drives the system with good voltage profile, reduced power oscillations under faulty conditions. The parameters of PI controller are tuned using Composite Differential Evolution algorithm. Simulations are performed on a two area benchmark problem in Matlab/Simulink. The simulation results of the optimized DFIG wind energy system are compared with the actual existed system. The simulation results show that the overall performance and stability of the optimized system are enhanced.

Robot manipulator identification based on adaptive multiple-input and multiple-output neural model optimized by advanced differential evolution algorithm

This article proposes a novel advanced differential evolution method which combines the differential evolution with the modified back-propagation algorithm. This new proposed approach is applied to train an adaptive enhanced neural model for approximating the inverse model of the industrial robot arm. Experimental results demonstrate that the proposed modeling procedure using the new identification approach obtains better convergence and more precision than the traditional back-propagation method or the lonely differential evolution approach. Furthermore, the inverse model of the industrial robot arm using the adaptive enhanced neural model performs outstanding results.