Self-adaptive Differential Evolution Algorithm Research Articles

Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial manipulators, this article proposes a method for solving the IK of manipulators using an improved self-adaptive mutation differential evolution (DE) algorithm. First, based on the self-adaptive DE algorithm, a new adaptive mutation operator and adaptive scaling factor are proposed to change the control parameters and differential strategy of the DE algorithm. Then, an error-related weight coefficient of the objective function is proposed to balance the weight of the position error and orientation error in the objective function. Finally, the proposed method is verified by the benchmark function, the 6-DOF and 7-DOF serial manipulator model. Experimental results show that the improvement of the algorithm and improved objective function can significantly improve the accuracy of the IK. For the specified points and random points in the feasible region, the proportion of accuracy meeting the specified requirements is increased by 22.5% and 28.7%, respectively.

A hybrid self-adaptive invasive weed algorithm with differential evolution

The invasive weed algorithm (IWO) is a meta-heuristic algorithm, which is an effective and promising optimiser to address the optimisation problems. In this study, a hybrid algorithm based on the self-adaptive invasive weed algorithm (IWO) and differential evolution algorithm (DE), named SIWODE, is proposed to address the continuous optimisation problems. In the proposed SIWODE, first, the two parameters are adaptively proposed to improve the convergence speed of the algorithm. Second, the crossover and mutation operations are introduced in SIWODE to improve the population diversity and increase the exploration capability during the iterative process. Furthermore, a local perturbation strategy is presented to improve exploitation ability during the late process. The exploration and exploitation ability of the algorithm is effectively balanced by cooperative mechanisms. The experiment results of SIWODE show that the SIWODE has the superior searching quality and stability than other mentioned approaches.

Predictive modeling in a steelmaking process using optimized relevance vector regression and support vector regression

The existence of contaminants in metal alloys products is the main problem affecting the product quality, which is an important requirement for competitiveness in industries. This paper proposes the application of a relevance vector machine for regression (RVR) and a support vector machine for regression (SVR) optimized by a self-adaptive differential evolution algorithm for regression to model the phosphorus concentration levels in a steelmaking process based on actual data. In general, the appropriate choice of learning hyperparameters is a crucial step in obtaining a well-tuned RVM and SVM. To address this issue, we apply a self-adaptive differential evolution algorithm, which is an evolutionary algorithm for global optimization. We compare the performance of the RVR and SVR models with the ridge regression, multiple linear regression, model trees, artificial neural network, and random vector functional link neural network models. RVR and SVR models have smaller RMSE values and better performance than the other models. Our study indicates that the RVR and SVR models are adequate tools for predicting the phosphorus concentration levels in the steelmaking process.

Wind turbine power output prediction using a new hybrid neuro-evolutionary method

Short-term wind power prediction is challenging due to the chaotic characteristics of wind speed. Since, for wind power industries, designing an accurate and reliable wind power forecasting model is essential, we deployed a novel composite deep learning-based evolutionary approach for accurate forecasting of the power output in wind-turbine farms, which is developed in three stages. At the beginning stage (pre-processing), the k-means clustering method and an autoencoder are employed to detect and filter noise in the SCADA measurements. In the Next step (decomposition), in order to decompose the SCADA time-series data, we proposed a new hybrid variational mode decomposition (HVMD) method, that consists of VMD and two heuristics: greedy Nelder-Mead search algorithm (GNM) and adaptive randomised local search (ARLS). Both heuristics are applied to tune the hyper-parameters of VMD that results in improving the performance of the forecasting model. In the third phase, based on prior knowledge that the underlying wind patterns are highly non-linear and diverse, we proposed a novel alternating optimisation algorithm that consists of self-adaptive differential evolution (SaDE) algorithm and sine cosine optimisation method as a hyper-parameter optimizer and then combine with a recurrent neural network (RNN) called Long Short-term memory (LSTM). This framework allows us to model the power curve of a wind turbine on a farm. A historical dataset from supervisory control and data acquisition (SCADA) systems were applied as input to estimate the power output from an onshore wind farm in Sweden. Two short time forecasting horizons, including 10 min ahead and 1 h ahead, are considered in our experiments. The achieved prediction results supported the superiority of the proposed hybrid model in terms of accurate forecasting and computational runtime compared with earlier published hybrid models applied in this paper.

A self-adaptive multi-objective dynamic differential evolution algorithm and its application in chemical engineering

This paper proposes a new multi-objective dynamic differential evolution algorithm with parameter self-adaptive strategies, named SA-MODDE. All components of the algorithm are synergically designed to reach its full potential, containing parental selection, mutation strategy, parameter setting, survival selection, constraint handling, and termination criteria. The improvement measures emphasize exploiting Pareto dominance information more efficiently. Particularly, parameter adaptation schemes are introduced based on both prior knowledges of current individual and feedback information on previous promising solutions, and their effectiveness is validated by comparison with three fixed-parameter combinations. Extensive numerical tests are conducted on multiple test suites with five state-of-the-art peer competitors. The statistical results demonstrated that the SA-MODDE exhibits good proximity and diversity in dealing with benchmark functions with various characteristics. Three industrial (bio)chemical processes, including two optimal control and one reformulated constrained tri-objective, are investigated to show the feasibility and robustness of the SA-MODDE.

Multi-performance based computational model for the cuboid open traveling salesman problem in a smart floating city

The term “smart city” has been emerged as a novel solution to uphold the useless urban areas and the term has taken the advantage of sustainable and environmental resources. On the other hand, the term “floating city” has been studied for just only a few years as alternative living spaces for humanity across the world since land scarcity has already begun. Therefore, in this research, we propose multi-objective optimization algorithms to obtain the Pareto front solutions for the cuboid open traveling salesman problem (COTSP) in a “smart floating city” context. Given n nodes and the distances between each pair of nodes, the COTSP in this paper aims to find the shortest possible tour with a traveling distance that starts from the depot (i.e., node 1) and visits each node exactly once without needing to return to the depot. As known, a cuboid has height, length, and depth and the COTSP defines its x, y, z coordinates as a cuboid corresponding to height, length, and depth. In addition to the traveling distance, the platform (building breakwaters) cost is measured by the z coordinates (depths) of the nodes/platforms that represent both the platforms below the sea level. Note that unlike the traditional TSP, it has a variable seed number and a variable number of nodes/platforms in each solution. The paper aims to find the Pareto front solutions by minimizing the traveling distance and platform cost of the infrastructures below the sea level simultaneously. We develop a multi-objective self-adaptive differential evolution (MOJDE) algorithm, a nondominated sorting genetic algorithm (NSGAII), and a harmony search (MOHS) algorithm to solve the problem in such a way that we minimize the traveling distance while minimizing the platform cost simultaneously. All algorithms are compared to each other. The computational results show that the MOJDE and NSGAII algorithms outperform the MOHS algorithm in terms of commonly used performance measures from the literature.

Inverse kinematics for cooperative mobile manipulators based on self-adaptive differential evolution.

This article presents an approach to solve the inverse kinematics of cooperative mobile manipulators for coordinate manipulation tasks. A self-adaptive differential evolution algorithm is used to solve the inverse kinematics as a global constrained optimization problem. A kinematics model of the cooperative mobile manipulators system is proposed, considering a system with two omnidirectional platform manipulators with n DOF. An objective function is formulated based on the forward kinematics equations. Consequently, the proposed approach does not suffer from singularities because it does not require the inversion of any Jacobian matrix. The design of the objective function also contains penalty functions to handle the joint limits constraints. Simulation experiments are performed to test the proposed approach for solving coordinate path tracking tasks. The solutions of the inverse kinematics show precise and accurate results. The experimental setup considers two mobile manipulators based on the KUKA Youbot system to demonstrate the applicability of the proposed approach.

Unfolding neutron spectra from water-pumping-injection multilayered concentric sphere neutron spectrometer using self-adaptive differential evolution algorithm

A self-adaptive differential evolution neutron spectrum unfolding algorithm (SDENUA) is established in this study to unfold the neutron spectra obtained from a water-pumping-injection multilayered concentric sphere neutron spectrometer (WMNS). Specifically, the neutron fluence bounds are estimated to accelerate the algorithm convergence, and the minimum error between the optimal solution and input neutron counts with relative uncertainties is limited to 10–6 to avoid unnecessary calculations. Furthermore, the crossover probability and scaling factor are self-adaptively controlled. FLUKA Monte Carlo is used to simulate the readings of the WMNS under (1) a spectrum of Cf-252 and (2) its spectrum after being moderated, (3) a spectrum used for boron neutron capture therapy, and (4) a reactor spectrum. Subsequently, the measured neutron counts are unfolded using the SDENUA. The uncertainties of the measured neutron count and the response matrix are considered in the SDENUA, which does not require complex parameter tuning or an a priori default spectrum. The results indicate that the solutions of the SDENUA agree better with the IAEA spectra than those of MAXED and GRAVEL in UMG 3.1, and the errors of the final results calculated using the SDENUA are less than 12%. The established SDENUA can be used to unfold spectra from the WMNS.

An RUL prediction approach for lithium-ion battery based on SADE-MESN

Accurately predicting the remaining useful life of lithium-ion batteries is critical to battery health management systems. Aiming at the problems of low long-term prediction accuracy, unstable model output, and difficult key parameter selection, this paper proposes a self-adaptive differential evolution optimized monotonic echo state network prediction method. First, we analyze the life decay characteristics of Li-ion batteries and select appropriate indirect health indicators to replace the capacity based on the partial correlation coefficient analysis. Then use the self-adaptive differential evolution algorithm to optimize the free parameters of the monotonic echo state network to maintain the monotonic relationship between input and output. Finally, the remaining useful life indirect prediction model is established. This paper uses NASA Li-ion battery experimental data and independent experimental data to verify the feasibility, followed by the different starting points experiments and cut-off voltage experiments. The accuracy of the proposed method is compared with other commonly used artificial intelligence prediction algorithms. Experimental results prove that this method has high prediction accuracy and stable output.

A Self-Adaptive Differential Evolution Algorithm for Scheduling a Single Batch-Processing Machine With Arbitrary Job Sizes and Release Times.

Batch-processing machines (BPMs) can process a number of jobs at a time, which can be found in many industrial systems. This article considers a single BPM scheduling problem with unequal release times and job sizes. The goal is to assign jobs into batches without breaking the machine capacity constraint and then sort the batches to minimize the makespan. A self-adaptive differential evolution algorithm is developed for addressing the problem. In our proposed algorithm, mutation operators are adaptively chosen based on their historical performances. Also, control parameter values are adaptively determined based on their historical performances. Our proposed algorithm is compared to CPLEX, existing metaheuristics for this problem and conventional differential evolution algorithms through comprehensive experiments. The experimental results demonstrate that our proposed self-adaptive algorithm is more effective than other algorithms for this scheduling problem.

Relevance vector machine with tuning based on self-adaptive differential evolution approach for predictive modelling of a chemical process

In the past decade, relevance vector machines have gained the attention of many researchers, and this machine learning technique is a Bayesian sparse kernel method, both for classification and regression problems. In general, the choice of appropriate learning hyperparameters is a crucial step in obtaining a well-tuned model. To overcome this issue, we apply a self-adaptive differential evolution algorithm. In this paper, we propose a relevance vector machine for regression combined with a novel self-adaptive differential evolution approach for predictive modelling of phosphorus concentration levels in a steelmaking process with real data. We compared the performance of proposed relevance vector machine (RVM) with other machine learning techniques, such as random forest (RF), artificial neural network (ANN), K-nearest neighbors (K-NN), and also with statistical learning techniques as, Beta regression model and multiple linear regression model. The RVM has performance better than RF, ANN, K-NN, and statistical techniques used. Our study indicates that RVM models are an adequate tool for the prediction of the phosphorus concentration levels in the steelmaking process.

A Self-Adaptive Differential Evolution Algorithm Using Oppositional Solutions and Elitist Sharing

Differential evolution (DE) has been applied to solve complex optimization problems. An effective DE algorithm should be convergent and able to jump out of the local optimal solution. Motivated by these considerations, an improved differential evolution is proposed, which is based on the oppositional solution, elite sharing schemes, the heuristic crossover operator and combined with a self-adaptive parameter setting strategy. The algorithm is denoted as SOSESDE. First, in the early stage of evolution, the disturbance oppositional strategy is applied to the worse individuals to increase the search space since the oppositional search strategy will generate redundant offspring by the same genetic operation, which can be avoided by random perturbation. Then, an elite sharing scheme is used for information exchange. In this scheme, the K-means is first used to divide the present population into several subpopulations, and then, the elitist in each subpopulation is taken for mutation operation. In addition, the related parameters F and CR are self-adaptively adjusted based on the results of the Wilcoxon signed-rank test and the probability that a parent is selected for the next generation. Besides, the heuristic crossover operator is constructed by using uniform design method. Finally, 53 benchmark functions are optimized using SOSESDE, and the results are compared with those of various state-of-the-art algorithms. The experiments show that compared to these algorithms, SOSESDE exhibits better performance.

A Characterization Method of Thin Film Parameters Based on Adaptive Differential Evolution Algorithm

According to the transmission mode of polarized light in Mueller ellipsometry, a characterization method for the thickness and optical constants of isotropic nano films based on Self-Adaptive Differential Evolution algorithm (SADE) is proposed. By establishing the least square model of the output light intensity with respect to the Mueller matrix of the standard sample to be measured, the elements of the Mueller matrix are solved by using the Sade algorithm, and the Mueller spectral curve obtained by fitting is compared with that measured by DRC-MME, and the film thickness is calculated by using the transfer matrix. The SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> / Si standard samples with calibration values of 100.4 nm and 121.56 nm are simulated and calculated. The experiment shows that: when numbers of iterations accumulated to 65 and 80 respectively, the residual square sum of the light intensity of the objective function converges to the minimum values of 1.24 and 1.02. The calculated film thickness values are 101.25nm and 120.53nm respectively, and the relative errors are both less than 1%. It proves the characteristics of simple calculation, fast convergence and accurate global optimal solution.

A Health Index Construction Framework for Prognostics Based on Feature Fusion and Constrained Optimization

The actual health state of a system is difficult to measure in online applications. Hence, features extracted from the sensor data are usually fused as a composite health index (HI) to represent the system status. If the HI shows explicit increasing or decreasing trend, prognostic methods are applied to extrapolate the future degradation and predict the remaining useful life (RUL). However, the practical requirements of prognostics for HI construction are often not considered. Thus, the advanced index may not capture the fault progression well, which would lead to inaccurate predictions. In this article, an HI construction frame-work based on feature fusion and constrained optimization is proposed. Multiple features are fused with weighted sum and nonlinear fusion functions. Three desired properties of HI for prognostics are considered to build an optimization model of feature weights. To solve this multi-variable model effectively, a self-adaptive differential evolution (SADE) algorithm is proposed. Then, a state-space model based on Wiener process is applied to the constructed HIs for online RUL prediction. To evaluate the performance of the HIs, a consistency-based metric is developed. Illustrative examples using two industrial datasets demonstrate the efficiency and adaptability of the proposed methods.

Dynamic Security and Stability Region Under Different Renewable Energy Permeability in IENGS System

The integration of renewable energy sources (RESs) remains growing rapidly in recent years. At the same time, security problems after contingencies were attracting great attention, due to deficiencies for the stable operation, even could induce the collapse of integrated energy systems with the deep interconnection of such RESs. To existing issues, the hyper-plane (HP) expression is proved to be an excellent tool for power system situational awareness and stability-constrained operation in practical dynamic security stability regions (PDSSR). In this study, a concept and model were proposed for a high renewable energy penetration of integrated electricity-natural gas system (HRE-IENGS) based on the N - 1 security guideline. The scheme instruction and system evaluation processes as follows: in order to improve the accuracy of the IENGS system, simulation fitting solutions of PDSSR boundary were presented according to a hybrid algorithm, including dynamic self-adaptive differential evolution algorithm (DSADE), and a least square support vector machine (LSSVM) algorithm. The adopted algorithm approach was applied to rapid approximating of the PDSSR boundary by HP expression in power injection spaces. Simultaneously, different renewable energy permeabilities (DREPs) were set from 5 to 50 %, and active power injections of DREPs under short circuit fault on IEEE 118 bus system simulated by using DIgSLENT/power factory software. As the consequence, this mentioned method effectively described the security stability boundary of HRE-IENGS, and realized the three-dimensional visualization space of DREPs-PDSSR. In addition, the behavior of operation points and PDSSR under DREPs were carefully investigated, and unstable runs were accurately exposed. Through the PDSSR, the state analysis could be conducted rapidly on several parameters, including security and stability assessment with various energy supply capabilities. Meanwhile, these indexes were calculated offline and applied on-line. The results of this study verified the accuracy and effectiveness of the proposed modeling for the considered system, and thus could provide technical support for the stability of the HRE-IENGS system.

Optimizing Connected Target Coverage in Wireless Sensor Networks Using Self-Adaptive Differential Evolution

Wireless Sensor Networks (WSNs) are advanced communication technologies with many real-world applications such as monitoring of personal health, military surveillance, and forest wildfire; and tracking of moving objects. Coverage optimization and network connectivity are the critical design issues for many WSNs. In this study, the connected target coverage optimization in WSNs is addressed and it is solved using self-adaptive differential evolution algorithm (SADE) for the first time in literature. A simulation environment is set up to measure the performance of SADE for solving this problem. Based on the experimental settings employed, the numerical results show that SADE is highly successful for dealing with connected target coverage problem and can produce higher performance in comparison with other widely-used metaheuristic algorithms such as classical DE, ABC, and PSO.

A novel hybrid self-adaptive heuristic algorithm to handle single- and multi-objective optimal power flow problems

The optimal power flow (OPF) is a key tool in the planning and operation of power systems, and aims to optimize the operational costs involved in the production and transport of energy by adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, a successful implementation of an expeditious and reliable optimization algorithm is crucial. To this end, this paper proposes and scrutinizes a novel fuzzy adaptive hybrid configuration oriented to a joint self-adaptive particle swarm optimization (SPSO) and differential evolution algorithms, namely FAHSPSO-DE, to address the multi-objective OPF (MOOPF) problem. For the sake of practicality, the objectives with innate differences such as total fuel cost, active power losses, and the emission are selected. Due to the practical limitations in real power systems, additional restrictions, including valve-point effect, multi-fuel characteristic, and prohibited operating zones, are also taken into account. In order to validate the performance of the proposed approach, ten various benchmark functions are examined, while three IEEE standard systems such as IEEE 30-, 57-, and 118-bus test systems are employed to demonstrate the performance and suitability of the proposed approach in solving the OPF problem expeditiously. Results have been compared with those in the literature and show the effectiveness of our proposal in handling different scales, multi-objective, and non-convex optimization problems.

Theoretical Derivation and Optimization Verification of BER for Indoor SWIPT Environments

Symmetrical antenna array is useful for omni bearing beamforming adjustment with multiple receivers. Beam-forming techniques using evolution algorithms have been studied for multi-user resource allocation in simultaneous wireless information and power transfer (SWIPT) systems. In a high-capacity broadband communication system there are many users with wearable devices. A transmitter provides simultaneous wireless information and power to a particular receiver, and the other receivers harvest energy from the radio frequency while being idle. In addition, the ray bounce tracking method is used to estimate the multi-path channel, and the Fourier method is used to perform the time domain conversion. A simple method for reducing the frequency selective effort of the multiple channels using the feed line length instead of the digital phase shifts is proposed. The feed line length and excitation current of the transmitting antennas are adjusted to maximize the energy harvest efficiency under the bit error rate (BER) constraint. We use the time-domain multipath signal to calculate the BER, which includes the inter symbol interference for the wideband system. In addition, we use multi-objective function for optimization. To the best of our knowledge, resource allocation algorithms for this problem have not been reported in the literature. The optimal radiation patterns are synthesized by the asynchronous particle swarm optimization (APSO) and self-adaptive dynamic differential evolution (SADDE) algorithms. Both APSO and SADDE can form good patterns for the receiver for energy harvesting. However, APSO has a faster convergence speed than SADDE.

Parameters estimation of solar photovoltaic models via a self-adaptive ensemble-based differential evolution

Photovoltaic (PV) system as a vital element in the utilize of solar energy, its optimization, control, and simulation are significant. The performance of the PV system is mainly influenced by its model parameters that are varying and unavailable, thus identifying these model parameters is always desired. However, accurate and robust parameters estimation of PV models brings great challenges to the existing methods, since the complicated characteristics when estimating the parameters. Hence, to efficiently provide accurate parameters for the PV model, this study develops a self-adaptive ensemble-based differential evolution algorithm. Three different mutation strategies with different properties are combined into two groups for updating each individual. Furthermore, in order to make the best of different mutation strategies, a self-adaptive scheme is suggested to equilibrate population diversity and convergence, by adjusting the proportion of the mutation strategies used in the population. To evaluate the performance of SEDE, it is used to obtain the parameters of three PV models and compared with other well-established algorithms. Systematic comparison results indicate that SEDE is capable of estimating the model parameters with higher efficiency.

Improved Kriging model based on weighted self-adaptive differential evolution and application for structural monitoring data

The integrity of structural health monitoring data plays an important role in extracting data features. An improved Kriging model based on weighted self-adaptive differential evolution algorithm (KWSDE) is proposed to repair the spatial missing data. In the KWSDE model, the control parameters of variogram in Kriging are optimized by a novel differential evolution (DE) with both scale factor and crossover rate adaptation. Besides, a weighted least-square method is presented for the optimization process to restrain the variable scale of the fitness function, which makes it more suitable for the lack of monitoring data. Finally, a distributed optical fiber monitoring experiment is designed to verify the reliability and functionality of the proposed model. Compared with the Kriging based on DE model (KDE), the interpolation precision of the KWSDE model can be improved by more than 10%, which has great significance for its engineering application.