Differential Evolution Optimization Algorithm Research Articles

A hybrid whale optimization-differential evolution and genetic algorithm based approach to solve unit commitment scheduling problem: WODEGA

BackgroundUnit Commitment (UC) is a complication in the domain of power systems engineering which is integral to the secure, efficient, and economic daily operation of a power system. UC is an optimization problem that aims at scheduling which generating units will be on at what time to meet electricity demand over a given horizon and that horizon it's typically 24–48hours from now. ObjectiveThis research paper proposes a hybrid approach which is the extension of hGADE algorithm aims at solving mixed-integer optimization problem known as the Unit Commitment scheduling problem. The Whale Optimization Algorithm has been incorporated for the calculation of total operation cost of power system operation. MethodThe technique has been tested on a 6 unit system by taking into consideration various system and unit constraints. The hybridization of differential evolution, genetic algorithm, and whale optimization algorithm has produced a significant improvement in overall results. ResultIt has been found that the average cost of operation is 142814.9603 INR and it gets reduced to 142809.8944 INR after applying optimization (hGADE). The cost is further reduced to 142790.0 INR after the application of the Whale Optimization Algorithm (WOA). ConclusionIn this paper, we present a hybrid approach which involves the blending of Differential Evolution, Genetic Algorithm, and Whale Optimization aim at solving mixed-integer optimization problem known as Unit Commitment scheduling problem and has given promising results.

Multiobjective Optimization Technique for Mitigating Unbalance and Improving Voltage Considering Higher Penetration of Electric Vehicles and Distributed Generation

The increasing penetration of distributed generations (DGs) and electric vehicles (EVs) offers not only several opportunities but also introduces many challenges for the distribution system operators (DSOs) regarding power quality. This article investigates the network performances due to uncoordinated DG and EV distribution. It also considers power quality-related performances such as the neutral current, energy loss, voltage imbalance, and bus voltage as a multiobjective optimization problem. The differential evolution optimization algorithm is employed to solve the multiobjective optimization problem to coordinate EV and DG in a distribution grid. This article proposed a method to coordinate EV and DG distribution. The proposed method allows DSOs to jointly optimize the phase sequence and optimal dispatch of DGs to improve the network's performance. If the network requires further improvement, the EV charging or discharging rate is coordinated for a particular location. The efficacy of the proposed method is tested in an Australian low-voltage distribution grid considering the amount of imbalance due to higher penetration of DG and EV. It is observed that the proposed method reduces voltage unbalance factor by up to 98.24%, neutral current up to 94%, and energy loss by 59.45%, and improve bus voltage by 10.42%.

A potential material for removal of nitrogen compounds in petroleum and petrochemical derivates

Activated carbon from coconut shell (Elaeis guineensis), in both crude and chemically modified forms, was evaluated as an adsorbent for removing nitrogen compounds of samples from synthetic and real fuel. The preliminary adsorption tests showed that 97.95 of indoline was removed by sulfuric acid-treated activated carbon (SAAC). The optimum conditions toward adsorption of nitrogen-containing compounds in crude oil (54 °C, 150 rpm and 1.104 g of adsorbent) was evaluated according to the design of experiments (DOE) technique associated with Differential Evolution optimization algorithm and an effective removal of 30.37% of basic nitrogenous compounds present in crude oil was achieved. For comparative effect, the tests were carried out using the previously optimized condition with the vacuum residue, diesel S-500, and diesel S-10, obtaining the removal of 21.56%, 44.44%, and 62.09%, respectively. Complementing the quantitative analysis, the analysis of mass spectrometry confirmed that coconut shell is a potential material for selective adsorption of nitrogenous compounds.

The prevention and control of tuberculosis: an analysis based on a tuberculosis dynamic model derived from the cases of Americans

BackgroundTuberculosis (TB), a preventable and curable disease, is claimed as the second largest number of fatalities, and there are 9,025 cases reported in the United States in 2018. Many researchers have done a lot of research and achieved remarkable results, but TB is still a severe problem for human beings. The study is a further exploration of the prevention and control of tuberculosis.MethodsIn the paper, we propose a new dynamic model to study the transmission dynamics of TB, and then use global differential evolution and local sequential quadratic programming (DESQP) optimization algorithm to estimate parameters of the model. Finally, we use Latin hypercube sampling (LHS) and partial rank correlation coefficients (PRCC) to analyze the influence of parameters on the basic reproduction number (mathcal R_{0}) and the total infectious (including the diagnosed, undiagnosed and incomplete treatment infectious), respectively.ResultsAccording to the research, the basic reproduction number is computed as 2.3597 from 1984 to 2018, which means TB is also an epidemic in the US. The diagnosed rate is 0.6082, which means the undiagnosed will be diagnosed after 1.6442 years. The diagnosed will recover after an average of 1.9912 years. Moreover, some diagnosed will end the treatment after 1.7550 years for some reason. From the study, it’s shown that 2.40% of the recovered will be reactivated, and 13.88% of the newborn will be vaccinated. However, the immune system will be lost after about 19.6078 years.ConclusionThrough the results of this study, we give some suggestions to help prevent and control the TB epidemic in the United States, such as prolonging the protection period of the vaccine by developing new and more effective vaccines to prevent TB; using the Chemoprophylaxis for incubation patients to prevent their conversion into active TB; raising people’s awareness of the prevention and control of TB and treatment after illness; isolating the infected to reduce the spread of TB. According to the latest report in the announcement that came at the first WHO Global Ministerial Conference on Ending tuberculosis in the Sustainable Development Era, we predict that it is challenging to control TB by 2030.

Determination of an optimal control strategy for vaccine administration in COVID-19 pandemic treatment

Background and objectiveFor decades, mathematical models have been used to predict the behavior of physical and biological systems, as well as to define strategies aiming at the minimization of the effects regarding different types of diseases. In the present days, the development of mathematical models to simulate the dynamic behavior of the novel coronavirus disease (COVID-19) is considered an important theme due to the quantity of infected people worldwide. In this work, the objective is to determine an optimal control strategy for vaccine administration in COVID-19 pandemic treatment considering real data from China. Two optimal control problems (mono- and multi-objective) to determine a strategy for vaccine administration in COVID-19 pandemic treatment are proposed. The first consists of minimizing the quantity of infected individuals during the treatment. The second considers minimizing together the quantity of infected individuals and the prescribed vaccine concentration during the treatment. MethodsAn inverse problem is formulated and solved in order to determine the parameters of the compartmental Susceptible-Infectious-Removed model. The solutions for both optimal control problems proposed are obtained by using Differential Evolution and Multi-objective Optimization Differential Evolution algorithms. ResultsA comparative analysis on the influence related to the inclusion of a control strategy in the population subject to the epidemic is carried out, in terms of the compartmental model and its control parameters. The results regarding the proposed optimal control problems provide information from which an optimal strategy for vaccine administration can be defined. ConclusionsThe solution of the optimal control problem can provide information about the effect of vaccination of a population in the face of an epidemic, as well as essential elements for decision making in the economic and governmental spheres.

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.

Nature-inspired and hybrid optimization algorithms on interval Type-2 fuzzy controller for servo processes: a comparative performance study

In this paper, performance evaluations of six well-known nature-inspired algorithms have been reported containing genetic algorithm, cuckoo search, particle swarm optimization, differential evolution, bee colony, and combined particle swarm optimization and differential evolution (CPSODE) algorithms. Based on these optimization algorithms, input and output scaling factors of an interval Type-2 fuzzy PID controller (IT2-FLC) are chosen for closed-loop servo tracking. Optimal values of the scaling factors are chosen by minimization of the objective function which is defined based on the closed-loop controller performance criteria. A detailed comparative analysis is reported based on the simulation and experimental results. Performance analysis reveals that improved performance, reliability, robustness, and lesser noise sensitivity are reported by IT2-FLC with the optimal values obtained by the hybrid algorithm CPSODE.

Robust Multi-Objective Singular Optimal Control Ofpenicillin Fermentation Process

The determination of optimal feeding profile of fed-batch fermentation requires the solution of a singular optimal control problem. The complexity in obtaining the solution to this singular problem is due to the nonlinear dynamics of the system model, the presence of control variables in linear form and the existence of constraints in both the state and control variables. Traditionally, during the optimization process, uncertainties associated with design variables, control parameters and mathematical model are not considered. In this contribution, a systematic methodology to evaluate uncertainties during the resolution of a singular optimal control problem is proposed. This approach consists of the Multi-objective Optimization Differential Evolution algorithm associated with Effective Mean Concept. The proposed methodology is applied to determine the feed substrate concentration in fed-batch penicillin fermentation process. The robust multi- objective singular optimal control problem consists of maximizing the productivity and minimizing the operation total time.

An effective co-evolutionary algorithm based on artificial bee colony and differential evolution for time series predicting optimization

Non-linear model optimization for predicting time series is a challenge problem. In Intelligent Transportation Systems (ITS) application, the indispensable short-term traffic flow prediction with big data makes the problem worst. To improve the prediction accuracy and ensure real-time performance in the big data environment, we propose a novel co-evolutionary artificial bee colony (ABC) improved by differential evolution (DE) optimization algorithm combined with a traffic flow predicting model trained by extreme learning machine (ELM) neural network. The proposed model can inherit the better generalization performance and the less training time consumption of the standard ELM, and can achieve a more balanced search strategy with the optimized weights and biases to overcome the random initialization deficiency of the typical ELM, and successfully obtain higher prediction accuracy compared with state-of-the-art methods. To verify the efficiency of the proposed model, we apply it to Lozi and Tent chaotic time series simulations and measured traffic flow time series experiments. Simulation and experimental results demonstrate that the proposed model has superior performance and competitive computational efficiency.

A Feature Selection-based Approach for the Identification of Critical Components in Complex Technical Infrastructures: Application to the CERN Large Hadron Collider

Complex Technical Infrastructures (CTIs) are large-scale systems made of tens of thousands of interdependent components organized in complex hierarchical architectures. They evolve in time in a way that at one point their functional logic may be more complex than originally designed, and, therefore, traditional reliability/risk importance measures cannot be used for identifying the critical components on which the protection and recovery efforts should be primarily allocated. We propose an approach for identifying the most critical components based on the large amount of operational data collected from the CTI monitoring systems over long time periods and under different operational settings. The underlying idea is to develop binary classifiers to associate different combinations of measured signals to the CTI operating or failed state. The critical CTI components are those whose condition monitoring signals allow optimally classifying the CTI state. To identify the signals and to build the classifier, we consider a feature selection wrapper approach based on the combined use of Support Vector Machine classifiers and the Binary Differential Evolution algorithm for optimization. The approach is successfully applied to a real dataset collected from the CERN (European Centre for Nuclear Research) Large Hadron Collider, a CTI for experiments of physics.

Fabrication of High-Quality Polymer Composite Frame by a New Method of Fiber Winding Process.

Polymer composite frame has been frequently used in the main structural body of vehicles in aerospace, automotive, etc., applications. Manufacturing of complex curved composite frame suffer from the lack of accurate and optimum method of winding process that lead to preparation of uniform fiber arrangement in critical location of the curved frame. This article deals with the fabrication of high-quality polymer composite frame through an optimal winding of textile fibers onto a non-bearing core frame using a fiber-processing head and an industrial robot. The number of winding layers of fibers and their winding angles are determined based on the operational load on the composite structure. Ensuring the correct winding angles and thus also the homogeneity of fibers in each winding layer can be achieved by using an industrial robot and by definition of its suitable off-line trajectory for the production cycle. Determination of an optimal off-line trajectory of the end-effector of a robot (robot-end-effector (REE)) is important especially in the case of complicated 3D shaped frames. The authors developed their own calculation procedure to determine the optimal REE trajectory in the composite manufacturing process. A mathematical model of the winding process, matrix calculus (particularly matrices of rotations and translations) and an optimization differential evolution algorithm are used during calculation of the optimal REE trajectory. Polymer composites with greater resistance to failure damage (especially against physical destruction) can be produced using the above mentioned procedure. The procedure was successfully tested in an experimental composite laboratory. Two practical examples of optimal trajectory calculation are included in the article. The described optimization algorithm of REE trajectory is completely independent of the industrial robot type and robot software tools used and can also be used in other composite manufacturing technologies.

Exploring various sizes of liquefied gas carriers by an optimisation approach to early-stage project

The present work aims to provide a comprehensive approach to assist within the early-stage project of ship energy systems. This integrated approach enables the optimisation of design, synthesis and operation, considering economic and technical aspects, as well as route weather. A Differential Evolution optimisation algorithm is used to maximise the net present value of the system. Constraints are considered to avoid propellers presenting strength, cavitation and vibration concerns. The case study is designed using three liquefied natural gas carriers of different cargo capacities sailing between Lake Charles and Tokyo Bay, via Panama Canal. The approach is shown to be effective to find optimal solution quickly and highlights the need for employing constraints to avoid future technical concerns. The approach also succeeded to quantify the gain of the net present value using the economy of scale, i.e. using bigger ships, and higher service speeds.

A Solution of Implicit Model of Series-Parallel Photovoltaic Arrays by Using Deterministic and Metaheuristic Global Optimization Algorithms

The implicit model of photovoltaic (PV) arrays in series-parallel (SP) configuration does not require the LambertW function, since it uses the single-diode model, to represent each submodule, and the implicit current-voltage relationship to construct systems of nonlinear equations that describe the electrical behavior of a PV generator. However, the implicit model does not analyze different solution methods to reduce computation time. This paper formulates the solution of the implicit model of SP arrays as an optimization problem with restrictions for all the variables, i.e., submodules voltages, blocking diode voltage, and strings currents. Such an optimization problem is solved by using two deterministic (Trust-Region Dogleg and Levenberg Marquard) and two metaheuristics (Weighted Differential Evolution and Symbiotic Organism Search) optimization algorithms to reproduce the current–voltage (I–V) curves of small, medium, and large generators operating under homogeneous and non-homogeneous conditions. The performance of all optimization algorithms is evaluated with simulations and experiments. Simulation results indicate that both deterministic optimization algorithms correctly reproduce I–V curves in all the cases; nevertheless, the two metaheuristic optimization methods only reproduce the I–V curves for small generators, but not for medium and large generators. Finally, experimental results confirm the simulation results for small arrays and validate the reference model used in the simulations.

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

The application of several swarm intelligence and evolutionary metaheuristic algorithms in data clustering problems has in the past few decades gained wide popularity and acceptance due to their success in solving and finding good quality solutions to a variety of complex real-world optimization problems. Clustering is considered one of the most important data analysis techniques in the domain of data mining. A clustering problem refers to the partitioning of unlabeled data objects into a certain number of clusters based on their attribute values or features, with the objective of maximizing intra-clusters homogeneity and inter-cluster heterogeneity. This paper presents an up-to-date survey of major nature-inspired metaheuristic algorithms that have been employed to solve automatic clustering problems. Further, a comparative study of several modified well-known global metaheuristic algorithms is carried out to solve automatic clustering problems. Also, three hybrid swarm intelligence and evolutionary algorithms, namely, particle swarm differential evolution algorithm, firefly differential evolution algorithm and invasive weed optimization differential evolution algorithm, are proposed to deal with the task of automatic data clustering. In contrast to many of the existing traditional and evolutionary computational clustering techniques, the clustering algorithms presented in this paper do not require any predetermined information or prior-knowledge of the dataset that is to be classified, but rather they are capable of spontaneously identifying the optimal number of partitions of the data points during the course of program execution. Forty-one benchmarked datasets that comprise eleven artificial and thirty real world datasets are collated and utilized to evaluate the performances of the representative nature-inspired clustering algorithms. According to the extensive experimental results, comparisons and statistical significance, the firefly algorithm appeared to be more appropriate for better clustering of both low and high dimensional data objects than were other state-of-the-art algorithms. Further, an experimental study demonstrates the superiority of the three proposed hybrid algorithms over the standard state-of-the-art methods in finding meaningful clustering solutions to the problem at hand.

Optimization of a Supersonic Rocket-Based Combined Cycle Inlet Using Differential Evolution

A differential evolution optimization algorithm is proposed for an airbreathing rocket inlet called the exchange inlet at supersonic flight conditions. A five-parameter fitness function is used, which includes variables representing the ingested air mass flow, total pressure drop through the inlet, and shear layer area. Using a differential weight of 0.85, a population size of 75, and a crossover probability of 0.3, it is shown that the algorithm yields a design with a genome within 10% of the most likely global optimum 93% of the time. Single-point optimization is performed at flight Mach numbers of 1.5, 2.5, and 3.5 to establish a Pareto front of optimal designs. From these Pareto fronts a single optimum is chosen and evaluated over a range of off-design flight Mach numbers from 1.3 to 4.0. In terms of air mass flow and total pressure, the Mach 2.5 optimal design is shown to outperform the other designs between Mach 2.0 and 3.2, while yielding an air mass flow within 12% of the others at all other flight conditions considered.

The relevance of preferential flow in catchment scale simulations: Calibrating a 3D dual‐permeability model using DREAM

AbstractThe occurrence of preferential flow in the subsurface has often been shown in field experiments. However, preferential flow is rarely included in models simulating the hydrological response at the catchment scale. If it is considered, preferential flow parameters are typically determined at the plot scale and then transferred to larger‐scale simulations. Here, we successfully used the optimization algorithm DiffeRential Evolution Adaptive Metropolis (DREAM) to calibrate a 3D physics‐based dual‐permeability model directly at the catchment scale. In order to keep computational costs of the optimization routine at a reasonable level, we limited the number of parameters to be calibrated to the ones that had been shown before to be most influential for the simulation of discharge. We also calibrated parameters of the matrix domain and the macropore domain with a fixed parameter ratio between soil layers instead of calibrating every layer separately. These ratios reflected observed depth profiles of soil hydraulic properties at our study site. The dual‐permeability parameter sets identified during calibration were able to simulate observed discharge time series satisfactorily but did not outperform a calibrated single‐domain reference model scenario. Saturated hydraulic conductivities of the macropore domain were calibrated such that they became very similar to matrix saturated hydraulic conductivities, thereby effectively removing the effect of macropores. This suggests that the incorporation of vertical preferential flow as represented by the dual‐permeability approach was not relevant for reproducing the hydrometric response reasonably well in the studied catchment. We also tested the scale‐invariance of the calibrated dual‐permeability parameter sets by using the parameter sets performing best at catchment scale to simulate plot‐scale bromide depth profiles obtained from tracer irrigation experiments. This parameter transfer proved to be not successful, indicating that soil hydraulic parameters are scale‐variant, independent of the direction of parameter transfer.

Internet of Things Based Occupancy Detection Using Ensemble Classifier for Smart Buildings

Buildings account for a large share in energy consumption in day to day life. Occupancy based models can help in modeling whether the building or the particular room is occupied or not. Occupancy detection mechanisms can help in automating the electrical appliances and make them operational only in presence of the person in the room. This helps in creating energy aware scenarios which can contribute to the energy efficiency and reduction in power tariff. In this work, we take the approach of occupancy modeling by the help of Ensemble Models constructed using Random Forests, Logistic Regression and Support Vector Machine classifiers. The ensemble approach undertaken in this work is Voting and the weights of classifiers to the meta-model are fine-tuned using a Differential Evolution optimization algorithm. The results were found to be of high accuracy, i.e., 98.8% and 98.7% on the given test sets.

The Contribution of Wind Energy Capacity on Generation Systems Adequacy Reliability using Differential Evolution Optimization Algorithm

The Contribution of Wind Energy Capacity on Generation Systems Adequacy Reliability using Differential Evolution Optimization Algorithm

A Differential Evolution-Based Optimized Fuzzy Logic MPPT Method for Enhancing the Maximum Power Extraction of Proton Exchange Membrane Fuel Cells

Recently, fuel cells (FCs) have found vast employment in several applications. However, unique maximum power point tracking (MPPT) exists for each set of operating condition for the efficient operation of FCs. Therefore, this paper presents a differential evolution optimization algorithm (DEOA)-based optimized fuzzy-logic (OFLC) MPPT method for enhancing the maximum power extraction of FCs. The various settings for the membership functions (MFs) of the input and output variables are optimized in the proposed method. Thence, more degree-of-freedom can be employed for accurate and fast tracking of the optimal power point of the proton exchange membrane FCs (PEMFCs). Whereas, existing MPPT methods in the literature for FC applications suffer from decreased degree-of-freedom for optimizing their performance, and lack of adaptivity, which obstructs their suitability for the wide operating range of FCs. The superiority and performance effectiveness of the proposed OFLC MPPT method have been validated and compared with the most prevalent techniques in the literature. Moreover, the robustness and sensitivity of the proposed OFLC MPPT method have been tested at various step changes in the water content of membrane and various temperature changes. Moreover, the proposed design of the suggested OFLC MPPT is general and it can be implemented on low-cost microcontrollers. The results verify the superior performance of the proposed OFLC MPPT method from the accurate and fast MPPT extraction, smooth output power with low ripple, and simplicity of the design point of views.

An IoT- Based Decision Support Tool for Improving the Performance of Smart Grids Connected with Distributed Energy Sources and Electric Vehicles

The growing penetration of distributed energy sources (DES), such as photovoltaic (PV) solar power, battery energy systems and electric vehicles (EVs) into low voltage distribution networks is creating serious challenges for distribution network operators. Uncertain nature of these DES and EV charging is a key factor to cause unbalance, which degrade network performance in terms of energy loss, voltage unbalance, and voltage profile of the distribution network, etc. Some methods were proposed to mitigate such negative impact of these uncertain DES and EV charging from both centralized and decentralized approaches by controlling charging or discharging power of EVs. However, these methods involve all active EVs to participate in coordination and this causes significant inconvenience to EV owners along with requirements of complex communication infrastructure and huge data processing overhead. This article proposes an Internet of Things -based centralized control strategy to coordinate EV and DES distribution by using the differential evolution (DE) optimization algorithm. The obtained results show that the proposed control strategy can improve network performance (voltage imbalance, neutral current, energy loss, and node voltage) significantly. In addition, the control strategy is less demanding on communication infrastructure and convenient for EV owners as well as having a lighter data processing overhead.