Differential Evolution Optimization Technique Research Articles

Retraction Note: Fuzzy-entropic approach on chest X-ray region of interest segmentation-heart position shifting using differential evolution optimization and multi-level segmentation technique with cloud computing

Retraction Note: Fuzzy-entropic approach on chest X-ray region of interest segmentation-heart position shifting using differential evolution optimization and multi-level segmentation technique with cloud computing

Bilevel planning of electric vehicle charging station and battery swapping station considering real-time uncertainty

The placement of electric vehicle charging stations (EVCS) and battery swapping stations (BSS) is crucial to meet demand while minimizing their impact on the power system network. This article addresses the placement and scheduling of EVCS and BSS within a radial distribution network (RDN) overlaid with a road network. Electric Vehicle Routing Problem (EVRP) and Traffic Assignment Problem (TAP) is introduced to reduce EV energy consumption during trips to EVCS and BSS. A Bi-layer optimization approach is used, with the outer layer minimizing energy loss by allocating EVCS and BSS optimally, and the inner layer optimizing scheduling considering associated costs. Uncertainties related to EV is considered on a real-time trip basis and modelled using 2m-Point estimation method. The problem is solved using Differential Evolution (DE) and Prairie Dog Optimization (PDO) techniques. PDO results in a 9.6% reduction in total energy losses and a 22.5% decrease in total associated costs by optimally placing EVCS at 69, 25,114 and BSS at 19, 84,113 of the RDN compared to DE. Inclusion of 2-m PEM uncertainty increases total energy loss by 8.76% but decreases total associated costs by 6.97% compared to real-time uncertainty cases, enhancing the realism of the results.

Estimating Non-Linear Heat Flux in Continuous Billet Casting Process With Limited Sensors

In a continuous billet caster, the heat transfer from the pool of molten steel to the cooling water via the copper mold depends non-linearly upon several casting parameters. A reliable and accurate methodology based on the principle of inverse heat transfer technique has been proposed in this work to obtain the nonlinear heat flux of a continuous billet casting mold. Differential evolution optimization technique has been used, in conjunction with the 2-D and 3-D steady state conduction heat transfer equations to develop the complete algorithm. It has been observed that both the 2-D and 3-D models are able to accurately predict the heat flux for different numbers and locations of temperature sensors. While the 2-D model estimates the 1-D heat flux profile varying along casting direction within a very short time period, the 3-D model offers the advantage of estimating a more accurate 2-D heat flux profile varying along both the mold length and width. The 2-D model has also been used to estimate the heat flux for an industrial continuous casting mold using measured plant data. It is observed that the present methodology accurately estimates the boundary heat flux in the continuous billet caster mold.

Sliding mode controller design for unstable processes with dead time with a case study on continuous stirred tank reactor

Unstable processes are difficult to control because one or more poles lie on the right-hand side of the s-plane. Control becomes further complicated by the presence of dead time in such systems. In this study, a sliding mode control (SMC) design is proposed for the control of unstable processes with dead time. To apply the SMC, a second order plus dead time (SOPDT) model of the unstable process is used, and a proportional-integral-derivative-acceleration type sliding surface is considered. The parameters of continuous and discontinuous control laws are obtained using the differential evolution optimization technique. The optimal control problem is solved by satisfying a new weighted bi-objective function constituting the performance index integral absolute error and control input total variation. The proposed control scheme has been satisfactorily extended to control unstable integrating and higher-order unstable processes with dead time by approximating them into the unstable SOPDT model. The efficacy of the suggested scheme has been evaluated on several benchmark unstable industrial chemical processes, including the continuous stirred tank reactor (CSTR). Further, this scheme has been compared with recently reported work, and the obtained results clearly demonstrate the effectiveness of the suggested controller.

Parametric Analysis and Multi-Objective Optimization for Energy-Efficient and High-Performance HVAC Air Filter Design and Selection

Abstract Heating, ventilation, and air conditioning (HVAC) systems are notorious for their high energy consumption in buildings, particularly in regions with extreme cooling or heating demands. Air filters play a vital role in these systems, affecting both energy efficiency and indoor air quality. However, high-efficiency filters, due to their significant increase in airflow resistance, require excessive energy compared to low-efficiency filters. This poses a challenge in finding the optimal compromise between reducing energy consumption and enhancing indoor air quality. To address this challenge, a meticulous selection process is crucial in achieving a middle ground that satisfies both objectives. Proper sizing and design of air filters are therefore essential for successful HVAC projects. This paper introduces the utilization of optimization techniques as decision-support tools to determine the optimal design parameters of commonly used HVAC air filters under various scenarios. The developed model incorporates multiple objectives and design criteria, including life-cycle cost (LCC), filter size, and efficiency. By leveraging the differential evolution optimization technique, an algorithm is developed to forecast a range of optimal solutions (Pareto front) based on predefined system criteria and boundary conditions. The model is extensively tested and demonstrates exceptional performance in returning optimal solutions, in addition to the capability of narrowing down and converging to a single value. This methodology holds significant potential in assisting investment decisions concerning HVAC air filters, providing valuable insights for optimizing energy efficiency while ensuring satisfactory indoor air quality.

Damping of Frequency and Power System Oscillations with DFIG Wind Turbine and DE Optimization

Wind power is one of the most promising renewable energy resources and could become a solution to contribute to the present energy and global warming crisis of the world. The commonly used doubly fed induction generator (DFIG) wind turbines have a general trend of increasing oscillation damping. Unless properly controlled, the high penetration of wind energy will increase the oscillation and affect the control and dynamic interaction of the interconnected generators. This paper discusses power oscillation damping control in the automatic generation control (AGC) of two-area power systems with DFIG wind turbines and Matlab code/Simulink interfacing optimization methods. The differential evolution (DE) optimization technique is used to obtain the controller gain parameters. In the optimization process, a step load perturbation (SLP) of 1% has been considered in Area 1 only, and the integral of time weighted absolute error (ITAE) cost function is used. Three different test studies have been examined on the same power system model with non-reheat turbine thermal power plants. In the first case, the power system model is simulated without a controller. In Case Study 2, the system is simulated with the presence of DFIG and without a controller. In Case Study 3, the system is simulated with a PID controller and DFIG. Most of the studies available in the literature do not optimize the appropriate wind penetrating speed gain parameters for the system and do not consider the ITAE as an objective function to reduce area control error. In this regard, the main contribution and result of this paper is—with the proposed PID+DFIG optimized DE—the ITAE objective function error value in the case study without a controller being 6.7865, which is reduced to 1.6008 in the case study with PID+DFIG-optimized DE. In addition, with the proposed controller methods, the dynamic system time responses such as rise time, settling time, overshoot, and undershoot are improved for system tie-line power, change in frequency, and system area controller error. Similarly, with the proposed controller, fast system convergence and fast system oscillation damping are achieved. Generally, it is inferred that the incorporation of DFIG wind turbines in both areas has appreciably improved the dynamic performance and system stability under consideration.

Placement of electric vehicle charging station and solar distributed generation in distribution system considering uncertainties

Electric vehicle penetration in the transport section is increasing and replacing the conventional fossil fuel based vehicles. Still, EV has not received success due to some limitations such as cost of the vehicle, battery capacity and availability of charging station. The availability of charging station depends on its geographical location. At the same time, location in the electrical network affects the energy loss and voltage deviation. Therefore, a road network of urban area overlapped with a 33-bus distribution network has been taken as test system for this work. Allocation of EV charging stations and photovoltaic energy resources as renewable distributed generation have been attempted simultaneously using 2-layer optimization. Differential Evolution and Harris Hawks Optimization techniques have been employed to solve the problem and the final results have been validated using eight other established optimization techniques. 2m point estimation method has been used to take care of uncertainties related to electric vehicles and PV. Monte-carlo simulation is also applied to cross verify the performance. The land cost, customer accessibility to charging station have been taken into account to allocate it at proper places. The whole work has been performed based on the 24-hrs dynamically varying electric vehicles flow and PV outputs.

A Comparative Study in Forming Behavior of Different Grades of Steel in Cold Forging Backward Extrusion by Integrating Artificial Neural Network (ANN) with Differential Evolution (DE) Algorithm

The cold forging backward extrusion is employed to produce parts that are characterized by better mechanical strength. However, in this process, punches are often prone to breakages because of the large forces encountered in deforming the steel billets. The service life of the punches is affected majorly by the geometrical attributes, the type of steel undergoing deformation, and hence the present investigation focuses on the applications of natural computing algorithms such as artificial neural network (ANN) and differential evolution (DE) optimization algorithm to study the differential influence on the forming behavior of different grades steel and enhance the punch service life. The AISI steel grades, such as AISI 1010, 1018, and 1045, employed extensively in the production of automotive components, have been compared in terms of forming behavior, such as effective stress, strain, strain rate, and punch force. The multi-layer feed-forward ANN architecture was utilized for process modeling with forming responses of finite element (FE) simulations that are strategically planned through the design of experiments (DoE) approach. Considerable variations were found for the effective stress and punch force amongst the steels, while marginal deviations were observed for effective strain and strain rates. Confirmatory experiments were conducted to validate the results of optimal combinations obtained through the DE optimization technique, and the deviations were observed to be in the acceptable range. The cold forging backward extruded components have also been examined for better mechanical soundness through microstructure and micro-hardness analysis that clearly revealed the mechanical integrity and strength enhancement within the forged components. The proposed study would assist the industries engaged in the production of cold-forged steel components in determining the appropriate values of variables to minimize the forming responses and, thus, help in enhancing the life of the tooling.

Robust and Efficient Identification of Hydraulic Flow Units using Differential Evolution Optimization and Two-Stage Clustering Techniques

Summary One essential process in reservoir characterization is the identification of hydraulic flow units (HFUs). It plays a critical role in determining hydrocarbon reserves and improving reservoir productivity. Flow zone indicator (FZI), determined from core data, is widely used to identify HFUs. One of the challenges in the FZI technique is that the number of HFUs is identified using qualitative methods and subjective estimation. This work proposes robust methods to identify the optimal HFUs using differential evolution (DE) and two-stage clustering. The first method tested in this work enumerates through a large number of HFUs scenarios using 10 clustering algorithms and different input parameters (number of clusters, minimum number of samples, etc.). The scenario with the largest average correlation coefficient is selected as optimum. The second method uses the DE algorithm to maximize the average correlation coefficient and hence obtain the optimal HFUs. The third method consists of two stages. The first stage uses the OPTICS clustering algorithm to determine the number of HFUs, while the second stage generates the desired clusters using the Gaussian mixture algorithm. Both iterative evaluation and DE optimization methods achieved the same clustering results. However, DE optimization resulted in 85% reduction in runtime due to the robust search capability of the DE algorithm which leads to the solution more efficiently. Furthermore, another significant reduction in runtime was achieved using the two-stage clustering method which yielded very close results. The proposed methods in this work provide unique and potential opportunity to improve the use of FZI data analysis to identify HFUs. This work uses the power of clustering and stochastic algorithms to support a critical process in reservoir characterization.

RETRACTED ARTICLE: Fuzzy-entropic approach on chest X-ray region of interest segmentation-heart position shifting using differential evolution optimization and multi-level segmentation technique with cloud computing

RETRACTED ARTICLE: Fuzzy-entropic approach on chest X-ray region of interest segmentation-heart position shifting using differential evolution optimization and multi-level segmentation technique with cloud computing

Structural design optimization of the arc spring and dual-mass flywheel integrated with different optimization methods

Abstract This paper is about the structural design optimization of the torsional arc spring and the dual-mass flywheel (DMF) using three different population-based optimization techniques: Particle swarm optimization (PSO), differential evolution (DE), and genetic algorithm (GA). For this purpose, the equations of the motions of the vehicle powertrain are derived and implemented into the dynamic analysis to minimize the vehicle torsional vibrations. The parameters and initial angles of the arc spring are optimized by considering the objective function that is the sum of the maximum acceleration amplitudes of the crankshaft vibrations. The results demonstrated that the proposed design procedure is able to provide a proper arc spring and DMF inertias to reduce the torsional vibration significantly. Moreover, it is indicated that the DE optimization techniques provide best performances than others. Finally, it is also shown that the natural frequencies can be reduced by the DMF and the optimization results are under the idling critical speed of the engine. The obtained results of this paper are of utmost importance for the arc spring manufacturer about the design process considering both DMF and spring parameters simultaneously to minimize torsional vibration of the vehicle powertrain.

A Review on Differential Evolution Optimization Techniques

A Review on Differential Evolution Optimization Techniques

Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment

Layout optimization is capable of increasing turbine density and reducing wake effects in wind plants. However, such optimized layouts do not guarantee fixed T-2-T distances in any direction and would be disadvantageous if reduction in computational costs due to turbine set-point updates is also a priority. Regular turbine layouts are considered basic because turbine coordinates can be determined intuitively without the application of any optimization algorithms. However, such layouts can be used to intentionally create directions of large T-2-T distances, hence, achieve the gains of standard/non-optimized operations in these directions, while also having close T-2-T distances in other directions from which the gains of optimized operations can be enjoyed. In this study, a regular hexagonal turbine layout is used to deploy turbines within a fixed area dimension, and a turbulence intensity-constrained axial induction-based plant-wide optimization is carried out using particle swarm, artificial bee colony, and differential evolution optimization techniques. Optimized plant power for three close turbine deployments (4D, 5D, and 6D) are compared to a non-optimized 7D deployment using three mean wind inflows. Results suggest that a plant power increase of up to 37% is possible with a 4D deployment, with this increment decreasing as deployment distance increases and as mean wind inflow increases.

Differential evolution optimization of water gap membrane distillation process for water desalination

A theoretical model to predict the performance of the water gap membrane distillation process is developed to study the impacts of the operating parameters on the permeate flux, specific energy consumption, and water production cost. The MD model is integrated with a heuristic differential evolution (DE) optimization technique to optimize the operating variables of the process to maximize the permeate flux and minimize the energy consumption and production cost. The optimized parameters include the feed temperature, coolant temperature, feed flow rate, coolant flow rate, and water gap thickness. The optimization is carried out for both single and multi-objective functions. Results indicated that about 60% increase in the value of the highest permeate flux is obtained from the optimization model compared with the theoretical model of the MD process. Moreover, the lowest values of both energy consumption and production cost are reduced by about 31% and 38%, respectively, when the optimization is applied. For the multi-objective model, optimization results showed significant improvement and achieved an optimized permeate flux 51% higher than the maximum flux obtained from the MD model without optimization, with an optimized value of specific energy consumption as low as 876 kWh/m3.

Design of aperiodic spherical antenna arrays for wideband performance

This paper illustrates a highly nonlinear problem related to the design of aperiodic antenna arrays using spherical geometry for maximum performance. This synthesis of aperiodic spherical antenna arrays considers two design cases: (1) aperiodic arrays with a nonuniform spacing between rings assuming equal element spacings on the same ring (ASANRUE case) and (2) aperiodic arrays with a nonuniform spacing between rings and between antenna elements of the same ring (ASANRNE case). This process is carried out by using the differential evolution (DE) optimization technique with the goal of finding the optimum angular position of each element on the elevation and azimuthal planes to achieve the maximum performance in terms of the side lobe level, directivity, and number of antenna elements used in the array for three design objectives: natural response of the radiation pattern, beam scanning, and the restriction of a minimum distance among elements. The innovative contribution of this paper is the application of the differential evolution algorithm to a nontrivial and highly complex design problem: the aperiodic element distribution over a spherical antenna arrangement. The advantage of this approach with respect to the state of the art on the topic of spherical array design is that a lower side lobe level and maximum directivity for a spherical antenna array can be obtained for aperiodic element distributions with a reduced number of antenna elements and hence a smaller radius, which means less complexity and lower fabrication costs. Simulation results based on CST Microwave Studio are provided to take mutual coupling into account.

Allocation of electric vehicle charging station considering uncertainties

Abstract Electric vehicle (EV) is essential to reduce the emission caused by conventional vehicle. Placement of electric vehicle charging station (EVCS) is crucial to fulfilling the charging demand at different locations maintaining the minimum negative impact on the power system network. In this article, placement of EVCS has been planned in a radial distribution network superimposed with a road network. The weightage for different places have been considered as per charging demand which depends on the supermarket, road junctions, residences etc. The objectives are minimization of the energy loss, voltage deviation of the power system network and minimization of the land cost with maximum weightage to serve maximum EV with minimum establishment cost. The area has been divided into three zones to establish an EVCS at each zone to make it distributed. Uncertainties related to EV have been considered in this work using 2m Point Estimation method (2m PEM). The optimization problem has been solved using Differential Evolution (DE) and Harris Hawks Optimization (HHO) techniques.

An optimal compact time-modulated circular antenna array synthesis using krill herd optimization

In this paper, an optimal far-field radiation pattern synthesis by using time modulation technique is carried out for a compact circular antenna array (CAA) design using a stochastic optimization method called krill herd (KH) optimization. A compact antenna array is achieved by reducing the inter-element spacing between the array elements, which leads to the miniaturization of the size of the array. On the other hand, an improved far-field radiation characteristic is achieved with a low side lobe level (SLL) and narrow first null beam width (FNBW). A low SLL is an essential requirement to reduce the interference with the other systems operating in the same frequency band. A narrow FNBW is necessary to obtain a high directivity. In time modulation technique, ‘time’ has the most significant role. It is considered the fourth-dimensional parameter, which plays the role of an additional degree of freedom to achieve an improved radiation pattern. The time-modulated antenna array (TMAA) radiates at various harmonic frequencies for the periodic switching time sequence. In this paper, the generation of the radiation pattern at the fundamental/central frequency only is considered. The KH algorithm is a flexible and robust search algorithm. It is applied here to find out the optimum sets of switching on-time duration for each array element and to determine the optimum inter-element separation between two array elements of the 20- and 36-element time-modulated circular antenna array (TMCAA). Particle swarm optimization (PSO) and differential evolution (DE) optimization techniques are also individually applied for the same design purpose to prove the superiority of the KH algorithm–based design. Finally, statistical analysis is performed to ensure the significance of the numerical results.

Congestion management using multi-objective hybrid DE-PSO optimization with solar-ess based distributed generation in deregulated power Market

Transmission congestion is one of the major drawbacks of deregulated power system. Due to increase in load demand, generation increases, but capacity of transmission line is limited, and transmission lines carry power more than its rated capacity, as a result congestion occurs. Congestion management in deregulated power system is a challenging and tough task which can be achieved by introducing one or more Distributed Generators (DG) at optimal location. Optimal location and sizing of DGs is very important for maximizing the social welfare function which is profit to both the producers and consumer of electricity. In this paper hourly congestion management is proposed. It manages the network congestion by optimal placement and sizing of DG in ‘24 h’ time frame. Transmission Congestion Rent (TCR) is used to find the optimal location for DG placement whereas hybrid Differential Evolution (DE) and Particle Swarm Optimization (PSO) technique is used for finding optimal size of DG. In this work, Solar PV along with Energy Storage System (ESS) is utilized as Distributed Energy Storage System (DESS). ESS along with DG is used to store surplus energy during off-load period which will be utilized at peak load thereby enhancing the overall efficiency of the system. 24 h solar irradiance and temperature data of Delhi is taken to mathematically model the power generated from Solar PV. Results obtained with hybrid optimization technique are compared with PSO based optimization. It is observed that hybrid optimization technique outperforms the PSO optimization technique in managing resources and congestion problem in the network for the ‘24 h’ time frame. Proposed work has been carried out on IEEE 30 bus test system.

Machinability Analysis and Optimization in Wire EDM of Medical Grade NiTiNOL Memory Alloy.

NiTiNOL (Nickel–Titanium) shape memory alloys (SMAs) are ideal replacements for titanium alloys used in bio-medical applications because of their superior properties like shape memory and super elasticity. The machining of NiTiNOL alloy is challenging, as it is a difficult to cut material. Hence, in the current research the experimental studies on machinability aspects of medical grade NiTiNOL SMA during wire electric discharge machining (WEDM) using zinc coated brass wire as electrode material have been carried out. Pulse time (Ton), pause time (Toff), wire feed (WF), and servo voltage (SV) are chosen as varying input process variables and the effects of their combinational values on output responses such as surface roughness (SR), material removal rate (MRR), and tool wear rate (TWR) are studied through response surface methodology (RSM) based developed models. Modified differential evolution (MDE) optimization technique has been developed and the convergence curve of the same has been compared with the results of differential evolution (DE) technique. Scanning electron microscopy (SEM) and energy dispersive X-ray spectrography (EDS) analysis are carried out to study the surface morphology of the machined alloy. SV is found to be more influential process parameter for achieving better MRR with minimal SR and TWR, followed by Ton, Toff, and WF. The WF has good impact on reduced SR and TWR responses and found to be least significant in maximizing MRR.

Process Optimization and Modeling of Methylene Blue Adsorption Using Zero-Valent Iron Nanoparticles Synthesized from Sweet Lime Pulp

The presence of dyes in waterbodies poses severe problems in human and aquatic creatures, and the development of treatment methods for the removal of these pollutants is of utmost importance. This research study investigates the elimination of methylene blue (MB) from an aqueous solution using zero-valent iron nanoparticles synthesized from sweet lime pulp waste (nZVISLP). The purity, chemical composition, and crystalline size of nZVISLP were investigated using microscopic and spectroscopic studies. A maximum MB removal efficiency of 98.9% was obtained at the following optimal conditions: C0: 10 mg/L, dosage: 1.2 g/L, and temperature: 25 °C. To understand the adsorptive removal characteristics of nZVISLP, the investigational adsorption data were tested with conventional kinetic and isotherm models. Furthermore, a differential evolution optimization (DEO) technique was used to estimate the optimal intrinsic parameters in the isotherm and kinetic models. For the various evaluated isotherms, the correlation coefficient (R2) values for the Freundlich and Sips isotherm models were ~0.98, thus confirming the aptness of these isotherms to represent MB adsorption onto nZVISLP. The robustness of non-linear models was verified by statistical metrics, thus validating the performance of the optimization technique. The results derived from this study affirm the potential of an ecofriendly biogenic nanomaterial, nZVISLP, for MB adsorptive removal.