Pattern Search Algorithm Research Articles

Artificial neural network (ANN) approach for modeling of lead (II) adsorption from wastewater using a ragi husk powder

The main sources of lead in the environment are effluent industries such as electroplating, alloying, smelting, mining, refining, pigmenting, plastic manufacture, and metallurgical industries. A batch experiment as well as an Artificial Neural Network (ANN) coupled with a Genetic Algorithm model for the extraction of lead from wastewater was conducted. In the development of the ANN model, a tan sigmoid transfer function for input and a purelin for output layers have been employed. A feed-forward back propagation with a single layer was used with thirteen neurons in the hidden layer. The optimized process parameters, viz., pH, adsorbent dosage, and initial concentration of lead, have been obtained. Based on the regression coefficient value of R2 of 0.998, it was confirmed that the ANN model predicted data and the experimental value data were a perfect match. The maximum percentage removal of lead was obtained at optimum conditions by means of a pattern search algorithm in GA.

Swarm intelligence‐based energy management of electric vehicle charging station integrated with renewable energy sources

This research paper proposes a detailed design problem of electrical vehicle (EV) fast-charging stations to maximize the net profit. The charging station is integrated with the renewable energy sources (RES) and battery energy storage system (BESS) to minimize the energy demand from the grid. The performance indices of the design problem, such as the number of chargers and their rating, installed RES power, energy and power of the storage units, and dynamic power contracted by the grid to the EV charging station, is estimated through the proposed algorithm. The detailed modelling of the charging process considers the EV behavioural characteristics like arrival time, state of charge, departure time, and battery capacity and is simulated through sequential Monte-Carlo method. The hybrid Crow Search Algorithm (hCSA), along with the particle swarm optimization (hCSA-PSO), is adopted for the first time to optimize the charging station's installation and operational cost. The effectiveness of the proposed method is compared with a hybrid genetic pattern search algorithm, CSA, and PSO. Several case studies are considered, and it is observed that hCSA-PSO provides the best-optimized results in profit maximization. The cost-benefit analysis is also performed to estimate the financial feasibility of both RES and BESS.

Novel load frequency control scheme for hybrid power systems employing interline power flow controller and redox flow battery

ABSTRACT This article proposes a novel load frequency control (LFC) scheme for hybrid power systems (HPS) in the presence of Interline Power Flow Controller (IPFC) and redox flow battery (RFB). Due to the vagueness nature of solar energy and wind energy sources present in the HPS, the LFC problem is a critical task in HPS. For this study, a fuzzy proportional integral derivative (FPID) controller with hybrid salp swarm algorithm and pattern search algorithm (hSSA-PS) is used to optimize the controller parameter of the HPS system under random and stochastic environments. The usefulness of the proposed hSSA-PS algorithm is established over salp swarm algorithm (SSA), genetic algorithm (GA) and Particle swarm optimization (PSO) algorithms. The performance of the system under study is evaluated with/without the presence of IPFC and RFB. It is observed that system performance is improved with the presence of an IPFC controller and RFB. The robustness of the proposed controller is justified by conducting sensitivity analysis. It is noticed that in presence of the proposed FPID controller the reduction in ITAE/IAE/ISE values is 92.18%, 90.84 and 98.52% as compared to PID controller and 31.42%, 86.90% and 90% as compared to PID controller in the occurrence of IPFC and RFB under stochastic environment. The robustness of the proposed controller is justified by conducting a sensitivity analysis. Also, the outperformance of the suggested controller is contrasted with some advanced controllers reported in some recent papers.

Generalised Pattern Search with Restarting Fitness Landscape Analysis

Fitness landscape analysis for optimisation is a technique that involves analysing black-box optimisation problems to extract pieces of information about the problem, which can beneficially inform the design of the optimiser. Thus, the design of the algorithm aims to address the specific features detected during the analysis of the problem. Similarly, the designer aims to understand the behaviour of the algorithm, even though the problem is unknown and the optimisation is performed via a metaheuristic method. Thus, the algorithmic design made using fitness landscape analysis can be seen as an example of explainable AI in the optimisation domain. The present paper proposes a framework that performs fitness landscape analysis and designs a Pattern Search (PS) algorithm on the basis of the results of the analysis. The algorithm is implemented in a restarting fashion: at each restart, the fitness landscape analysis refines the analysis of the problem and updates the pattern matrix used by PS. A computationally efficient implementation is also presented in this study. Numerical results show that the proposed framework clearly outperforms standard PS and another PS implementation based on fitness landscape analysis. Furthermore, the two instances of the proposed framework considered in this study are competitive with popular algorithms present in the literature.

Optimization Based on Pattern Search Algorithm Applied to pH Non-Linear Control: Application to Alkalinization Process of Sugar Juice

In this paper, an approach for the tuning of a model-based non-linear predictive control (NMPC) is presented. The proposed control uses the pattern search optimization algorithm (PSM), which is applied to the pH non-linear control in the alkalinization process of sugar juice. First, the model identification is made using the Takagi Sugeno T-S fuzzy inference systems with multidimensional fuzzy sets; the next step is the controller parameters tuning. The PSM algorithm is used in both cases. The proposed approach allows the minimization of model uncertainty and decreases, in the response, the error in a steady state when compared with other authors who perform the same procedure but apply other optimization algorithms. The results show an improvement in the steady-state error in the plant response.

Bad data correction in harmonic state estimation for power distribution systems: an approach based on generalised pattern search algorithm

This paper presents a novel methodology for bad data correction in harmonic state estimation for power distribution systems. An optimisation model is formulated considering an objective function to be minimised based on the weighted least squares. Inequality constraints are incorporated to the problem for those buses which are not monitored in real time by any dedicated meter, being their corresponding active and reactive powers considered between upper and lower bounds in order to provide supplementary information about the current system state. In this paper, a measurement calibration vector is introduced into the optimisation model, assuming that a correction factor is associated with the measurements gathered from the network. GPSA (Generalized Pattern Search Algorithm) is used to determine the optimal values of each calibration factor to provide correct state estimation results. A 69-bus test system is used for the computational simulations considering different case studies with multiple bad data to be identified and corrected proving the efficiency and viability of the proposed method. The main contribution of this work is the automatic detection of bad data, identification of the corrupted measurements and correction of the bad data ensuring that the system states are estimated with errors lower than 1%.

Stepwise algorithm and new analytical model for estimating multi-parameter of energy piles from thermal response tests

It is critical to determine the thermal parameters of the soil and pile before designing energy piles. A new parameter estimation method is proposed to interpret the thermal response test (TRT) data of energy piles. A cylindrical heat source model that considers the difference in the thermal properties between the soil and pile is adopted. This analytical model accurately describes the heat transfer process inside and outside the pile at the initial stage. First, a three-dimensional numerical model is established and verified by a large sandbox experiment in the literature. The numerical model is used to simulate a series of virtual TRTs. Second, the sensitivities and correlations of different thermal parameters are investigated. Based on the above analyses, a stepwise parameter estimation method combined with the pattern search algorithm is proposed to estimate the thermal resistance in the pile Rb, effective thermal conductivity ks and diffusivity as of the soil successively. The results show that the proposed method is feasible and effective for using the early-time data to estimate multiple parameters accurately. The relative errors of Rb, ks, and as are equal to 5.2%, 1.2%, and 13.3%, respectively. Finally, the influences of pile diameters and configurations on the accuracy of the estimation results are discussed in detail. Compared with the traditional slope method and parameter estimation methods based on other analytical models, this proposed method can save the time cost of TRTs and obtain more accurate thermal parameters.

Energy management and optimized operation of renewable sources and electric vehicles based on microgrid using hybrid gravitational search and pattern search algorithm

• Considering renewable energy resources. • Reporting superior solutions in the case of cost and execution time • Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management • Presenting Hybrid Gravitational Search and Pattern Search Algorithm The use of plug-in hybrid electric vehicles (PHEVs) can solve many environmental problems and energy crises around the world. Using a large number of PHEVs with high storage and control capabilities can enhance the flexibility of distribution networks. However, optimal management PHEVs with the presence of renewable energy sources (RESs) is one of the main challenges that must be addressed. The optimal management of various RESs along with PHEVs is possible in the form of a microgrid (MG). Moreover, the uncertainties of input parameters are successfully considered in the model development by Monte Carlo simulation (MCS). In the modelling, the uncertainties in PHEVs, load, RESs and energy price are considered and simulated for 24 h. The NiMH-Battery is also used to investigate the role of the storage device. The objective function is minimizing the total cost of the grid-connected MG including the costs of load supply, PHEVs charging demand and power losses. The optimization problem of this paper is solved using the hybrid gravitational search and pattern search (GSA-PS) algorithms. Simulation confirm the efficiency of the GSA-PS technique compared with conventional schemes. The results also show that the generation costs of the GSA-PS are considerably reduced than the classical optimization algorithms.

Coordinated optimal control of active power of wind farms considering wake effect

In a large-scale wind farm, under the influence of the wake effect, the single-machine maximum power extraction control strategy would not be able to function at the ideal optimal value. It is important to study the coordinated operation strategy of the wind farm under the wake effect to improve the output power of wind farms and improve the economic benefit. In this paper, a practical wake model called the PARK model is used and a wake superposition model based on energy balance is derived. Based on these models, an optimization problem is formulated to maximize the output power of the wind farm considering the wake effect. Taking the Horns Rev offshore wind farm as an example, the stochastic points method, particle swarm optimization, and the pattern search algorithm are implemented and compared with the single-machine maximum power extraction algorithm. Test results show that the particle swarm optimization and the pattern search algorithm have better performance. The output power of the wind farm increases by about 10 percent. The particle swarm optimization requires less computation while the pattern search algorithm obtains better and more practical results. Finally, the pattern search algorithm is used to improve economic benefits under different wind conditions.

Enhancement of MPPT controller in PV-BES system using incremental conductance along with hybrid crow-pattern search approach based ANFIS under different environmental conditions

Today, photovoltaic (PV) systems have turned into one of the best options to supply the load demand in developed countries thanks to their numerous benefits. The amount of active power generation in solar arrays effectively depends on their terminal voltage. To enhance the operation of PV systems, it is needed to use the maximum power point tracking methods known as MPPT algorithms. The conventional MPPT methods do not function properly in rapidly changing atmospheric conditions. An MPPT algorithm is developed is in the paper, utilizing the incremental conductance (INC) technique, together with a hybrid crow and pattern search (HCS-PS) algorithm based adaptive neuro-fuzzy inference system (ANFIS). The developed framework would be deployed to specify and track the MPP within a bi-step technique. The optimal voltages would be determined by employing the hybrid HCS-PS, considering various values of temperature and solar irradiance and used as the input–output training information set in the ANFIS in the first stage. The INC technique is applied for the tracking cycle process and begins an accurate tracking method from the mentioned point in the second stage. The power output of the solar PV panel is associated with substantial volatility, indicating the storage requirements. In this regard, a battery energy storage (BES) unit can be utilized along the solar panel. The simulation results confirm that the proposed HCS-PS-ANFIS-INC strategy can successfully decrease the convergence time and increase the output power under different conditions.

A Generalized Pattern Search Algorithm Methodology for solving an Under-Determined System of Equality Constraints to achieve Power System Observability using Synchrophasors

The impact of the generalized pattern search algorithm (GPSA) on power system complete observability utilizing synchrophasors is proposed in this work. This algorithmic technique is an inherent extension of phasor measurement unit (PMU) minimization in a derivative-free framework by evaluating a linear objective function under a set of equality constraints that is smaller than the decision variables in number. A comprehensive study about the utility of such a system of equality constraints under a quadratic objective has been given in our previous paper. The one issue studied in this paper is the impact of a linear cost function to detect optimality in a shorter number of iterations, whereas the cost is minimized. The GPSA evaluates a linear cost function through the iterations needed to satisfy feasibility and optimality criteria. The other issue is how to improve the performance of convergence towards optimality using a gradient-free mathematical algorithm. The GPSA detects an optimal solution in a fewer number of iterations than those spent by a recursive quadratic programming (RQP) algorithm. Numerical studies on standard benchmark power networks show significant improvement in the maximum observability over the existing measurement redundancy generated by the RQP optimization scheme already published in our former paper.

Maximizing the latency fairness in UAV‐assisted MEC system

Unmanned aerial vehicles (UAV) assisted edge computing has risen as an assuring technique to accommodate ubiquitous edge computation for resource-limited devices. Thus, this paper proposes an approach to maximize the latency fairness in a UAV-assisted multi-access edge computing (MEC) system. To maximize latency fairness, the authors focus on minimizing the maximum latency experienced among the users. In here, multiple ground users (GUs) offload their tasks to MEC UAV in the absence or unavailability of ground servers due to a disaster or heavy traffic where an iterative algorithm is proposed to minimize the maximum latency among the users subject to minimum control link rate and total power constraints. Sequentially, the UAVs' 3D location, offloading ratio, GUs' transmit power and GUs' computational capacity are optimized. The location of the UAV is optimized by using the novel approach, guided pattern search algorithm while the altitude of the UAV is optimized by analyzing the elevation angle dependant behaviour of the channel gain. A simple approach is utilized for optimizing the offloading ratio of the users by considering the problem as minimizing the point-wise maximum of two convex functions while the bisection method is used to optimize the power allocation. Numerical simulation results illustrate that the proposed approach outperforms other baseline approaches in convergence, minimizing the maximum latency and maximizing and maintaining the fairness among the GUs. Furthermore, it is proved that the guided pattern search algorithm converges at least 3.5 times better while the proposed combined optimization gives 400% fairness gain, in comparison with the baseline approach.

Estimation of soil and grout thermal properties for energy piles: A new parameter estimation method

It is critical to determine the thermal parameters of the soil and pile for designing energy piles. Based on the infinite different-thermal properties cylindrical heat source model, this paper proposes a method that can use short-term thermal response test (TRT) data to estimate the soil thermal conductivity, thermal diffusivity and thermal resistance in the pile. A three-dimensional numerical model was established and verified by a large sandbox test. The numerical model was used to generate virtual TRT data, and the pattern search algorithm was adopted to estimate the parameters. Then the accuracy of the parameter identification results was discussed. It is found that, compared with the previous methods, the proposed method can greatly shorten the time required for TRT. At the same time, this method can estimate the thermal parameters more accurately, especially the soil thermal conductivity that has the greatest impact on heat transfer performance, with an estimated error of 1.23%.

Whole-time scenario optimization of steam-assisted gravity drainage (SAGD) with temperature, pressure, and rate control using an efficient hybrid optimization technique

Whole-time (simultaneous) scenario optimization of oil recovery over a long time necessitates control of many parameters which is very time-consuming. Usually, to reduce the decision variables, scenarios are optimized in separate intervals. As a new work, a whole-time scenario optimization of the steam-assisted gravity drainage (SAGD) process with several decision parameters (steam injection rate, temperature, and pressure of all intervals) is performed in a 3D reservoir defining an 8-year scenario. Two relatively fast optimization algorithms, i.e. particle swarm optimization (PSO) and pattern search (PS), are utilized to maximize the net present value (NPV). First, through different optimizations, appropriate population size is selected for PSO. Next, the performance of four different scenarios is investigated, and the excellence of whole-time scenario optimization is proved. It is concluded that a higher NPV is obtained by increasing the number of time-intervals. Furthermore, conducting the whole-time optimization with 8 time-intervals can reduce the SAGD process by 1 year. Finally, a new hybrid PSO-PS algorithm is proposed that uses the advantages of both PSO and PS algorithms and reduces the number of function calls. The hybrid algorithm could result in the same results as PSO, while remarkably improved the convergence speed (about 84%).

An efficient field-programmable gate array-based hardware oriented block motion estimation algorithm based on diamond adaptive rood pattern search algorithm for multi-standard video codec

Motion estimation and motion compensation techniques are effectively used for removing temporal redundancy between adjacent frames during video compression, but video quality will be degraded with the traditional pixel-based matching algorithms. To overcome this issue, diamond adaptive rood pattern search algorithm-based block-matching motion estimation algorithms are proposed in this manuscript for multi-stranded codec hardware design, which provide a high compression rate with less computational complexity. In this work, a diamond adaptive rood pattern search algorithm is used, which is a combination of a diamond-search algorithm and adaptive rood pattern search. In addition, the proposed algorithm applies multiple search strategies and terminates unnecessary searching while reserving high data regularity. The proposed diamond adaptive rood pattern search algorithm-based block-matching motion estimation algorithm for multi-stranded codec hardware design provides good performance in terms of complex calculations reduction and reaches a high speed in comparison to traditional search algorithms. The proposed architecture is implemented in Xilinx ISE design 14.5 suite and the implemented results are compared to the existing motion estimation techniques on field-programmable gate array devices. Simulation results shows that the diamond adaptive rood pattern search algorithm for multi-stranded codec hardware design provides a very good improvement in terms of computations overhead reduction, with 96.4%, 97.3% latency and 98.7% throughput and achieves a high compression rate without video quality degradation.

A novel efficient time and frequency waveform design for filter bank multicarrier communication systems by using Hybrid gray wolf optimization algorithm

To realize reliable high data rate transmission in multicarrier communication systems, the orthogonality of prototype filters in time and frequency is required. However, the time and frequency dispersion of the mobile radio channel leads to the loss of orthogonality which produces intersymbol interference (ISI) and interchannel interference (ICI). Therefore, the prototype filter design plays a critical role in the performance of multicarrier systems. In this paper, we address this issue as an optimization problem. To the best of our knowledge, in all previous prototype filter optimization algorithms, a solution is obtained by working on the samples of filter only in time or frequency domains to satisfy both time and frequency domain characteristics. In this study, the proposed algorithm jointly works on the filter samples both in time and frequency domains. To do this, the hybrid of gray wolf optimization (GWO), Levenberg Marquardt (LM) and pattern search (PS) algorithms is introduced. The GWO algorithm used for exploring space, the LM and PS algorithms exploit the solution, locally. While the PS algorithm nondeterministically changes the samples, the LM algorithm moves all samples by considering gradient descent direction. Furthermore, a smoothing approach is used to achieve better characteristics of designed filter by applying a moving average filter. By using the proposed algorithm, prototype filters can be designed with any possible characteristics based on the user preferences, while in other studies, usually filters are designed with some limited special characteristics. Simulation results show that the prototype filter which is obtained by our proposed algorithm, achieves better characteristics in comparison with the other conventional filters.

Automated configuration design framework for payload integration in unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have became appreciably compact and lightweight over the years, imposing stricter geometric and stability constraints. The new-generation UAVs are required to carry numerous sensors and peripheral components for a variety of tasks. These components are placed in accordance with the requirements for use and stability conditions, which complicates the determination of an optimal layout configuration. This article presents the design automation framework for determining favourable sensor placement locations under specific stability constraints. The proposed methodology is a unique end-to-end framework that determines convenient locations for sensor placement, considering the finite element model of UAVs and payload-related parameters as inputs. The design automation routine proposes an integrated approach using the support vector machine and extended pattern search algorithms to identify the design space and relevant cost function. The efficacy of the proposed framework is examined by leveraging finite element solvers to optimize payload placements on a simulated UAV model.

Application of Surrogate Optimization Routine with Clustering Technique for Optimal Design of an Induction Motor

This paper proposes a new surrogate optimization routine for optimal design of a direct on line (DOL) squirrel cage induction motor. The geometry of the motor is optimized to maximize its electromagnetic efficiency while respecting the constraints, such as output power and power factor. The routine uses the methodologies of Latin-hypercube sampling, a clustering technique and a Box–Behnken design for improving the accuracy of the surrogate model while efficiently utilizing the computational resources. The global search-based particle swarm optimization (PSO) algorithm is used for optimizing the surrogate model and the pattern search algorithm is used for fine-tuning the surrogate optimal solution. The proposed surrogate optimization routine achieved an optimal design with an electromagnetic efficiency of 93.90%, for a 7.5 kW motor. To benchmark the performance of the surrogate optimization routine, a comparative analysis was carried out with a direct optimization routine that uses a finite element method (FEM)-based machine model as a cost function.

A novel combinatorial hybrid SFL–PS algorithm based neural network with perturb and observe for the MPPT controller of a hybrid PV-storage system

In recent years, various control methods have been proposed for maximum power point tracking (MPPT) of photovoltaic (PV) power plants. Different MPPT methods for PV systems in the literature have been evaluated in terms of energy efficiency, energy conversion, dynamic performance and reliability in different environmental conditions. Among the various MPPT methods, the Artificial Neural Network (ANN) MPPT is one of the best methods due to its ability in noise rejection and no need for prior information of physical parameters. For implementing the ANN-based MPPT two input variables including temperature and irradiance and an output variable containing voltage of MPP are taken into account. In this paper, a hybrid shuffled frog leaping and pattern search (HSFL–PS) algorithm is used for optimizing ANN-based MPPT in a grid-tied PV system. The P&O approach is used for the tracking cycle procedure and starts a precise tracking scheme after training the ANN and specification of neuron weights. MATLAB/Simulink is utilized for simulation tests to confirm the performance of the offered MPPT method. The outcomes from simulation tests validate the improved performance of the recommended MPPT in comparison with the conventional methods with a fast response of 011 sec.

Sizing renewable energy systems with energy storage systems based microgrids for cost minimization using hybrid shuffled frog-leaping and pattern search algorithm

The future of the power system is tied to the use of distributed generation (DGs) like photovoltaic (PV) and wind turbine (WT) since international societies are concerned regarding environmental pollution. However, the high penetration of distributed energy resources (DERs) in power systems causes a lot of control and operational problems that should be addressed by network operators. Therefore, this article proposes optimal management of a grid-connected microgrid (MG) including various DERs such as PV systems and storage devices. The use of the PV system in this MG is based on proposing new mathematical modelling. In the proposed model, the influence of radiation on different days and seasons on optimal operation is considered. Also, to overcome the random behaviour of the PV system, WT and loads, the uncertainties of the problem are modelled using the scenario method. The optimization problem is solved with the hybrid shuffled frog leaping and pattern search (HSFLA-PS) algorithm. The proposed optimal management is simulated by MATLAB/Simulink considering a practical PV model in a real environmental condition. This used algorithm has some advantages such as fast computational time and accurate results. The results show that the suggested day-ahead scheduling using the HSFLA-PS has an edge over other conventional methods.