Reliable Optimization Algorithm Research Articles

Optimal proportional-integral speed control for closed-loop engine timing system

In internal combustion engines, adjusting the air-fuel ratio is essential to control the speed and minimize the burnt fuel. The throttle opening is the actuator to control the air-fuel. A better design for the used/conventional controller can give a better response without additional cost. In this work, the proposed controller gains of the proportional-integral (PI) controller are tuned to enhance the speed in constant and variable drive cycle modes. The tuning process is conducted based on two of the most efficient performance indices used in this field. The performance indices are integral absolute error (IAE) and integral time absolute error (ITAE). The optimization problem is solved using three reliable stochastic optimization algorithms to ensure mature convergence of the solutions, to avoid local optima solutions, and to ensure effective shrinking of the search space. The optimization algorithms are teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and genetic algorithm (GA). Different simulations are conducted to validate the results. The results are compared with conventional tuning methods regarding the system's time response.

Multi-level optimization strategies for large-scale nonlinear process systems

With growing needs to develop and improve climate-friendly processes, optimization strategies are essential at all levels of decision-making in chemical and energy processes, including process development, process synthesis and design, as well as process operations, control, scheduling, and planning. Challenges include the formulation of well-posed and well-conditioned process models, and development and application of efficient, reliable optimization algorithms. Here we describe a synthesis of optimization concepts and algorithms that enable large-scale nonlinear programming, nonintrusive decomposition strategies and the inclusion of a wide class of surrogate models. All of these are crucial to address challenging nonconvex, multi-scale problems in Computer Aided Process Engineering (CAPE). These elements are demonstrated through dynamic optimization strategies for novel energy generation, demand-based optimization for specialty chemicals, and optimization with integrated heterogeneous models for carbon capture processes.

A high-performance democratic political algorithm for solving multi-objective optimal power flow problem

The optimal power flow (OPF) is one of the most noticeable and integral tools in the power system operation and control and aims to obtain the most economical combination of power plants to exactly serve the total demand of the system without any load shedding or islanding through adjusting control variables to meet operational, economic, and environmental constraints. To achieve this goal, the successful implementation of an expeditious and reliable optimization algorithm is crucial. To solve this issue, this research proposes an enhanced democratic political algorithm (DPA), which can effectively solve multi-objective optimum power flow problems. The proposed method is a version of the democratic political optimization algorithm in which the search capability of this method to cover the borders of the Pareto frontier is enhanced. For the sake of practicality, the objectives with innate differences such as total emission, active power loss, and fuel cost are selected. Due to the practical limitations in real power systems, additional restrictions including valve-point effect, multi-fuel characteristics, and forbidden operational zones, are also considered. The proposed approach is tested and validated on IEEE 57-bus and IEEE 118-bus systems with different case studies. Simulation results are analyzed and compared with two popular and commonly used multi-objective-evolutionary algorithms namely, non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) on the problem. The study results illustrate the effectiveness of the proposed method in handling different scales, non-convex, and multi-objective optimization problems.

Efficient variable cell shape geometry optimization

A fast and reliable geometry optimization algorithm is presented that optimizes atomic positions and lattice vectors simultaneously. Using a series of benchmarks, it is shown that the method presented in this paper outperforms in most cases the standard optimization methods implemented in popular codes such as Quantum ESPRESSO and VASP. To motivate the variable cell shape optimization method presented in here, the eigenvalues of the lattice Hessian matrix are investigated thoroughly. It is shown that they change depending on the shape of the cell and the number of particles inside the cell. For certain cell shapes the resulting condition number of the lattice matrix can grow quadratically with respect to the number of particles. By a coordinate transformation, which can be applied to all variable cell shape optimization methods, the undesirable conditioning of the lattice Hessian matrix is eliminated.

Solving Economic Load Dispatch Problem Using Grey Wolf Optimization Algorithm

Abstract: This article presents a new evolutionary optimization approach named grey wolf optimization (GWO), which is based on the behavior of grey wolves, for the optimal operating strategy of economic load dispatch (ELD). Nonlinear characteristics of generators like ramp rate limits, valve point discontinuities and prohibited operating zones are considered in the problem. GWO method does not require any information about the gradient of the objective function, while searching for an optimum solution. The GWO algorithm concept appears to be a robust and reliable optimization algorithm is applied to the nonlinear ELD problems. The proposed algorithm is implemented and tested on two test systems having 40 Thermal generators. The results confirm the potential and effectiveness of the proposed algorithm compared to various other methods available in the literature. The outcome is very encouraging and proves that the GWO is a very effective optimization technique for solving various ELD problems.

Comparative analysis of different artificial neural networks for predicting and optimizing in vitro seed germination and sterilization of petunia.

The process of optimizing in vitro seed sterilization and germination is a complicated task since this process is influenced by interactions of many factors (e.g., genotype, disinfectants, pH of the media, temperature, light, immersion time). This study investigated the role of various types and concentrations of disinfectants (i.e., NaOCl, Ca(ClO)2, HgCl2, H2O2, NWCN-Fe, MWCNT) as well as immersion time in successful in vitro seed sterilization and germination of petunia. Also, the utility of three artificial neural networks (ANNs) (e.g., multilayer perceptron (MLP), radial basis function (RBF), and generalized regression neural network (GRNN)) as modeling tools were evaluated to analyze the effect of disinfectants and immersion time on in vitro seed sterilization and germination. Moreover, non‑dominated sorting genetic algorithm‑II (NSGA‑II) was employed for optimizing the selected prediction model. The GRNN algorithm displayed superior predictive accuracy in comparison to MLP and RBF models. Also, the results showed that NSGA‑II can be considered as a reliable multi-objective optimization algorithm for finding the optimal level of disinfectants and immersion time to simultaneously minimize contamination rate and maximize germination percentage. Generally, GRNN-NSGA-II as an up-to-date and reliable computational tool can be applied in future plant in vitro culture studies.

Performance comparison of optimization methods on variational quantum algorithms

Variational quantum algorithms (VQAs) offer a promising path toward using near-term quantum hardware for applications in academic and industrial research. These algorithms aim to find approximate solutions to quantum problems by optimizing a parametrized quantum circuit using a classical optimization algorithm. A successful VQA requires fast and reliable classical optimization algorithms. Understanding and optimizing how off-the-shelf optimization methods perform in this context is important for the future of the field. In this work, we study the performance of four commonly used gradient-free optimization methods: SLSQP, COBYLA, CMA-ES, and SPSA, at finding ground-state energies of a range of small chemistry and material science problems. We test a telescoping sampling scheme (where the accuracy of the cost-function estimate provided to the optimizer is increased as the optimization converges) on all methods, demonstrating mixed results across our range of optimizers and problems chosen. We further hyperparameter tune two of the four optimizers (CMA-ES and SPSA) across a large range of models and demonstrate that with appropriate hyperparameter tuning, CMA-ES is competitive with and sometimes outperforms SPSA (which is not observed in the absence of hyperparameter tuning). Finally, we investigate the ability of an optimizer to beat the `sampling noise floor' given by the sampling noise on each cost-function estimate provided to the optimizer. Our results demonstrate the necessity for tailoring and hyperparameter-tuning known optimization techniques for inherently-noisy variational quantum algorithms and that the variational landscape that one finds in a VQA is highly problem- and system-dependent. This provides guidance for future implementations of these algorithms in the experiment.

Multi-Objective Optimization Strategy for Permanent Magnet Synchronous Motor Based on Combined Surrogate Model and Optimization Algorithm

When a permanent magnet synchronous motor (PMSM) is designed according to the traditional motor design theory, the performance of the motor is often challenging to achieve the desired goal, and further optimization of the motor design parameters is usually required. However, the motor is a strongly coupled, non-linear, multivariate complex system, and it is a challenge to optimize the motor by traditional optimization methods. It needs to rely on reliable surrogate models and optimization algorithms to improve the performance of the PMSM, which is one of the problematic aspects of motor optimization. Therefore, this paper proposes a strategy based on a combination of a high-precision combined surrogate model and the optimization method to optimize the stator and rotor structures of interior PMSM (IPMSM). First, the variables were classified into two layers with high and low sensitivity based on the comprehensive parameter sensitivity analysis. Then, Latin hypercube sampling (LHS) is used to obtain sample points for highly sensitive variables, and various methods are employed to construct surrogate models for variables. Each optimization target is based on the acquired sample points, from which the most accurate combined surrogate model is selected and combined with non-dominated ranking genetic algorithm-II (NSGA-II) to find the best. After optimizing the high-sensitivity variables, a new finite element model (FEM) is built, and the Taguchi method is used to optimize the low-sensitivity variables. Finally, finite element analysis (FEA) was adopted to compare the performance of the initial model and the optimized ones of the IPMSM. The results showed that the performance of the optimized motor is improved to prove the effectiveness and reliability of the proposed method.

An enhanced Gradient-based Optimizer for parameter estimation of various solar photovoltaic models

The performance of a PhotoVoltaic (PV) system could be inferred from the features of its current–voltage relationships, but the PV model parameters are uncertain. Because of its multimodal, multivariable, and nonlinear properties, the PV model requires that its parameters be extracted with high accuracy and efficiency. Therefore, this paper proposes an enhanced version of the Gradient-Based Optimizer (GBO) to estimate the uncertain parameters of various PV models. The Criss-Cross (CC) algorithm and Nelder–Mead simplex (NMs) strategy are hybridized with the GBO to improve its performance. The CC algorithm maximizes the effectiveness of the population and avoids local optima trapping. The NMs strategy enhances the individual search capabilities during the local search and produces optimum convergence speed; therefore, the proposed algorithm is called a Criss-Cross-based Nelder–Mead simplex Gradient-Based Optimizer (CCNMGBO). The primary objective of this study is to propose a simple and reliable optimization algorithm called CCNMGBO for the parameter estimation of PV models with five, seven, and nine unknown parameters. Firstly, the performance of CCNMGBO is validated on 10 benchmark numerical optimization problems, and secondly, applied to the parameter estimation of various PV models. The performance of the CCNMGBO is compared to several other state-of-the-art optimization algorithms. The results proved that the proposed algorithm is superior in handling the numerical optimization problem and obtaining the uncertain parameters of various PV models and performs better during different operating conditions. The convergence speed of the proposed CCNMGBO is also better than selected optimization algorithms with highly reliable output solutions. The average objective function value for case 1 is 9.83E−04, case 2 is 2.43E−04, and the average integral absolute error and relative error values are 1.05E−02 and 3.51E−03, respectively, for all case studies. With Friedman’s rank test values of 2.21 for numerical optimization and 1.66 for parameter estimation optimization, the CCNMGBO stood first among all selected algorithms.

A Time and Reliability Optimization Algorithm for Workflow Scheduling in Heterogeneous Distributed Computing System

In the heterogeneous distributed computing systems, efficient task scheduling is necessary for high performance. System reliability and completion time are generally the two important metrics. In this paper, we proposed an algorithm named Merging and Duplication for Makespan and Reliability (MDMR) which combines task merging and task duplication to achieve single-objective constrained optimization of workflow reliability and completion time. The MDMR algorithm is divided into three phases: task merging, entry task duplication and reliability detection with nonentry task duplication. In the task merging phase, two tasks with a sole parent–child relationship are merged into a single task, then this process is iterated until no tasks satisfy the merging condition, thus simplifying the directed acyclic graph. In the entry task duplication phase, the entry task with the lowest reliability is duplicated on the processors, which are available at that moment to increase the reliability while reducing the workflow completion time. In the reliability detection and nonentry task duplication phase, the current workflow scheduling reliability is first calculated, then the result is directly output if the reliability is higher than the target value, otherwise the nonentry task duplication is executed with the aim of increasing the reliability until the value surpasses the target. According to the experimental results, MDMR dominates the compared algorithms and ensures that the entire workflow achieves the target reliability and shortens the workflow makespan as much as possible.

Developing a new heuristic algorithm for efficient reliability optimization

Developing a new heuristic algorithm for efficient reliability optimization

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Guest editorial: Smart communications and networking: architecture, applications, and future challenges

Optimasi Particle Swarm Optimization Pada Peningkatan Prediksi dengan Metode Backpropagation Menggunakan Software RapidMiner

Backpropagation is a method of Artificial Neural Networks that is quite reliable in solving prediction problems (forecasting). However, in its application, this algorithm still has weaknesses such as optimizing the artificial neural network weights to avoid local minimums, the problem of long training times to achieve convergence and the process of determining the right parameters (learning rate and momentum) in the training process. The purpose of this research is to solve this problem by using Particle Swarm Optimization (PSO) which is a simple and reliable optimization algorithm to solve optimization problems. The data source is obtained from the site sumut.bps.go.id. There are 5 network architecture models used in this study, including 2-5-1, 2-7-1, 2-9-1, 2-11-1 and 2-13-1. The results of trials conducted with Rapid Miner software, the best architectural model is the 2-9-1 model with a total RMSE of 0.056 +/- 0.000 in the implementation of Backpropagation, while in the implementation of Backpropagation + particle swarm optimization the amount of RMSE is 0.055 +/- 0.000. The smaller the RMSE (Root Mean Squared Error), the better the model

A hybrid TLNNABC algorithm for reliability optimization and engineering design problems

A hybrid TLNNABC algorithm for reliability optimization and engineering design problems

A variable neighborhood search simheuristic algorithm for reliability optimization of smart grids under uncertainty

AbstractA fundamental part of the self‐healing grid is reducing disturbances and automatically responding to problems. Toward this goal, smart grid operators would be of great use to ensure stability after a potential overload for a planning horizon, as the electrical values are unknown. To evaluate the robustness of the topology reconfiguration after a disturbance, like an overload, reliability analysis through simulation can be employed. However, the simulation approach we propose in this paper, except for the excessive time it consumes, cannot assure the quality of its solutions. To achieve a more robust configuration, we, additionally, propose a single‐stage stochastic program to optimize the grid topology configuration after a potential overload to ensure stability for the next day. We suggest a simheuristic approach based on a Variable Neighborhood Search metaheuristic to solve the above stochastic optimization problem. We evaluate the two approaches for this real‐world problem, together with Creos Luxembourg S.A., the leading grid operator in Luxembourg. We show that our method can quickly suggest countermeasures to operators facing potential overloading incidents, ensuring the smart grid's stability for the next day.

REO: A Reliable and Energy Efficient Optimization Algorithm for Beacon-Enabled 802.15.4–Based Wireless Body Area Networks

Nowadays, due to the advancements in microelectronics and wireless communications, Wireless Body Area Networks (WBANs) have gained significant attention. They have various application fields including telemedicine, military, sports, and entertainment, which require contradictory Quality of Service (QoS) in terms of reliability, delay, energy, and simplicity. Medium Access Control (MAC) protocols play an essential role in finding a solution to these tradeoffs. Many ongoing research activities, primarily based on the low power IEEE 802.15.4 standard, tried to improve the WBAN performance by MAC parameter adjustment but, to the authors' knowledge, none were able to properly satisfy delay and reliability requirements. In this paper, a numerical MAC optimization algorithm is proposed for the Beacon-enabled IEEE 802.15.4-based WBANs, optimizing the delay, reliability, and energy tradeoffs based on the existing traffic and QoS requirements. The simulation results show that in comparison with similar works, the energy efficiency and reliability are improved by at least 14% and 25%, respectively while reducing the delay substantially.

Optimal placement ofFACTSdevices and power‐flow solutions for a power network system integrated with stochastic renewable energy resources using new metaheuristic optimization techniques

Flexible AC transmission systems (FACTS) and optimal power-flow (OPF) solutions play an important role in solving power operation problems. The volatile nature of the power generation profiles from renewable energy sources, solar and wind systems, and determining the optimal locations and sizes of FACTS devices increase the complexity of the OPF problems in modern power network models, such as transmission power loss, power generation operation cost and voltage deviation, as a highly nonlinear-nonconvex optimization problem. Therefore, this article introduces and employs four new independent, reliable and efficient optimization algorithms inspired by nature and biological nature, namely: Slime Mould Algorithm (SMA), Artificial Ecosystem-based Optimization (AEO), Marine Predators Algorithm (MPA) and Jellyfish Search (JS), for solving both multi- and single-OPF objective problems for a power network incorporating FACTS and stochastic renewable energy sources. The proposed new metaheuristic optimization techniques are compared to the common and available alternatives in the literature, Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO) and Grey Wolf Optimizer (GWO), using IEEE 30-bus test system. To consider and address the challenges of the OPF in modern power network models, the proposed optimization techniques tested under different operation cases such as an increasing in the load, with and without FCTAS and renewable energy sources, different renewable energy sources locations on the network. The result showed that the MPA, SMA, JS and AEO algorithms are more effective solvers for the OPF problems cases compared to the PSO, GWO and MFO algorithms. For example, the AEO obtained 0.0844 p.u. in case of minimizing the voltage deviation compared to 0.1155 p.u. for PSO, which means that the AEO algorithm improved the voltage deviation term by 27% compared to the PSO algorithm.

Nonlinear generalized predictive controller based on ensemble of NARX models for industrial gas turbine engine

New design and operation of modern gas turbine engines (GTEs) are becoming more and more complex where several limitations and control modes should be fulfilled at the same time to accomplish a safe and ideal performance for the engine. For this purpose, a constrained multi-input multi-output (MIMO) non-linear model predictive controller (NMPC) based on neural network model is designed to fulfill the control requirements of a Siemens SGT-A65 three-spool aero-derivative gas turbine engine (ADGTE) used for power generation. However, the implementation of NMPC in real time has two challenges: Firstly, the design of an accurate non-linear model, which can run many times faster than real time. Secondly, the usage of a rapid and reliable optimization algorithm to solve the optimization problem in real time. To solve these issues, the constrained MIMO NMPC is created based on the generalized predictive control (GPC) algorithm as a result of its clarity, ease of use, and capacity to deal with problems in one algorithm. In addition, seven ensembles of eight multi-input single-output (MISO) non-linear autoregressive network with exogenous inputs (NARX) models are used as a base model for the GPC controller to predict the future process outputs. Estimation of free and forced responses of the GPC based on the neural network (NN) model of the plant each sampling time without performing instantaneous linearization is proposed in this study, which reduces the NMPC optimization problem to a linear optimization problem at each sampling step. In addition, the Hildreth's quadratic programming algorithm is used to solve the quadratic optimization problem within the NMPC controller, which offers ease of use and reliability in real time applications. To demonstrate the performance of the NNGPC controller developed in this study, we have compared the performance of the neural network generalized predictive control (NNGPC) controller to the existing controller of the SGT-A65 engine. The simulation results show that the NNGPC has demonstrated output responses with less oscillatory behavior and smoother control actions to the sudden variation in the electric load disturbance than those observed in the existing min-max controller. However, the min-max controller has faster response than that of the NNGPC controller.

Enhanced Marine Predators Algorithm for identifying static and dynamic Photovoltaic models parameters

Providing an accurate and precise photovoltaic model is a vital stage prior to the system design, therefore, this paper proposes a novel algorithm, enhanced marine predators algorithm (EMPA), to identify the unknown parameters for different photovoltaic (PV) models including the static PV models (single-diode and double-diode) and dynamic PV model. In the proposed EMPA, the differential evolution operator (DE) is incorporated into the original marine predators algorithm (MPA) to achieve stable, and reliable performance while handling that nonlinear optimization problem of PV modeling. Three different real datasets are used to show the effectiveness of the proposed algorithm. In the first case study, the proposed algorithm is used to identify the unknown parameters of a single-diode and double-diode PV models. The root-mean-square error (RMSE) and standard deviation (STD) values for a single-diode are 7.7301e-04 and 5.9135e-07. Similarly for double diode are 7.4396e-04 and 3.1849e-05, respectively. In addition, the second case study is used to test the proposed model in identifying the unknown parameters of a double-diode PV model. Here, the proposed algorithm is compared with classical MPA in five scenarios at different operating conditions. In this case study, the RMSE and STD of the proposed algorithm are less than that obtained by the MPA algorithm. Moreover, the third case study is utilized to test the ability of the proposed model in identifying the parameters of a dynamic PV model. In this case study, the performance of the proposed algorithm is compared with the one obtained by MAP and heterogeneous comprehensive learning particle swarm optimization (HCLPSO) algorithms in terms of RMSE ± STD. The obtained value of RMSE ± STD by the proposed algorithm is 0.0084505±1.0971e-17, which is too small compared with that obtained by MPA and HCLPSO algorithms (0.0084505±9.6235e-14 and 0.0084505±2.5235e-9). The results show the proposed model’s superiority over the MPA and other recent proposed algorithms in data fitting, convergence rate, stability, and consistency. Therefore, the proposed algorithm can be considered as a fast, feasible, and a reliable optimization algorithm to identify the unknown parameters in static and dynamic PV models. The code of the dynamic PV models is available via this link: https://github.com/DAyousri/Identifying-the-parameters-of-the-integer-and-fractional-order-dynamic-PV-models?_ga=2.104793926.732834951.1616028563-1268395487.1616028563.

Optimal Siting and Sizing of SSSC Using Modified Salp Swarm Algorithm Considering Optimal Reactive Power Dispatch Problem

The Flexible alternating current transmission systems (FACTS) is considered one of the most developed technologies applied for enhancing the performance of system due to their ability of adjusting different parameters in the transmission systems such as the buses voltage, the transmission line impedance, the active and reactive powers flow in transmission lines. The static synchronous series compensator (SSSC) is an effective member of the FACTS which is connected in series with the transmission lines and it consists of a solid-state voltage source inverter coupled with a transformer which aims to control and secure the operation of the power system. The main function of SSSC is inserting a controllable voltage in series with the transmission line to control the active and reactive powers flow in transmission lines. Solving the optimal reactive power dispatch (ORPD) problem is nonlinear, non-convex and it becomes a complex problem with integration of the SSSC. The contributions of article include, 1) an efficient and reliable optimization algorithm is developed to solve the ORPD problem and identify the optimal location and ratings of the SSSC, 2) The proposed algorithm is based on modifying the salp swarm algorithm (SSSA) using Levy Flight Distribution and spiral movement of particles to enhance the searching capabilities of the SSA, 3) an efficient model of SSSC based on power injection approach is used for representation the SSSC in ORPD. The ORPD is solved with and without the SSSC controller to minimize power losses and voltage deviations as well as improve the voltage stability. The proposed algorithm for ORPD is tested on the standards IEEE 30-bus and 57-bus systems. The simulation results demonstrate that MSSA is more effective and superior for solving the ORPD compared with some other reported meta-heuristic techniques. Moreover, the system performance is enhanced considerably with optimal inclusion the SSSC.