Modern Optimization Techniques Research Articles

Optimal sizing and siting of single tunned passive filter based on jellyfish algorithm

<p class="MsoNormal" style="margin-top: 10pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; font-size: 14pt;">The presence of electrical harmonics in low voltage networks causes many problems for customers, the most important of which is the decrease in the power quality beyond the permissible limits, which leads to a decrease in voltage for long periods, a decrease in power factor and a large losses in the electrical network due to the deformation of the electric waveform of voltage and current. The aim of this paper is to design an optimal passive filter and its optimal location to suppress the harmonic distortion in the distribution system that results from nonlinear loads. This filter will use single objective functions and multi objective functions that will minimize the proposed filter cost, the real power losses, total harmonic distortion "THD" and individual harmonic distortion "IHD". This will be done under the equality constraint of power balance and some inequality constraints such as filter parameters limits, quality factor limits, voltage limits, harmonic distortion limits. The optimal passive filter parameters and location will be calculated using the modern optimization techniques such jellyfish optimization technique. IEEE 33 bus radial system will be used for demonstrating results obtained by the proposed method.</span></p>

Optimization of industrial Fenton process of phenolic effluent using artificial neural network and design of experiments

This work aimed to optimize the homogeneous Fenton process used to remove phenol in a chemical industry located in Mauá (São Paulo, Brazil). Phenolic effluent exhibits significant variability in its chemical composition, which poses a great challenge in finding the optimal reagents ratio for conducting Fenton treatment. Often, there is a need for repetitions to treat the same batch, leading to increased reagent consumption and treatment time. Thus, to optimize the industrial process, the present work applied modern optimization techniques on real data, such as design of experiments (DoE) and artificial neural network (ANN). ANN was used to perform simulations in the company’s process, thereby predicting the quantities of ferrous sulfate heptahydrate and hydrogen peroxide. The following optimal conditions have been determined by DoE (phenol concentration ≤ 4 ppm): pH from 2.7 to 4 and temperature from 28 to 40 °C. Before the implementation of the optimized parameters, the efficiency for a single treatment was 45%. After, the efficiency was 82% and the company saved, on average, 51.6% of reagent costs. The main contribution of the present work is the development and industrial testing of data science methods that aims to optimize the industrial Fenton process.

Optimal inexactness schedules for tunable oracle-based methods

Several recent works address the impact of inexact oracles in the convergence analysis of modern first-order optimization techniques, e.g. Bregman Proximal Gradient and Prox-Linear methods as well as their accelerated variants, extending their field of applicability. In this paper, we consider situations where the oracle's inexactness can be chosen upon demand, more precision coming at a computational price counterpart. Our main motivations arise from oracles requiring the solving of auxiliary subproblems or the inexact computation of involved quantities, e.g. a mini-batch stochastic gradient as a full-gradient estimate. We propose optimal inexactness schedules according to presumed oracle cost models and patterns of worst-case guarantees, covering among others convergence results of the aforementioned methods under the presence of inexactness. Specifically, we detail how to choose the level of inexactness at each iteration to obtain the best trade-off between convergence and computational investments. Furthermore, we highlight the benefits one can expect by tuning those oracles' accuracy instead of keeping it constant throughout. Finally, we provide extensive numerical experiments that support the practical interest of our approach, both in offline and online settings, applied to the Fast Gradient algorithm.

Spotted Hyena Optimizer enhances the performance of Fractional-Order PD controller for Tri-copter drone

AbstractIn this study, nonlinear control design is presented for trajectory tracking of Tricopter system. A Fractional Order Proportional Derivative (FOPD) controller has been developed. The performance of controlled Tri-copter system can be enhanced by suggesting modern optimization technique to optimally tune the design parameters of FOPD controller. The Spotted Hyena Optimizer (SHO) is proposed as an optimization method for optimal tuning of FOPD's parameters. To verify the performance of controlled Tricopter system based on optimal SHO-based FOPD controller, computer simulation is implemented via MATLAB codes. Moreover, a comparison study between SHO and Particle Swarm Optimization (PSO) has been made in terms of robustness and transient behavior characteristics of FOPD controller.

A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference

AbstractOptimization is a method which is used in every field, such as engineering, space, finance, fashion market, mass communication, travelling, and also in our daily activities. In every field, everyone always wants to minimize or maximize something called the objective function. Traditional and modern optimization techniques or Meta-Heuristic (MH) optimization techniques are used to solve the objective functions. But the traditional optimization techniques fail to solve the complex and real-world optimization problem consisting of non-linear objective functions. So many modern optimization techniques have been proposed exponentially over the last few decades to overcome these challenges. This paper discusses a brief review of the different benchmark test functions (BTFs) related to existing MH optimization algorithms (OA). It discusses the classification of MH algorithms reported in the literature regarding swarm-based, human-based, physics-based, and evolutionary-based methods. Based on the last half-century literature, MH-OAs are tabulated in terms of the proposed year, author, and inspiration agent. Furthermore, this paper presents the MATLAB and python code web-link of MH-OA. After reading this review article, readers will be able to use MH-OA to solve challenges in their field.

Toward Climate Neutrality: A Comprehensive Overview of Sustainable Operations Management, Optimization, and Wastewater Treatment Methods

Various studies have been conducted in the fields of sustainable operations management (SOM), optimization, and wastewater treatment, yielding unsubstantiated recovery. In the context of Europe’s climate neutrality vision, this paper reviews effective decarbonization strategies and proposes sustainable approaches to mitigate carbonization in various sectors such as buildings, energy, industry, and transportation and how these interlink with wastewater management. The study also explores the role of digitalization in decarbonization and reviews policies that can direct governments’ actions towards a climate-neutral society. This paper presents a review of optimization approaches applied in the fields of science and technology, incorporating modern optimization techniques based on various peer-reviewed published research papers. It emphasizes non-conventional energy and distributed power-generating systems along with the deregulated and regulated environment. Additionally, this paper critically reviews the performance and capability of the micellar-enhanced ultrafiltration (MEUF) process in the treatment of dye wastewater. The review presents evidence of the simultaneous removal of co-existing pollutants and explores the feasibility and efficiency of biosurfactants instead of chemical surfactants. Lastly, the paper proposes a novel Firm–Regulator–Consumer-Technology Enablers/Facilitators interaction framework to study operations, decisions and interactive cooperation considering the relationships between the four agents through a comprehensive literature review of SOM. The proposed framework provides support for exploring future research opportunities and holistic sustainability initiatives.

Modern Optimization Techniques Based PID Controller Tuning for the Speed Control of a DC Motor

In this paper, the optimal configuration of the Proportional Integral Derivative (PID) controller for the speed control of a DC motor are determined and compared using five optimization algorithms. The five optimization algorithms are respectively Ant Lion Optimization (ALO), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO), Multi-Verse Optimization (MVO) and Salp Swarm Optimization (SSO). The objective function uses The Integral of Time multiplied by Absolute Error (ITAE) as the performance index. A comparison of all these methods is done using the following step response parameters - steady-state error, settling time, maximum overshoot and rise time. ALO performed best among all the optimization algorithms.

3D wind field profiles from hyperspectral sounders: revisiting optic-flow from a meteorological perspective

In this work, we present an efficient optic flow algorithm for the extraction of vertically resolved 3D atmospheric motion vector (AMV) fields from incomplete hyperspectral image data measures by infrared sounders. The model at the heart of the energy to be minimized is consistent with atmospheric dynamics, incorporating ingredients of thermodynamics, hydrostatic equilibrium and statistical turbulence. Modern optimization techniques are deployed to design a low-complexity solver for the energy minimization problem, which is non-convex, non-differentiable, high-dimensional and subject to physical constraints. In particular, taking advantage of the alternate direction of multipliers methods (ADMM), we show how to split the original high-dimensional problem into a recursion involving a set of standard and tractable optic-flow sub-problems. By comparing with the ground truth provided by the operational numerical simulation of the European Centre for Medium-Range Weather Forecasts (ECMWF), we show that the performance of the proposed method is superior to state-of-the-art optical flow algorithms in the context of real infrared atmospheric sounding interferometer (IASI) observations.

Global analysis approach of stability of deep foundation pit slopes reinforced by underground diaphragm walls and prestressed anchor cables

The stability evaluation of deep foundation pit slopes is crucial, including the solution of the factor of safety and the location of the critical slip surface. Prestressed anchor cables and underground diaphragm walls are commonly used to reinforce pit slopes. By means of the dimensional increase technique and the global analysis approach of slope stability, a nonlinear optimization problem is defined for the stability analysis of pit slopes. In the optimization problem, the ordinates of discrete points on the slip surface and the factor of safety are both the decision variables, the objective function is the factor of security, and the constraints are the equilibrium equations and the convexity of the slip surface. Because the objective function is linear and the constraint functions are polynomials of degree three at most, the optimization problem is a classical optimization one that conventional optimization techniques can solve without recourse to modern optimization techniques such as the AI technique. Examples suggest that the proposed procedure is far more efficient and stable than the optimization models based on the methods of slides. At last, applications are demonstrated to ongoing deep foundation pit slopes situated at Tong Zhou – Beijing sub-center.

Application of different MPPT algorithms for PMSG-based grid-connected wind energy conversion system

Renewable energy sources such as wind energy sources have become the most prominent area of research work over the last two decades. Extraction of maximum power and lowering of THD are still the main issues during the integration of wind energy sources to the grid due to the noncontrollable variability of wind energy sources and the application of power electronic devices as interfacing devices. The main objective of this paper is to utilize modern optimization techniques for extracting maximum power points and implementing a Multilevel inverter to obtain minimal THD. This paper has introduced Improved Grey Wolf Optimization with Levy Flight for tracking maximum power from WECS. This Levy flight concept is applied in combination with the improved hunting process of GWO for providing an efficacy solution and a high rate of convergence through global tracking. Distinct MPPT algorithms such as Perturb and Observe (P&O), Grey Wolf Optimization (GWO), and Levy Flight Grey Wolf optimization (LGWO) are presented, simulated, and analyzed using MATLAB/SIMULINK. These algorithms have been compared based on quantitative analysis to examine the stability, figure out performance, and verify the best algorithm among the proposed algorithms. The consequences of the comparison demonstrated the supercilious characteristic of LGWO in the matter of tracking GMPP, rate of convergence, and the time to catch GMPP. The multilevel inverter is utilized as an interfacing unit between wind energy sources and the grid. This power electronic-based multilevel inverter is the main source of harmonic generation. This harmonic has been reduced to 5.5% (THD) due to the application of a 23-level Cascaded H-bridge multilevel Inverter using MATLAB/SIMULINK software.

A mixed-integer exponential cone programming formulation for feature subset selection in logistic regression

Logistic regression is one of the widely-used classification tools to construct prediction models. For datasets with a large number of features, feature subset selection methods are considered to obtain accurate and interpretable prediction models, in which irrelevant and redundant features are removed. In this paper, we address the problem of feature subset selection in logistic regression using modern optimization techniques. To this end, we formulate this problem as a mixed-integer exponential cone program (MIEXP). To the best of our knowledge, this is the first time both nonlinear and discrete aspects of the underlying problem are fully considered within an exact optimization framework. We derive different versions of the MIEXP model by the means of regularization and goodness of fit measures including Akaike Information Criterion and Bayesian Information Criterion. Finally, we solve our MIEXP models using the solver MOSEK and evaluate the performance of our different versions over a set of toy examples and benchmark datasets. The results show that our approach is quite successful in obtaining accurate and interpretable prediction models compared to other methods from the literature.

Energy management in smart inter-connected micro-grids using Archimedes optimization algorithm

This paper produces a modern meta-heuristic optimization technique based on the buoyancy principle called the Archimedes optimization algorithm (AOA). The proposed algorithm is used here to determine the optimal economic operation of interconnected micro-grids (IMGs). Each micro-grids (MG) includes different types of distributed generation (DG) units such as solar photovoltaic (PV), wind turbine (WT), and micro-turbine (MT) and aims for achieving minimization of total power generation cost as the main objective function taking into consideration the power exchange between the IMGs and utility with special emphasis on technical constraints. To prove the effectiveness of the proposed AOA algorithm, it is compared with another optimization method based on the particle swarm optimization algorithm (PSO). Results obtained with the AOA algorithm show how managing energy transfer between utility and each MG. For various daily loads, it can reduce electricity consumption while lowering the cost of overall electricity generation, minimizing utility bills, and maximizing micro turbine (MT) efficiency.

Finite-time Synchronization of Fractional-order Energy Resources Demand-Supply Hyperchaotic Systems via Fractional-order Prediction-based Feedback Control Strategy with Bio-inspired Multiobjective Optimization

Abstract The concept of fractional-order control (F-oC) is exploited in this paper to synchronize fractional-order dynamical systems. The addressed systems in this paper reflect the real physical phenomena characterized by the complicated relationship between supply and demand for energy resources in the Shanghai area. Thus, we provide the developed fractional energy resource attractor and the simulation results regarding synchronization under the proposed control law of the same fractional energy resource attractor. Note that most of the synchronization methods achieved excellent performance when dealing with complex continuous systems; however, no method addressed the synchronization problem of fractional-order energy resource systems based on the F-oC and modern optimization techniques, to the best of our knowledge. By designing the finite-time control theory, the finite-time full synchronization of two identical fractional-order energy resources demand-supply hyperchaotic systems (F-oERDSHSs) is investigated due to its performance. The advanced prediction-based fractional-order control law (Pb-FoCL) is established for finite-time synchronization of F-oERDSHSs. The design procedure becomes a multiobjective optimization problem of the knowledge base of the developed controller while satisfying the desired performance requirements. The Finite-Time Stability (F-TS) of the control-loop system is proved by using the finite-time Lyapunov stability theory. Furthermore, the Improved Artificial Hummingbird Algorithm (I-AHA) is used to find an optimal knowledge base of Pb-FoCL while achieving the design constraints. Simulation results are provided to verify the efficiency of the proposed control strategy.

Cuckoo Search Algorithm: Review and its Application

Optimization techniques play a major role in real-world problems, As many cases in which decisions are made are based on random research. But choosing the optimization technology is a big challenge for the user. The cuckoo search algorithm is one of the modern optimization techniques that can replace many of the traditional techniques used, The cuckoo bird use a Levi's flight strategy based on the egg laying method to extract a solution to the problem. The presented work will provide a review of the original cuckoo search algorithm with mentioning some types of developed and hybrid cuckoo search algorithm as well as some of their applications.

Design of Bio-Inspired Optimized Integer And Fractional Pid Controller

The PID controllers are being widely used in almost every real-time engineering problem in accordance with the increasing demand for aquick and consistent response of the system. To justify the performance of PID controllers, a mathematical model of an electric motor is considered in this paper. The complete system is simulated on MATLAB/SIMULINK with an integer and fractional-order PID controller and analyzed in terms of control characteristics. A comparative study of the various closed-loop response of the system is also carried out to compare the performance of an integer and fractional-order PID controller. The controllers used in the system are modern optimized, i.e., the particle swarm optimization technique is used to acquire a more optimized system response. The simple concept of application of integer and fractional-order PID controllers is proposed in this research article. A similar approach can be applied to various real-time problems to obtain optimized results. However, other modern optimization techniques are also available and emerging regularly that can produce a stable and optimized response to satisfy the demand for system stability.

Reliability-based optimum design of multiple tuned mass dampers for minimization of the probability of failure of buildings under earthquakes

This work proposes a methodology for reliability-based optimum design of multiple tuned mass dampers (MTMD) for installation in buildings situated in seismic regions. Unlike works that disregard uncertainties and perform deterministic optimizations, the approach proposed in this work aims to minimize the probability of failure of buildings under earthquakes taking the uncertainties into account. For this purpose, a modern optimization technique, the Whale Optimization Algorithm (WOA), is employed. Actual records of seismic accelerograms that occurred in the site of interest are used to characterize the seismic risk, determining the expected value of the peak ground acceleration (PGA) annual exceedance rate. Thus, the seismic risk is estimated by fitting probability distribution functions of the PGA for periods of one and fifty years, to assess, via numerical simulation, the building’s reliability. The proposed methodology is illustrated considering two buildings located in the city of Cúcuta, Colombia, where earthquake data are available. Robust estimators of the system reliability are calculated using a database of seismic records with notably different features. The results confirm that the proposed methodology for optimization of MTMD considerably increases the reliability of both buildings, highlighting the effectiveness of MTMD in minimizing the probability of failure of buildings situated in regions with significant seismic activity.

Proxy Capacitance-Resistance Modeling for Well Production Forecasts in Case of Well Treatments

Summary We disclose a new-age field-scale production forecast model that handles complex treatment of wellbores during their life cycle. Predictive production models have been an object of increased interest and research for a long time due to the need for a fast tool for forecasting production rates or choosing an optimal field development scheme. The existing approaches based on the material balance equation have several limitations and are not very applicable for real objects. Full-scale reservoir modeling is relatively slow and requires large computing resources. In this paper, we propose a proxy model based on advanced capacitance-resistance approach. The model predicts multiphase flow rates based on the available historical data of field production and information about well treatments. In addition, it provides preferable transmissibility trends, the presence of sealed or leaking faults, and the degree of dissipation between injector-producer well pairs. The advanced feature of the model is time-dependent weight coefficients, which have not been studied previously. They help in accounting the shut-in and workover periods and can be found during the optimization procedure simultaneously. Another feature is fast calculations due to a vectorized form of the model and application of modern optimization techniques. All these options allow modeling real oil fields with a large number of wells and a complex system of production control.

Modern Optimal Controllers for Hybrid Active Power Filter to Minimize Harmonic Distortion

Nowadays, AC distributed power networks are facing many challenges in guaranteeing and improving the required level of power quality indices in power networks with increasing nonlinear, time-variable and unbalanced loads. Power networks can benefit from avoiding and minimizing different AC problems, such as frequency fluctuation and Total Harmonic Distortions (THDs), by using power filters, such as Hybrid Active Power Filters (HAPFs). Therefore, attention towards responsible power quality indices, such as Total Harmonic Distortion (THD), Power Factor (P.F) and Harmonic Pollution (HP) has increased. THD and HP are important indices to show the level of power quality at the network. In this paper, modern optimization techniques have been employed to optimize HAPF parameters, and minimize HP, by using a nature-inspired optimization algorithm, namely, Whale Optimization Algorithm (WOA). The WOA algorithm is compared to the most competitive powerful metaheuristic optimization algorithms: Manta Ray Foraging Optimization (MRFO), Artificial Ecosystem-based Optimization (AEO) and Golden Ratio Optimization Method (GROM). In addition, the WOA, and the proposed modern optimization algorithms, are compared to the most competitive metaheuristic optimization algorithm for HAPF from the literature, called L-SHADE. The comparison results show that the WOA algorithm outperformed all other optimization algorithms, in terms of minimizing harmonic pollution, through optimizing parameters of HAPF; therefore, this paper aims to present the WOA as a powerful control model for HAPF.

Design and Development of Hybrid Converter for Marine Applications

Abstract Recently, there has been an increase in the growth and advancement of electric propulsion in marine electrical drives. A maximum amount of energy is utilized by ships for propulsion drives. To be aware of it and develop an optimized structure to improve the effectiveness of the propulsion system with power consumption is necessary. The proposed paper aims to develop a model and perform functional analysis as per the above understanding and requirements. The factors considered include greenhouse gas emissions, CO2 emissions, environmental aspects, and the availability of non-renewable resources, which leads to the introduction of renewable energy as a replacement method of power generation. For this work, two different renewable sources, such as solar and wind energy, were chosen. The combination of these two resources can manipulate the voltage and satisfy the load in a desirable way. For voltage improvement, a high gain converter with a minimal number of active and passive components is selected. This system adopts a storage system to meet the needs in the future. The inverter switches are controlled by the recommended control algorithm, which can balance and provide adequate power towards the drive by a feedback control loop. The speed of propulsion in the drive is adjusted by the induction motor coupled with the propeller. The analytical study of the proposed system is carried out in MATLAB software. The simulation study revealed the effectiveness of this modern optimization technique.

Optimal Power Flow Analysis Based on Hybrid Gradient-Based Optimizer with Moth–Flame Optimization Algorithm Considering Optimal Placement and Sizing of FACTS/Wind Power

Optimal power flow (OPF) is one of the most significant electric power network control and management issues. Adding unreliable and intermittent renewable energy sources to the electrical grid increase and complicates the OPF issue, which calls for using modern optimization techniques to solve this issue. This work presents the optimal location and size of some FACTS devices in a hybrid power system containing stochastic wind and traditional thermal power plants considering OPF. The FACTS devices used are thyristor-controlled series compensator (TCSC), thyristor-controlled phase shifter (TCPS), and static var compensator (SVC). This optimal location and size of FACTS devices was determined by introducing a multi-objective function containing reserve costs for overestimation and penalty costs for underestimating intermittent renewable sources besides active power losses. The uncertainty in the wind power output is predicted using Weibull probability density functions. This multi-objective function is optimized using a hybrid technique, gradient-based optimizer (GBO), and moth–flame optimization algorithm (MFO).