Optimization Algorithm Research Articles

An improved golden jackal optimization algorithm for combined economic emission dispatch problems

In this research paper, a new improved golden jackal optimization (IGJO) algorithm is applied to address the combined economic emission dispatch (CEED) problem, along with various thermal generator constraints such as valve point loading (VPL) effect, generator limits (GL) in power system. The hunting behavior of the golden jackals is mimicked in the golden jackal optimization (GJO) algorithm. The main aim of the CEED problem is to find the best optimal generation scheduling while minimizing both fuel cost and emission besides meeting the different power system constraints. The original GJO algorithm faces challenges when dealing with high-dimensional optimization problems, as it tends to get trapped in local optima. To address this issue the opposition-based learning (OBL) method was adopted in this GJO algorithm to obtain the global optimal solution and ensure enhanced performance in finding the solution for the CEED problems. To assess the competitiveness of the IGJO algorithm, it is used for various CEED test problems available in the literature, and results are contrasted with other recent heuristic optimization algorithms. Simulation results show that the proposed IGJO performs more effectively than the other compared algorithms in terms of solution quality, and robustness.

Innovative Hierarchical Control of Multiple Microgrids: Cheetah Meets PSO

The expansion of microgrids (MGs) comes from the increasing integration of renewable energy resources (RES) and energy storage systems (ESs) into distribution networks. However, effective integration, coordination, and control of Multiple Microgrids (MMGs) during energy transition from microgrid to grid, and vice versa presents a formidable challenge. This arises from the necessity to ensure smooth operation, coordination, and control of MMGs for stability and efficiency during transitions. The dynamic operation of MMGs due to intermittent renewable energy in microgrids and load variations presents an additional challenge for hierarchical control techniques.This paper provides an Artificial Intelligent (AI)-based control technique and introduces an innovative hybrid optimization technique, denoted as HYCHOPSO, derived from the fusion of Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques, and the quantitative findings of extensive benchmark testing, constitute key aspects of novelty. The strategic choice of this novel optimization technique, coupled with a Proportional-Integral (PI) controller, provides key insights.HYCHOPSO is tested within benchmark functions and reaches its optimal score before 50 iterations as compared with optimization algorithms namely CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO which reaches after approximately 200 iterations. HYCHOPSO consistently exhibits the lowest mean values, indicating its robust convergence capabilities.The proposed control technique results in a significant reduction of the microgrid's Current Total Demand Distortion to 1.1%, meeting acceptable limits for a 600V-bus According to IEEE519-2014 standard, even under nonlinear loads. Additionally, precise regulation of voltage and frequency is achieved with0.19%−+ deviations and a frequency overshoot of 1.9%. Furthermore, optimization contributes to seamless energy transition from microgrid to grid, achieving synchronization in less than 5 milliseconds. This enhances the real application's reliability, flexibility, scalability, and robustness. Furthermore, HYCHOPSO is introduced as an AI-based technique to facilitate control parameter design under dynamic conditions, with substantial simulation cases demonstrating control feasibility

Half mirror algorithm: a metaheuristic that hybridizes swarm intelligence and evolution-based system

This paper promotes a new metaheuristic called the half mirror algorithm (HMA). As its name suggests, HMA offers a new kind of mirroring search. HMA is developed by hybridizing swarm intelligence and the evolution system. Swarm intelligence is adopted by constructing several autonomous agents called swarms. On the other hand, the evolution system is adopted using arithmetic crossover based on a particular reference called a mirror. Four mirrors are used in HMA: the best swarm member, a randomly selected swarm member, the central point of the space, and the corresponding swarm member. During the confrontative assessment, HMA is confronted with average and subtraction-based optimization (ASBO), total interaction algorithm (TIA), walrus optimization algorithm (WaOA), coati optimization algorithm (COA), and clouded leopard optimization (CLO). The result shows that HMA is superior to ASBO, TIA, WaOA, COA, and CLO in 20, 19, 19, 20, and 20 out of 23 functions, respectively. Moreover, HMA has found the global optimal of eight functions. It means the superiority of HMA occurs in almost entire functions. In the future, the mirroring search can be combined with the guided and neighborhood search to construct a more powerful metaheuristic.

High-performance Smart Home System based on Optimization Algorithm

Background: With the recent COVID-19 pandemic, people have become increasingly concerned about their physical health. Therefore, the ability to monitor changes in the surrounding environment in real-time and automatically improve the environment has become a current hot topic to improve the overall health level. Objective: This article describes the design of a high-performance intelligent home system that can simultaneously perform monitoring and automatic adjustment functions. Methods: The ESP8266 was used as the core controller, and the DHT11 and G12-04 sensors were used to collect data, such as temperature, humidity, and ambient light intensity. The sampling frequency was increased and the sampled data were processed to improve data accuracy. The sampled data were wirelessly transmitted to a PC or mobile terminal for real-time display. When the sampled data underwent sudden changes, an alert message was sent via the mobile terminal. Based on the real-time changes in ambient light, an improved lighting brightness adjustment algorithm combining bang-bang and single neuron adaptive PID control was used to adjust the lighting brightness. Results: After testing the system designed in this paper and analyzing the errors compared to standard values, the temperature measurement error ranged from 0% to 0.01107%, and the humidity measurement error ranged from 0% to 0.03797%. The improved algorithm was simulated and tested using MATLAB software and compared with traditional PID algorithms and single-neuron adaptive PID algorithms. The improved algorithm did not overshoot during adjustment, and the system reached a steady state much faster than traditional algorithms. Conclusion: The system showed good performance in real-time, stability, and accuracy, fully demonstrating the effectiveness of the devices and algorithms used in the system. This provides ideas for the design and improvement of future smart homes.

Optimal Location of PMUs for Full Observability of Power System using Coronavirus Herd Immunity Optimizer

Phasor measurement units (PMU) are currently considered as an essential step toward the future smart grid due to their capability in increasing the power system's situation awareness. Due to their high costs and limited resources, optimal placement of PMUs (OPP) is an important challenge to compute the minimum number of PMUs and their optimal distribution in the power systems for achieving full monitoring. The coronavirus herd immunity optimizer (CHIO) is a novel optimization algorithm that emulates the flock immunity strategies for the elimination of the coronavirus pandemic. In this research, the CHIO is adapted for the OPP problem for full fault observability. The proposed algorithm is implemented on power systems considering the zero injection bus impacts. A program is created in MATLAB® environment to implement the proposed algorithm. The algorithm is applied to different test systems including; IEEE 9-bus, 14-bus, 30-bus, 118-bus, 300-bus, New England 39-bus and Polish 2383-bus. The proposed CHIO-based OPP is compared to some exact and metaheuristic-based OPP techniques. Compared to these techniques, the promising results have proved the effectiveness and robustness of the proposed CHIO to solve the OPP problem for full fault observability.

Optimized FOPID controller for nuclear research reactor using enhanced planet optimization algorithm

Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of Planet Optimization Algorithm (POA-M), leveraging the strengths of the Arithmetic Optimization Algorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and Particle Swarm Optimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-M-FOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.

Combining optimization and dynamic movement primitives for planning energy optimal forestry crane motions

Forestry cranes are an important tool for safe and efficient timber harvesting with forestry machines. However, their complex manual control often led to inefficiencies and excessive energy usage, due to the many joysticks and buttons that must be used in a precise sequence to perform efficient movements. To address this, the industry is increasingly turning to partial automation, making manual control more intuitive for the operator and, consequently, achieving improvements in energy efficiency. This article introduces a novel approach to energy-optimal motion planning that can be used along with a feedback control system to automate crane motions, taking over portions of the operator’s work. Our method combines dynamic movement primitives (DMPs) and an energy-optimization algorithm. DMPs is a machine learning technique for motion planning based on human demonstrations, while the optimization algorithm exploits the crane’s redundancy to find energy-optimal trajectories. Simulation results show that DMPs can replicate human-like controlled motions with a 25% reduction in energy consumption. However, our energy optimization algorithm shows improvements of over 40%, providing substantial energy savings and a promising pathway towards environmentally friendly partially automated machines.

A Taguchi method-based optimization algorithm for the analysis of the wind driven-self-excited induction generator

This paper investigates the use of a new global optimization algorithm that is based on Taguchi method to determine the performance parameters of self-excited induction generator being driven by variable speed wind. This analysis is based on solving equations obtained from the per-phase equivalent circuit of the induction generator. The equations have two unknowns namely the frequency and the magnetizing reactance. Both unknown are strongly dependent on the wind turbine speed, the capacity of the excitation, the load being connected at the terminals of the stator and eventually the per-phase equivalent circuit parameters. The resulting equations are nonlinear and subsequently to solve them one can employ either gradient-based algorithms or heuristic algorithms. This paper uses a new heuristic algorithm based on the Taguchi method which, in addition to its global research capability, offers superior characteristics in terms of accuracy and ease of implementation. A comparison with recently published optimization methods is carried out to show its performances in terms of accuracy and ease of implementation. The MATLAB software will be used to perform this analysis on a machine of 0.75 kW while some will be validated experimentally to confirm the aforementioned benefits.

Enhanced Sampling in Molecular Dynamics Simulations: How Many MD Snapshots can be Needed to Reproduce the Biological Behavior?

Since its early days in the 19th century, medicinal chemistry has concentrated its efforts on the treatment of diseases, using tools from areas such as chemistry, pharmacology, and molecular biology. The understanding of biological mechanisms and signaling pathways is crucial information for the development of potential agents for the treatment of diseases mainly because they are such complex processes. Given the limitations that the experimental approach presents, computational chemistry is a valuable alternative for the study of these systems and their behavior. Thus, classical molecular dynamics, based on Newton's laws, is considered a technique of great accuracy, when appropriated force fields are used, and provides satisfactory contributions to the scientific community. However, as many configurations are generated in a large MD simulation, methods such as Statistical Inefficiency and Optimal Wavelet Signal Compression Algorithm are great tools that can reduce the number of subsequent QM calculations. Accordingly, this review aims to briefly discuss the importance and relevance of medicinal chemistry allied to computational chemistry as well as to present a case study where, through a molecular dynamics simulation of AMPK protein (50 ns) and explicit solvent (TIP3P model), a minimum number of snapshots necessary to describe the oscillation profile of the protein behavior was proposed. For this purpose, the RMSD calculation, together with the sophisticated OWSCA method was used to propose the minimum number of snapshots.

Optimizing Network Reconfiguration to Reduce Power Loss and Improve the Voltage Profile in the Distribution System: A practical Case Study

The electricity provider incurs a high costs and investments to transfer the electrical power to customers in the distribution network. Hence, the reliability of the distribution network (DN) is particularly important in the lives of customers. A distribution system faces many issues due to its direct impact of the voltage profile (VP) and power losses (PL) problems. The aim of this study is to provide an effective scheme to address the of VP and PL problems. In this research, a Selective Particle Swarm Optimization (SPSO) algorithm was used to find combination of switches to enhance VP and reduce PL. The proposed method has been tested in the distribution network system, 125-buses Sigeri-Makassar Indonesia. There are three kinds of considered load scenarios, namely: nominal load; active power load increase; active and reactive power load increase. The research results indicated that the VP of the system can increased from 0.918 pu to be 0.922 pu and decreased PL from 0.6482 MW to be 0.6052 MW; the VP can increased from 0.895 pu to be 0.901 pu and reduced PL from 1.0469 MW to be 0.97532 MW, the VP can increased from 0.895 pu to be 0.900 pu and reduced PL from 1.1929 MW to be 1.0758 MW, for scenarios 1, 2 and 3, respectively. The simulation results showed that the proposed method provides optimal solution, which can enhance the VP and reduce PL. This is indicated that the proposed method is an effective strategy for enhancing voltage stability and solve PL problem.

Controlling parameters proportional integral derivative of DC motor using a gradient-based optimizer

In this paper, a gradient-based optimizer (GBO) algorithm is presented to optimize the parameters of a proportional integral derivative (PID) controller in DC motor control. The GBO algorithm which mathematically models and mimics is inspired by the gradient-based Newton method. It was developed to address various optimization issues. To determine the performance of the proposed method, a comparison method with the ant colony optimization (ACO) method. It was compared using the integral of time multiplied absolute error (ITAE). They are most popularly used in the literature. From the test results, the proposed method is promising and has better effectiveness. The proposed method, namely GBO-PID, shows the best performance.

Multi-source data driven harmonic spectrum estimation of substation feeder current

With the proliferation of non-linear loads and distributed power resources that connect through power electronic equipment, decentralized harmonic pollution in three-phase symmetric power system becomes increasingly serious. Despite this, monitoring the current harmonic of all substation feeders regardless of cost is unrealistic. To this end, a multi-source data driven method is proposed in this paper, utilizing data from the power quality monitoring system, dispatching system, and market business system, to estimate the harmonic spectrum of substation feeder currents and estimate the harmonic current components of each feeder with monitoring data of bus voltage harmonics and line harmonic currents. The objective function of the harmonic spectrum estimation model is constructed based on the analysis of current harmonic phasor and power relationship on the feeders. An improved particle swarm optimization (PSO) algorithm is proposed to solve the proposed estimation model thereby obtaining the current harmonic content of each feeder and the phase angle difference between the current harmonics of the feeders. The performance of the proposed model and algorithm is validated by numerical simulation with a test system and an actual system.

Fatigue loads compressed editing by discrete wavelet transform and optimal wavelet parameters selection algorithm

Discrete wavelet transform (DWT) can be used to edit the vehicle road load signals for accelerating durability analyses. Nevertheless, it should be noted that one of inherent deficiencies of DWT is the obvious energy leakage. This paper proposes an improved compressed editing method for vehicle road load signals, which is to achieve more accurate results, based on the energy leakage of DWT to select the optimal wavelet parameters. A wavelet parameters optimal method is proposed by investigating the severity of parameters on energy leakage. Daubechies wavelet functions with different order are performed on signals under the same decomposition level. Then, the intensity of energy leakage in wavelet components in each level is calculated and compared with adjacent levels. Thus, the optimal wavelet parameters will be extracted taking into account the time–frequency resolutions. The effectiveness of this method has been validated by numerical signal, and the optimal method is applied to a series of load signal to achieve compressed compilation.

Optimal coordination of directional over current relays for distribution systems using hybrid GWO-CSA

Coordination of protective relays is a critical aspect of electrical distribution systems, ensuring effective and reliable protection against faults. In modern power systems, the integration of distributed generation (DG) sources adds complexity to the coordination task. The dynamic nature of DG systems requires adaptive relay settings that can swiftly detect and isolate faults while minimizing potential damage and downtime. The purpose of this research is to improve the coordination of directional over current relays in electrical distribution systems, particularly in DG systems. An optimization technique combining the grey wolf optimization (GWO) and cuckoo search algorithm (CSA) is developed to identify the best relay settings that reduce overall operation time while ensuring excellent fault identification and isolation. To address relay faults caused by DG integration, a suitable primary and backup relay design is chosen, and the influence of time multiplier settings (TMS) on system performance and reliability is investigated. The proposed GWO-CSA technique is evaluated and implemented on IEEE 3, 8 and 15-bus systems using MATLAB. Simulation results show that the GWO-CSA strategy outperforms well compared to previous algorithms, enabling optimal coordination and increased protection in DG systems while drastically lowering relay operating time.

A Coronavirus Optimization (CVO) Algorithm to Harvest Maximum Power from PV Systems under Partial and Complex Partial Shading Conditions

Power generation from solar photovoltaic (PV) systems is employed due to its recyclable nature, limitless source of energy, low maintenance, ease of installment, and eco-friendly potential. There is a major challenge in the PV system to harvest the maximal power in varying weather scenarios which decreases the overall efficiency and causes significant power loss. Maximum power point tracking (MPPT) methods are exploited to crop the maximum power from PV strings. Conventional algorithms do not find the Global Maxima (GM) and are trapped on any Local Minima (LM). However, bio-inspired methods find the GM but take more time to search the GM. This paper presents a Corona-Virus Optimization (CVO) algorithm to harvest maximum power against seven well-known conventional and bio-inspired MPPT algorithms. The proposed technique is estimated for Uniform Irradiance (UI), Partial Shading (PS), and Complex Partial Shading (CPS) conditions. Furthermore, the comparative analysis between seven already implemented and proposed techniques shows that the CVO algorithm lessens the unwanted oscillation and quicker searching the GM. Moreover, their numerical analysis is also conducted to evaluate the stability, robustness, performance, and sensitivity of the CVO algorithm. The proposed technique gives the highest efficiencies of 99.99%, 99.98%, 99.97%, and 99.98% in all cases of UI, PS-1, PS-2, and CPS respectively when comparing with P&O, InC, DFO, CS, FFO, PSO, and ACO techniques. Real-time data from the Beijing database is also used to verify the performance of the proposed algorithm.

Hyperspectral remote sensing of forage stoichiometric ratios in the senescent stage of alpine grasslands

Senescence is an important phenological stage in the life cycle of grassland plants, and estimating grass senescence in alpine grasslands is particularly crucial for animal husbandry and grazing the utilization of grassland resources for grazing. However, the application of proximal sensing technologies to capture the variations in forage growth parameters in the senescent stage, especially regarding some key stoichiometric ratios, still faces challenges associated with indiscernible spectral properties and indeterminate absorption and reflectance features. The present study aims to demonstrate the potential and effectiveness of applying hyperspectral data to assess the forage carbon-nitrogen (C:N) ratio and calcium-phosphorus (Ca:P) ratio during the senescent stage (September to November). A progressive variable dimensionality reduction strategy based on a genetic algorithm (GA) and hybrid optimization method (ant optimization colony (ACO) and extreme learning machine (ELM)) coupled with multiple linear and nonlinear statistical methods is applied to develop C:N ratio and Ca:P ratio estimation models via combination with data from 205 sample sites collected from six field campaigns (2016-2019) on the eastern Tibetan Plateau. The results show that the important spectral bands favorable for forage C:N ratio and Ca:P ratio retrieval are generally in the red and shortwave infrared (SWIR) regions, and the proposed model presents satisfactory performance in the estimation of the forage C:N ratio (V-R2 = 0.81, V-RMSE = 6.44) and Ca:P ratio (V-R2 = 0.64, V-RMSE = 2.54) during senescence. Moreover, the model can effectively overcome the spatial differences among sampling areas and perform optimally in capturing the variations in the forage C:N ratio and Ca:P ratio in the early and middle stages of senescence. Overall, our study demonstrates that it is feasible and promising to estimate these stoichiometric ratios using hyperspectral feature bands during grass senescence at the canopy level, with potential applications that may further enhance the monitoring of forage quality and quantity.

Multilevel routing for data transmission in internet of things

Internet of things (IoT) is the network in which sensor nodes sense information and transmit sensed information to sink. The small size and far deployment of SN results in causing problems related to higher energy utilization. Many techniques have been proposed in the last years to improve lifetime of the network. The already developed methods are the clustering techniques which are path optimization algorithms. The virtual grid-based dynamic routes adjustment (VGDRA) is the protocol which is already been proposed to increase network’s duration. The VGDRA protocol improve life span but doesn't solve the issue of energy hole which affect network performance. This work aims to improvise the VGDRA algorithm to solve the power hole problem. The utilization of cache motes is done in the network and the sink will move energy to cache nodes for the data collection. MATLAB is executed to simulate the suggested model, and amount of dead motes, active ones and amount of packets, whose transmission is done to base station (BS).

Deciphering inter-catchment groundwater flow: A water balance perspective in the Choshui River Basin, Taiwan

Study regionFive catchments in upstream Choshui River Basin, Taiwan. Study focusInter-catchment groundwater flow (IGF)is commonly assumed negligible role in a catchment water balance due to its obscure nature. Many studies required detailed on-site geological information to analyze and confirm the significance of IGF. In contrast to these studies, this study investigated IGF using the rainfall-runoff model combined with the deterministic optimization algorithm and the stochastic generalized likelihood uncertainty estimation method, relying on simple and easily accessible water balance data. New hydrological insights for the regionThe results showed that the hydrological environment was non-stationary, and IGF inclusion in the model improved its simulation performance. Though IGF direction varied among the catchments, at least three were found with significant IGF potential, with one of the catchments accounting for 30 % (505 mm) of the yearly precipitation. Ignoring IGF may lead to larger errors, especially in smaller catchments and during the dry season. Overall, this study highlights the importance of considering IGF in a catchment water balance and provides insights into the hydrological cycle.

An enhanced fuzzy IDOCRIW-COCOSO multi-attribute decision making algorithm for decisive electric vehicle battery recycling method

An adaptation to electric mobility quickens waste management tasks for recyclers to end-to-end processing of marketed electric vehicle batteries. Especially lithium-ion batteries play a prominent role in electrifying the world for e-transport technology innovation. This research offers a multi-attribute decision-making (MADM) structure for finding the best performance e-vehicle recycling techniques. The structured algorithm combines an advanced stratified MADM strategy with e-transportation recycling techniques. The optimal algorithm evaluates the results of qualitative attributes and alternatives using a weighted-ranking MADM approach. The importance of attributes is calculated using a blending of dual objective-weighted approaches: entropy and CILOS methods, viz., the aggregated IDOCRIW approach. The ranking of alternatives is determined through the COCOSO method in a hesitation environment. The q-rung orthopair picture fuzzy set (q-ROPFS) is used to cope with uncertainty and vagueness in decision analysis. The feasibility and robustness of the suggested algorithm were validated through different MADM methods and by altering crucial ranking-dependent parameters in the problem.

A hybrid deep learning optimization for predicting the spread of a new emerging infectious disease

<span lang="EN-US">In this study, a novel approach geared toward predicting the estimated number of coronavirus disease (COVID-19) cases was developed. Combining long short-term memory (LSTM) neural networks with particle swarm optimization (PSO) along with grey wolf optimization (GWO) employ hybrid optimization algorithm techniques. This investigation utilizes COVID-19 original data from the Ministry of Health of Indonesia, period 2020-2021. The developed LSTM-PSO-GWO hybrid optimization algorithm can improve the performance and accuracy of predicting the spread of the COVID-19 virus in Indonesia. In initiating LSTM initial weights with weaknesses, using the hybrid optimization algorithm helps overcome these problems and improve model performance. The results of this study suggest that the LSTM-PSO-GWO model can be utilized as an effective and reliable predictive tool to gauge the COVID-19 virus’s spread in Indonesia. </span>