Optimization Algorithm Research Articles

Comparative computational analysis of orthoconic antiferroelectric liquid crystals: DFT analysis.

The study undertakes a comparative analysis of four distinct semi-fluorinated chiral Organic Active Ferroelectric Liquid Crystals (OAFLCs). The comparative analysis of the compounds is done by using various parameters, including thermodynamic, non-linear optical, electrical, atomic charge distribution, and atomic orientations. We use optimization algorithms to look at chemical reactivity, electrical properties, intermolecular interactions, and static hyperpolarizability. Sample 4 is the best choice for a wide range of display applications. This research contributes to understanding the nuanced properties of semi-fluorinated chiral OAFLCs and highlights Sample 4's potential for novel applications in display technology, owing to its superior stability and optimized properties. This study helps to enhance our understanding of the comparative analysis of semi-fluorinated chiral OAFLCs for potential advancements in display technologies by incorporating findings from key studies. The simulations are performed using density functional theory (DFT) with the B3LYP functional for predicting molecular properties, and Vibrational Energy Distribution Analysis (VEDA) software is used to perform the vibrational analysis of the molecules.

Configuration and optimisation of a green closed-loop supply chain with delivery time and green investment considering government subsidy under meta-heuristics algorithms

Consumers' environment consciousness, together with the fierce rivalries among companies, has attracted increasing attention from scholars and industries to the configuration and optimisation of green closed-loop supply chain. However, current studies of closed-loop supply chains rarely consider both the uncertainty from the market and the quality from the recycled used products. Therefore, this article constructs a green closed-loop supply chain total profit model that considers government subsidies, delivery times, green investment and the above uncertainties. Fuzzy chance constraint method is used to solve the uncertain demand; genetic and particle swarm optimisation algorithms are adopted to calculate the model. This study finds (1) enterprises can formulate flexible and adaptable strategies based on different demands; (2) their delivery time decisions rely on both consumer demand and their logistics development levels; (3) enterprises should adjust their delivery times and green investment decisions according to consumers' different sensitivities; (4) enterprises can increase profits by recycling products within a certain range of greenness; (5) for improving recycling and remanufacturing rates, the government can provide secondary subsidies to enterprises for product recycling within the green range of 0.35 ∼ 0.40. This study contributes to the optimisation of strategies developed by enterprises under conditions of subsidies and multiple uncertainties.

Blockchain abetted energy efficient archerfish hunting and Namib Beetle optimization algorithm espoused clustering protocol for wireless sensor network

Blockchain abetted energy efficient archerfish hunting and Namib Beetle optimization algorithm espoused clustering protocol for wireless sensor network

Enhancing arabic handwriting recognition through optimized deep learning frameworks

Detecting Arabic handwriting is challenging due to letter shapes, intervening segments, and diacritical marks, despite recent advances in pattern recognition. Deep learning architectures ConvNeXt and NFNet-F5 and the meta-heuristic optimization algorithm Aquila Optimizer, inspired by eagle hunting, are used to overcome these challenges. We first review Arabic handwriting recognition literature to determine strengths, weaknesses, and future directions. Next, we describe Arabic handwriting, particularly its interconnectivity, diversity, and many diacritical symbols that make recognition difficult. The Aquila Optimizer is used to optimize CNN hyperparameters in this paper. The goal is to increase the recognition rate and reduce the computational workload. To prove the Aquila Optimizer's efficacy, the ConvNeXt and NFNet-F5 topologies are compared with and without it. The optimized ConvNeXt model has a recognition rate of 98.96%, significantly higher than comparable techniques. It also thoroughly assesses numerous meta-heuristic optimizers and highlights the Aquila Optimizer's potential to improve model performance. This work has enhanced Arabic handwriting recognition by building more precise and efficient models and provides a framework for future research on optimization and applying these models to other scripts like Persian and Urdu.

Ultra high-speed 3D shape measurement technology for specular surfaces based on μPMD

Phase measuring deflectometry (PMD) has been extensively applied to measure specular surfaces due to its non-contact, high-precision, full-field measurement capabilities. Liquid crystal display (LCD) screen is the most common structured light source in PMD. However, the response time of liquid crystal molecules limits its frame rate to around 100 frames per second (fps). Therefore, it is quite difficult for traditional PMD to measure rapidly moving surfaces. This paper proposes a 3D dynamic sensing technique, microsecond-PMD (µPMD) based on the high-frame-rate sinusoidal fringe display (HSFD). In the proposed method, the switching time for each fringe pattern display is at a sub-microsecond level, enabling high-speed fringe acquisition with kHz-level area array detection or 100kHz-level line array scanning. The HSFD method uses a specially designed LED array and two-step optical expansion. The high-speed switching characteristic of LED sources is utilized to allow a superfast display rate. Moreover, the superior sinusoidal property can be achieved by the combination of the specially designed discrete sinusoidal LED array, the light-diffracting effect of orthogonal gratings, and the filtering effect of the light diffuser. The mechanism and analytic model of fringe generation are thoroughly analyzed and discussed in this work. Furthermore, the swarm optimization algorithm and corresponding weighted fringe quality evaluation function are presented to obtain the optimal fringes. To the best of our knowledge, the proposed µPMD, for the first time, achieved a superfast fringe acquisition rate of 4000fps with sub-micrometer precision in three-dimensional (3D) reconstruction for specular surfaces. We envision this proposal to be broadly implemented for real-time monitoring in manufacturing.

Optimization of renewable energy hybrid power plant in the state of Karnataka, India

This paper proposes a methodology on reliability and cost minimization of the electricity produced by Renewable energy sources. The approach is to use Hybrid Renewable Energy Systems, which involves integrating various renewable sources. The objective of this study is to address these challenges by HRES method in the state of Karnataka. In Karnataka, there has been large scale curtailment of renewable energy generation due to the various structural issues. This study would give a solution to reliability and cost related issues faced by this state. For the study a Mathematical Model has been developed using Feed Forward Neural Network (FNN) and Chemical Reaction Optimization algorithms. Chemical reaction Optimization model is chosen to remove biases of the Artificial Neural Network Model of FNN. This model will be used to simulate interaction between Hybrid Renewable Energy Systems. The result of the study will provide a power system with high & optimized renewable energy output in the state of Karnataka, India which will be reliable and cost effective.

An efficient technique for developing the active sound control system in electric vehicle

The sound characteristic is a critical metric to manifest the brand differentiation of electric vehicle (EV), and the sound design with more diverse acoustic characteristics has become a hot issue in current research of EV technology. In this paper, an efficient technique of active sound design (ASD) is explored to develop the control system of active sound generation (ASG) with powerful sound quality for EV. Firstly, an optimization algorithm of sound synthesis based on multi-frequency superimposition is proposed to improve the harmonic interference phenomenon in the synthesized sounds. Subsequently, an adaptive sound control strategy is formulated, where an iterative accumulation method is proposed to calculate the “virtual engine speed” of EV, and a gain table is presented to divide the running state. Besides, an ASG system is developed based on the proposed ASD technique. The tests result demonstrate that there are 18 target order sounds are reproduced perfectly, and the powerful sound quality interior EV is enhanced while the original interior acoustic environment of EV is retained, which confirms the effectiveness of the proposed control technique of ASG system. The proposed ASD technique here accelerates the change from silence to sound quality in the electric vehicles, which has important theoretical significance and engineering value.

Patellar Sleeve Fracture: An Update of Literature

Patellar sleeve fractures, though relatively rare, present unique challenges in diagnosis and management. This review aims to provide a comprehensive overview of the current understanding of patellar sleeve fractures, focusing on their epidemiology, clinical presentation, imaging, and management strategies. Epidemiologically, these fractures are uncommon, representing approximately 1% of all fractures in pediatric patients, yet they account for 50% of all patella fractures in this population. They predominantly affect adolescents, with a peak incidence around 12.7 years of age, and are more common in boys, occurring at a ratio of 3 to 5:1. Understanding these demographic patterns is crucial for early recognition and appropriate management. Clinically, patellar sleeve fractures typically present with the sudden onset of severe pain, often associated with explosive activities such as jumping. However, diagnosis can be challenging, particularly in cases with minimal displacement or where alternative muscle groups compensate for the injury. Differential diagnosis is essential, and clinicians should be vigilant for signs such as palpable gaps at the lower pole of the patella and patella alta. Imaging modalities play a vital role in diagnosis, with plain X-rays often revealing no bony damage. Ultrasonography may offer a cost-effective alternative, especially in cases where radiographic findings are inconclusive. Advanced imaging techniques such as MRI can assist in characterizing the extent of the injury and assessing for associated complications. Management strategies encompass a spectrum of approaches, ranging from conservative measures such as immobilization and physical therapy to surgical interventions, including open reduction and internal fixation or arthroscopic surgery. The choice of treatment depends on various factors, including the fracture pattern, displacement, patient age, activity level, and associated injuries. Despite advancements in diagnosis and treatment, the optimal management of patellar sleeve fractures remains a clinical challenge. Further research is warranted to elucidate optimal algorithms for diagnosis and treatment, with the ultimate goal of improving outcomes and reducing the risk of long-term complications associated with this rare but clinically significant knee injury.

Optimization of Residential Hydrogen Facilities with Waste Heat Recovery: Economic Feasibility across Various European Cities

The European Union has established ambitious targets for lowering carbon dioxide emissions in the residential sector, aiming for all new buildings to be “zero-emission” by 2030. Integrating solar generators with hydrogen storage systems is emerging as a viable solution for achieving these goals in homes. This paper introduces a linear programming optimization algorithm aimed at improving the installation capacity of residential solar–hydrogen systems, which also utilize waste heat recovery from electrolyzers and fuel cells to increase the overall efficiency of the system. Analyzing six European cities with diverse climate conditions, our techno-economic assessments show that optimized configurations of these systems can lead to significant net present cost savings for electricity and heat over a 20-year period, with potential savings up to EUR 63,000, which amounts to a 26% cost reduction, especially in Southern Europe due to its abundant solar resources. Furthermore, these systems enhance sustainability and viability in the residential sector by significantly reducing carbon emissions. Our study does not account for the potential economic benefits from EU subsidies. Instead, we propose a novel incentive policy that allows owners of solar–hydrogen systems to inject up to 20% of their total solar power output directly into the grid, bypassing hydrogen storage. This strategy provides two key advantages: first, it enables owners to profit by selling the excess photovoltaic power during peak midday hours, rather than curtailing production; second, it facilitates a reduction in the size—and therefore cost—of the electrolyzer.

Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

Metalens Topology Optimization

Photonic metasurfaces are thin optical elements comprised of structures arranged strategically upon a surface to manipulate electromagnetic waves. To produce metasurfaces at a large scale, it is beneficial to obtain not just an optimal design, but also an understanding of which areas of the metasurface are most influential on the overall performance of the device and require greater manufacturing accuracy. My work consisted of implementing and testing a new physics-based method for identifying the relative importance of different regions of a proposed metasurface design. This method built on an existing topology optimization code, where the designable region of the metasurface was discretized, and the material density of each point in the design was optimized such that incoming light was maximally focussed to a target location. The existing optimization algorithm was then coupled to a molecular dynamics model called a Nosé-Hoover thermostat, by modelling the discrete locations on the design region as particles and their corresponding material densities as their positions. By numerically integrating the equations of motion of the thermostat model, I was able to generate metasurface designs at different thermostat temperatures, and observe how the designs changed with increasing temperature. To determine the most important areas of the metasurface design, I calculated the entropy of each of the "particles" for all temperature samples, and looked for design regions that had low entropy even at high temperatures, indicating strong convergence on an optimal material density value amidst high thermal noise. Once I implemented this design analysis framework, I tested it on both a metalens and a reflector design at multiple wavelengths of incoming light. My results demonstrated that this physics-based method provides easily interpretable information about the relative importance of different elements of a metasurface design.

Ultra-high-cardinality geometric shaping in the finite SNR regime.

Four-dimensional (4D) constellations with up to 131 072 points (17 bit/4D-sym) are designed for the first time using geometric shaping. The constellations are optimized in terms of mutual information (MI) and generalized MI (GMI) for the additive white Gaussian noise (AWGN) channel, targeting a forward error correction (FEC) rate of 0.8 at finite signal-to-noise ratios. The presented 15-17 bit constellations are currently the highest-performing constellations in the literature, having a gap to the AWGN capacity as low as 0.17 dB (MI) and 0.45 dB (GMI) at 17 bit/4D-sym. For lower cardinalities, our constellations match or closely approach the performance of previously published optimized constellations. We also show that (GMI-)optimized constellations with a symmetry constraint, optimized for a FEC rate of 0.8, perform nearly identical to their unconstrained counterparts for cardinalities above 8 bit/4D-sym. A symmetry constraint for MI-optimized constellations is shown to have a negative impact in general. The proposed procedure relies on a Monte-Carlo-based approach for evaluating performance and is extendable to other (nonlinear) channels. Stochastic gradient descent is used for the optimization algorithm for which the gradients are computed using automatic differentiation. This article is part of the theme issue 'Celebrating the 15th anniversary of the Royal Society Newton International Fellowship'.

Dynamic Niches-Based Hybrid Breeding Optimization Algorithm for Solving Multi-Modal Optimization Problem

Some problems exist in classical optimization algorithms to solve multi-modal optimization problems and other complex systems. A Dynamic Niches-based Improved Hybrid Breeding Optimization (DNIHBO) algorithm is proposed to address the multi-modal optimization problem in the paper. By dynamically adjusting the niche scale, it effectively addresses the issue of niche parameter sensitivity. The structure of the algorithm includes three distinct groups: maintainer, restorer, and sterile lines for updating operations. However, the maintainer individuals often stagnate, leading to the risk of the local optima. To overcome this, neighborhood search and elite mutation strategies are incorporated, enhancing the balance between exploration and exploitation. To further improve individual utilization within niches, a niche restart strategy is introduced, ensuring sustained population diversity. The efficacy of DNIHBO is validated through simulations on 16 multi-modal test functions, followed by comparative analyses with various multi-modal optimization algorithms. The results convincingly demonstrate that DNIHBO not only effectively locates multiple global optima but also consistently outperforms other algorithms on test functions. These findings underscore the superiority of DNIHBO as a high-performing solution for multi-modal optimization.

A Path Planning Method Based on Hybrid Sand Cat Swarm Optimization Algorithm of Green Multimodal Transportation

Aiming at the difficulty of measuring various costs and time-consuming elements in multimodal transport, this paper constructs a green vehicle comprehensive multimodal transport model which incorporates transportation, transit, quality damage, fuel consumption, and carbon emission costs and proposes a hybrid embedded time window to calculate the time penalty cost in order to reflect the actual transport characteristics. Furthermore, in order to better solve the model, a hybrid sand cat swarm optimization (HSCSO) algorithm is proposed by introducing Logistic–Tent chaotic mapping and an adaptive lens opposition-based learning strategy to enhance the global search capability, and inspired by the swarm intelligence scheme, a momentum–bellicose strategy and an equilibrium crossover pool are introduced to improve the search efficiency and convergence ability. Through testing nine benchmark functions, the HSCSO algorithm exhibits superior convergence accuracy and speed in dealing with complex multi-dimensional problems. Based on the excellent global performance, the HSCSO algorithm was utilized for multimodal vehicle transportation in East China, and a path with a lower comprehensive cost was successfully planned, which proved the effectiveness of the HSCSO algorithm in green intermodal transport path planning.

A novel fractional rat hawk optimization–enabled routing with deep learning–based energy prediction in wireless sensor networks

SummaryWireless sensor networks (WSNs) contain different sensors, which collect various data in the monitoring area. In general, one of the significant resources in WSNs is energy, which prolongs the network's lifetime. The energy‐efficient routing algorithms reduce energy consumption and enhance the survival cycle of WSNs. Thus, this work developed the optimization‐based WSN routing and deep learning (DL)–enabled energy prediction scheme for efficient routing in WSNs. Initially, the WSN simulation is carried out, and then, the node with minimum energy consumption is chosen as the cluster head (CH). Here, the proposed rat hawk optimization (RHO) algorithm is established for finding the best CH, and the RHO is the integration of rat swarm optimization (RSO) and fire hawk optimization (FHO). Furthermore, the routing is accomplished by the developed fractional rat hawk optimization (FRHO) using the fitness function includes delay, distance, link lifetime, and predicted energy of a network for predicting the finest route. Here, the fractional calculus (FC) is incorporated with the RHO to form the FRHO. The energy prediction is achieved by deep recurrent neural network (DRNN). The energy, delay, and throughput evaluation metrics are considered for revealing the efficiency of the proposed system, and the proposed system achieves the best results of 0.246 J, 0.190 s, and 67.13 Mbps, respectively.

Enhancing Smart Grid Efficiency through Machine Learning-Based Renewable Energy Optimization

Managing renewable energy in smart grids poses a significant challenge due to the inherent uncertainty and variability of energy sources like solar and wind power. To address this issue, we propose a novel approach that leverages the strengths of both Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) algorithms. Our method utilizes ELM to model and predict renewable energy generation, enabling more accurate forecasting and planning. Meanwhile, PSO optimizes the parameters of the ELM algorithm, ensuring optimal performance and efficiency. We evaluated our approach using a dataset of solar energy production and compared its performance to existing optimization techniques. The results show that our ELM-PSO approach significantly improves the accuracy of renewable energy predictions and reduces energy costs in smart grids. The implications of our research are far-reaching, as our approach can be applied to various renewable energy systems, including wind turbines, solar panels, and hydroelectric power plants. By enhancing the efficiency and reliability of renewable energy utilization, we can create a more sustainable and resilient energy future.

A Study on Hyperspectral Soil Moisture Content Prediction by Incorporating a Hybrid Neural Network into Stacking Ensemble Learning

The accurate prediction of soil moisture content helps to evaluate the quality of farmland. Taking the black soil in the Nanguan District of Changchun City as the research object, this paper proposes a stacking ensemble learning model integrating hybrid neural networks to address the issue that it is difficult to improve the accuracy of inversion soil moisture content by a single model. First, raw hyperspectral data are processed by removing edge noise and standardization. Then, the gray wolf optimization (GWO) algorithm is adopted to optimize a convolutional neural network (CNN), and a gated recurrent unit (GRU) and an attention mechanism are added to construct a hybrid neural network model (GWO–CNN–GRU–Attention). To estimate soil water content, the hybrid neural network model is integrated into the stacking model along with Bagging and Boosting algorithms and the feedforward neural network. Experimental results demonstrate that the GWO–CNN–GRU–Attention model proposed in this paper can better predict soil water content; the stacking method of integrating hybrid neural networks overcomes the limitations of a single model’s instability and inferior accuracy. The relative prediction deviation (RPD), root mean square error (RMSE), and coefficient of determination (R2) on the test set are 4.577, 0.227, and 0.952, respectively. The average R2 and RPD increased by 0.056 and 1.418 in comparison to the base learner algorithm. The study results lay a foundation for the fast detection of soil moisture content in black soil areas and provide a data source for intelligent irrigation in agriculture.

Invasive-Weed-Optimization-Based Extreme Learning Machine for Prediction of Lake Water Level Using Major Atmospheric–Oceanic Climate Scenarios

Fresh water lakes are vulnerable assets that need to be protected against manmade/natural challenges like climate change and anthropogenesis activities. This study addresses the predictability of the lake water level changes based on the knowledge acquired directly from the climate data. Two fresh water lakes named Lake Iznik and Uluabat, located in Turkey, are addressed. Time series of the lake water levels during October 1990–September 2019 at a monthly scale, along with the corresponding anomalies of 24 Large-Scale Atmospheric–Oceanic Oscillations (LSAOOs) from around the globe, are used in the analysis. The relationship between variables and the structure of the models are initially acquired based on the significance of the dependence between climate indices and lake water levels with consideration of the significance of the Spearman rank-order coefficient. Then, the time series are divided into training (80%) and testing (20%) sets. The Extreme Learning Method (ELM), enhanced with the genetic algorithm (ELM-GA) and Invasive Weed Optimization (ELM-IWO), is then used in the predictive models. Based on the results, Lake Uluabat showed a stronger teleconnection with LSAOOs, while the ELM-GA for Lake Iznik and ELM-IWA for Lake Uluabat depicted the best performance in the prediction of lake water levels. Comparison of the enhanced ELM-IWO to the corresponding ELM-GA illustrates that the ELM-IWO reveals more acceptable results owing to its flexible nature.

Routing algorithm for sparse unstructured P2P networks using honey bee behavior

SummaryUnstructured peer‐to‐peer (P2P) networks pose unique challenges for efficient and scalable routing. In this study, we introduce a novel routing algorithm inspired by the foraging behavior of honey bees named Honey Bee Optimization in P2P Networks (HBO_P2P) to address the inherent limitations of routing in unstructured P2P networks, focusing on improving packet delivery, minimizing hop count, reducing message overhead, and optimizing overall throughput. To evaluate the performance of our proposed algorithm, we conducted comprehensive experiments comparing it with existing algorithms commonly used in P2P networks, namely, particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO). After the simulation, we got the results as follows: Our algorithm outperforms ACO, GA, and PSO by exhibiting the highest number of data hops indicating potential efficiency in route optimization. Routing overhead is also minimal as compared to ACO, GA, and PSO. The average data packet delay is also low in our algorithm as compared to ACO, GA, and PSO. HBO_P2P achieves the highest throughput, nearly reaching 100 Mbps. While ACO and GA exhibit similar throughput of around 80 Mbps, and PSO has the lowest throughput, approximately 60 Mbps.

Exploring Innovative Methods in Maritime Simulation: A Ship Path Planning System Utilizing Virtual Reality and Numerical Simulation

In addressing the high costs, inefficiencies, and limitations of purely digital simulations in maritime trials for unmanned vessel path planning, this paper introduces a ship virtual path planning simulation test system. This system, unbound by temporal and spatial constraints, vividly showcases the navigational performance of vessels. After analyzing the virtual testing requirements for the autonomous navigation performance of unmanned surface vehicles (USVs), we established the overall framework of this system. Data-driven by a numerical simulation platform, the system achieves synchronized operation between physical and virtual platforms and supports interactive path planning simulations between USVs and the virtual testing system. Furthermore, to address the limitations of traditional ship trajectory planning evaluation, this paper develops a global path planning fitness evaluation function that comprehensively considers trajectory safety, navigation distance, and vessel stability, achieving optimal comprehensive routes through the particle swarm optimization algorithm. Test results indicate an average roll reduction of 14.31% in the planned routes, with a slight increase in navigation distance. By integrating two-dimensional curve simulation with three-dimensional visualization, this paper not only overcomes the limitations of purely physical and purely virtual simulations but also enhances the overall credibility and intuitiveness of the simulation. Experimental results validate the system’s effectiveness, providing a novel method for autonomous navigation testing and evaluation of USVs.