Powerful Optimization Algorithm Research Articles

Neural network algorithm with transfer learning and dropout for using a UAV to search the lost target in motion

When performing the rescue mission, how to find a lost target in motion as soon as possible is a very valuable and interesting research topic. To find the optimal flight path of the UAV used to search the lost target in motion under different prior knowledge and interference intensity, this paper proposes a novel optimization technique called neural network algorithm with transfer learning and dropout (TLDNNA), which is a variant of neural network algorithm (NNA) inspired by artificial neural networks. To improve the global search ability and convergence performance of NNA, the multiple transfer mechanism, the generalized mean transfer position, and the random dropout operator driven by the principles of transfer learning and dropout are introduced to TLDNNA. To investigate the performance of TLDNNA, TLDNNA and 12 powerful population-based optimization algorithms are applied to the problem of using a UAV to search the lost target in motion in the designed 9 scenarios. Experimental results show that TLDNNA is remarkably better than the other 12 algorithms in most of the scenarios in terms of solution quality, stability, and convergence performance. In addition, TLDNNA can improve the overall search performance by 18.14% and overall computational efficiency by 3.41 times compared to NNA. The source code of TLDNNA can be obtained by https://github.com/jsuzyy/TLDNNA.

AnFiS-MoH: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms

The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it challenging to optimize via traditional gradient-based methods alone. This study introduces AnFiS-MoH, a novel framework that synergistically integrates ANFIS with metaheuristic optimization algorithms to address these challenges. By leveraging the global search capabilities of metaheuristics such as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA), ANFIS-MOH enhances the parameter tuning process of ANFIS models. We evaluate ANFIS-MOH on benchmark datasets including Boston Housing and Wine Quality, demonstrating significant improvements in prediction accuracy and generalization compared to traditional ANFIS and neural network approaches. The proposed framework achieves up to 20% reduction in Mean Squared Error and 15% increase in R2 scores, particularly excelling in handling high-dimensional, noisy data. This work contributes to the field of hybrid intelligent systems by introducing effective ways to combine the strengths of ANFIS with powerful metaheuristic optimization algorithms. The findings suggest that such hybrid approaches can be effective in tackling challenging nonlinear modeling problems. Our code is available at https://github.com/AmbitYuki/Metaheuristic-Adaptive-ANFIS.

Email Spam Detection Using a Hybrid Approach of Feedforward Neural Network and Penguin Optimization Algorithm

The electronic communication of today is beset by unwanted junk messages. These are not just a nuisance; they waste our time and energy and clog up our inboxes, which are supposed to be, in many ways, our digital front doors. But how do we keep them from coming? Well, as today's article makes clear, the situation is hardly straightforward. There are various methods that can be used, both traditional and modern, and each has its own set of pros and cons. One method, however, stands out in today's article as one that we think might work well in practice, the good old-fashioned neural network. The method's novelty derives from its use of neural networks, a type of machine learning, in conjunction with the POA. The POA is a powerful optimization algorithm that can perform very well when fine-tuning the weights and biases of a neural network. We therefore used the POA to optimize these two crucial components of our model and achieved a surprising level of accuracy—98.7%. This figure surpasses the next nearest competitor by 2% and demonstrates the efficacy of our method in spam detection while also highlighting the POA's potential in this field. The overall proposed method pushes the spam-detection field forward, but it also demonstrates something even broader: the power of hybrid approaches in machine learning. Neural networks are great—but you have to train them properly, and the use of a particle swarm for that purpose is a neat idea. But in the end, a hybrid spam filter is still a spam filter, and this one does what it's supposed to do better than many rivaling methods.

Optimized Selective Assembly using Hungarian Algorithm

Assembly of discrete parts guided by hardware design specification constitutes the final phase in product manufacturing. In the course of mass production of components, mating parts with geometric or dimensional deviation from their intended design can be made acceptable by the identification of suitable pairs after analysing design fit and tolerance limits. By transforming this application-specific problem into a unified mathematical model, an optimal solution can be achieved that minimizes the rejection of non-conforming fabricated parts. Regardless of the type and range of a design fit, the problem can be mapped into a matrix using a ranking function defined by the user. The ranking function is modifiable as per the user requirements and may vary based on the selection criteria for an assembly. Based on the type of ranking function used, the tabulated matrix is solved using the Hungarian minimization/maximization algorithm, which is a powerful combinatorial optimization algorithm that solves the classical assignment problem in mathematics. This approach ensures maximum number of suiting pairs as well as nominal suiting of parts with each other resulting in high-quality products and maximum utilization of fabricated resources.

Optimized task scheduling approach with fault tolerant load balancing using multi-objective cat swarm optimization for multi-cloud environment

Multi-cloud environment enables an organization to access services from more than one cloud service providers the use of multiple cloud computing and it can be treated as single heterogeneous environment. It enables autonomy to run the tasks on private or public cloud based on business or technical requirements. In a multi-cloud platform, load balancing is an essential task to serve the requests from multiple users with different resources effectively. It helps to improve utilization of the cloud resources, throughput, reduce makespan and avoid overload at resources. Load balancing also facilitates the redirection of traffic to resources running in another cloud when a failure occurs in a cloud. Hence, it is more vital to have optimized load balancing methods in multi-cloud infrastructure in order to improve the system performance. This paper presents an optimized fault tolerant load balancing method using multi-objective cat swarm optimization algorithm called MCSOFLB and the results are then compared against other powerful optimization algorithms. The experimental results evidently show that the proposed algorithm ranks first on the whole. The MCSOFLB method produces an average improvement of 31 % makespan, 6 % resource utilization, 12 % cost, 6 % success rate and 32 % average throughput over other benchmark algorithms.

PEA-PODs: Perceptual Evaluation of Algorithms for Power Optimization in XR Displays

Display power consumption is an emerging concern for untethered devices. This goes double for augmented and virtual extended reality (XR) displays, which target high refresh rates and high resolutions while conforming to an ergonomically light form factor. A number of image mapping techniques have been proposed to extend battery usage. However, there is currently no comprehensive quantitative understanding of how the power savings provided by these methods compare to their impact on visual quality. We set out to answer this question. To this end, we present a perceptual evaluation of algorithms (PEA) for power optimization in XR displays (PODs). Consolidating a portfolio of six power-saving display mapping approaches, we begin by performing a large-scale perceptual study to understand the impact of each method on perceived quality in the wild. This results in a unified quality score for each technique, scaled in just-objectionable-difference (JOD) units. In parallel, each technique is analyzed using hardware-accurate power models. The resulting JOD-to-Milliwatt transfer function provides a first-of-its-kind look into tradeoffs offered by display mapping techniques, and can be directly employed to make architectural decisions for power budgets on XR displays. Finally, we leverage our study data and power models to address important display power applications like the choice of display primary, power implications of eye tracking, and more 1 .

Investigation the effects of operational parameters and multi-factor optimization of butene metathesis process via statistical approach

In this study, the production of propylene from 2-butene and 1-butene via the metathesis process was investigated and simulated. The effects of operational parameters, including reactor temperature, residence time, and 1-butene/2-butene molar ratio, on reactor performance were examined, and statistical models were developed. The design of experiment (DoE) was performed using the response surface methodology (RSM)-central composite design (CCD) approach for efficient experimentation and model development as well as optimization. The study's findings indicate a high conversion rate for 2-butene. By statistically analyzing the obtained data and performing analysis of variance (ANOVA), the influential parameters and their degree of importance were determined. Sensitivity analysis was conducted based on the developed models. Finally, using these models and desirability function as a powerful optimization algorithm, optimal conditions for the process were obtained. The results of the optimization showed that the maximum propylene molar fraction (32.5 %) was obtained at a temperature of 508.4 °C, a residence time of 20.7 s, and a 1-butene/2-butene molar ratio of 0.1.

A Kepler optimization algorithm improved using a novel Lévy-Normal mechanism for optimal parameters selection of proton exchange membrane fuel cells: A comparative study

Proton exchange membrane fuel cells (PEMFCs) are considered a promising renewable energy source and have sparked a lot of interest over the last few years due to their robust efficiency, low operating temperature, and longevity. The PEMFC's electrochemical model has seven unknown parameters, which are not given in the manufacturer's datasheets and need to be accurately estimated to present a more accurate model, leading to improved efficiency and performance of the PEMFC systems. The estimation of those unknown parameters has been dealt with as a complex and non-linear optimization problem that needs a powerful optimization algorithm to solve it. The existing optimization algorithms still have some disadvantages, such as falling into local minima and low convergence speed, which make them ineligible to solve this complicated problem with acceptable accuracy and low computational cost. Therefore, this study presents a new parameter estimation technique for estimating the unknown parameters of the PEMFC model more accurately, thereby achieving precise modeling of PEMFCs. This technique called IKOA is based on integrating the Kepler optimization algorithm (KOA) with a novel Lévy-Normal (LN) mechanism to strengthen its exploration and exploitation capabilities against this multimodal optimization problem. The Lévy flight in this mechanism aims to improve the KOA's exploitation operator to accelerate the convergence speed toward the near-optimal solution, thus minimizing the computational cost; meanwhile, the normal distribution is used to strengthen its exploration operator, thereby aiding in the escape of local minima. The proposed IKOA and KOA are herein evaluated against several rival algorithms using six well-known commercial PEMFC stacks to highlight their efficiency and effectiveness. Key performance metrics such as computational cost, fitness measures, and statistical validation through the Wilcoxon rank-sum test are herein used to highlight IKOA's effective performance in enhancing the predictive accuracy and operational efficiency of PEMFCs. The numerical findings show the high superiority of IKOA against all rival optimizers on all solved benchmarks.

Additional Acceleration of Antenna Optimal Characterizationwith Modeling Support

Optimal characterization is assumed to provide the best solution for the designed cost function among the possible solutions within the specified range. These processes can take a long time depending on the applications and computer hardware used. Here, the optimization process is supported by ANN modeling in order to shorten the current optimization processes as much as possible. For this purpose, the selection of design parameters of the bowtie patch antenna (BPA) is presented as a multi-dimensional, multi-objective modeling-supported design optimization problem. The operating frequency of the proposed antenna is 28 GHz, which is the standard for millimeter wave band and 5G technologies. To overcome this challenging design optimization, a new, fast and powerful optimization algorithm was used by modifying the non-dominant sorting genetic algorithm (NSGA)-III, and the optimal characterization of the microwave antenna design was achieved. Although the proposed method gives the same results compared to the existing process, it takes much less time. Therefore, it is possible to shorten the process and reduce costs without the need for extra applications or hardware. As a whole, the proposed design optimization process is an efficient, fast and reliable solution for all design problems.

Statistical spatial information based power optimization algorithm for multi-user massive MIMO systems

Power optimization has a significant role in multi-user massive multiple-input and multiple-output (mMIMO) wireless communication systems, whereas the acquisition of instantaneous channel state information (I-CSI) is a serious challenge in practical scenarios. The statistical spatial information (SPI), which contains angle-of-departures (AoDs) and corresponding large-scale fading factors, is slowly varying. The power optimization based on SPI to improve system performance needs to be explored for multi-user mMIMO systems. In this paper, two novel algorithms for SPI-based power optimization are proposed to maximize the ergodic sum-rate (ESR) under total power constraint for the system. Specifically, the minorization-maximization-based power optimization (MM-PO) algorithm and the closed-form power optimization (CF-PO) algorithm are proposed utilizing the minorization-maximization (MM) method and the fractional programming (FP) method, respectively. Moreover, the ESR is expressed based on the extracted SPI. The approximate expression of the ESR is derived. The complexity of the algorithms depends on the number of users, and the algorithms are scalable in the system. The proposed algorithms can effectively achieve the advantages of SPI while being robust to the uncertainty of I-CSI. Simulation results verify the ESR of our two proposed algorithms is 1.47 and 1.4 times higher than that of the equal power allocation algorithm at a high SNR when the number of antenna and user are equal to 64 and 20, respectively.

Predictive analysis of concrete slump using a stochastic search-consolidated neural network

Attaining a dependable measurement of concrete slump is crucial as it is a valuable indication of concrete workability. On the other hand, complexities associated with costly traditional approaches have driven engineers to use indirect efficient models such as metaheuristic-based machine learning for approximating the slump. While the literature shows promising application of some metaheuristic techniques for this purpose, the large variety of these algorithms calls for evaluating the most capable ones to keep the solution updated. Stochastic fractal search (SFS) is one of the most powerful optimization algorithms in the literature that has not received appropriate attention in analyzing concrete mechanical parameters. In the present research, a multi-layer perceptron neural network (NN-MLP), is enhanced using the SFS. The proposed SFS-NN-MLP model aims to predict the slump based on the amount of ingredients in the mixture, as well as the curing age of specimens. Accuracy assessment revealed that the proposed model can deal with the assigned task with excellent accuracy. It indicates that the SFS could properly tune the parameters required for training the NN-MLP, and consequently, the trained network could reliably calculate the slump of specimens that were not analyzed before. For comparative validation, the SFS was replaced with two similar optimizers, namely elephant herding optimization algorithm (EHO) and slime mould algorithm (SMA). Based on the calculated mean square errors of 5.6526, 6.1129, and 7.3561 along with mean absolute errors of 4.6657, 5.0078, and 6.3066, as well as the percentage-Pearson correlation coefficients of 78.06 %, 73.95 %, and 58.11 %, respectively for the SFS-NN-MLP, EHO–NN–MLP, and SMA–NN–MLP, it was shown that the SFS-NN-MLP is the most accurate predictor. Hereupon, the SFS-NN-MLP model is recommended to be effectively used for obtaining a cost-efficient approximation of concrete slump in real-world projects.

Multi-strategy enhanced Marine Predators Algorithm with applications in engineering optimization and feature selection problems

This paper introduces a novel meta-heuristic optimization algorithm named Clustering Wavelet Opposition-based Marine Predators Algorithm (CWOMPA) to address some limitations present in the well-established Marine Predators Algorithm (MPA). CWOMPA incorporates three key strategies: a fuzzy clustering approach for escaping local optima, using wavelet basis function-based impact coefficient adjustment for elites to prevent premature convergence, and finally an adaptive opposition-based learning strategy for maintaining population diversity. Compared with some recent meta-heuristic algorithms, extensive evaluations conducted affirm that CWOMPA achieves the best Friedman rank, 4.30 and 1.95 respectively, on 23 benchmark functions and the CEC 2017 benchmark set. Not only does CWOMPA demonstrate significant effectiveness on six constrained problems from the CEC 2020 real-world benchmarks, but also when applied to 14 medical datasets, it gains superior Friedman ranks in terms of selected features, classification accuracy, F-Score, and objective function value, 2.5, 2.25, 2.96, 2.93 respectively, outperforming other common methods. Finally, CWOMPA exhibits the highest classification accuracy across all datasets and the best F-Score performance in nine datasets compared to traditional feature selection algorithms. The obtained results reveal that CWOMPA is a powerful and versatile optimization algorithm with significant potential in various real-world applications, including feature selection for medical datasets.

D2D Matching Techniques for Resource Allocation Management and Power Control Management in B5G/6G

The upcoming 6G network is expected to have a great integration of Device-to-Device (D2D) technology. One of the key advantages of D2D technology is its potential to minimize the load on cellular base stations, which can help extend the lifespan of cellular network infrastructure. D2D multicast communication can meet the growing demand for multimedia content in local area services by maximizing energy efficiency and device lifespan by reusing the same resources for the cellular network. In this paper, we investigate an efficient resource allocation scheme by using the One-To-Many Gale Shapley pairing algorithm (GSA) for efficient allocation and power optimization. We propose a joint optimization approach for D2MD clusters that considers the Signal-to-Interference Noise ratio and energy levels of devices' batteries. The problem is optimized, so it is divided into two convex sub-problems. In the first sub-problem, power allocation is performed for each candidate cluster-head (CH)and cellular user to maximize energy efficiency using the Dinkelbach matching algorithm. In the second sub-problem, the One-to-Many Gale Shapley matching algorithm is used to optimize resource allocation and cluster-head selection to select the Cluster Nodes (CRns) to form the cluster. Numerous investigations show that the suggested technique maintains QoS and minimal battery power requirements while increasing cluster-head longevity and energy efficiency (EE) in D2D applications.

Voltage and Reactive Power-Optimization Model for Active Distribution Networks Based on Second-Order Cone Algorithm

To address the challenges associated with wind power integration, this paper analyzes the impact of distributed renewable energy on the voltage of the distribution network. Taking into account the fast control of photovoltaic inverters and the unique characteristics of photovoltaic arrays, we establish an active distribution network voltage reactive power-optimization model for planning the active distribution network. The model involves solving the original non-convex and non-linear power-flow-optimization problem. By introducing the second-order cone relaxation algorithm, we transform the model into a second-order cone programming model, making it easier to solve and yielding good results. The optimized parameters are then applied to the IEEE 33-node distribution system, where the phase angle of the node voltage is adjusted to optimize the reactive power of the entire power system, thereby demonstrating the effectiveness of utilizing a second-order cone programming algorithm for reactive power optimization in a comprehensive manner. Subsequently, active distribution network power quality control is implemented, resulting in a reduction in network loss from 0.41 MW to 0.02 MW. This reduces power loss rates, increases utilization efficiency by approximately 94%, optimizes power quality management, and ensures that users receive high-quality electrical energy.

Impact of Component Correlation on Probabilistic Seismic Responses of Steel Frames by Modifying Non-Positive Definite Correlation Matrices

ABSTRACT This paper studies the effects of spatial correlation between the structural components on probabilistic seismic performance of steel moment resisting frames, using extended incremental dynamic analysis (IDA) method to consider the effects of aleatoric uncertainties caused by ground motion records. In this probabilistic assessment, a combination of Latin hypercube sampling (LHS) and Cholesky decomposition (CD) methods are utilized to take the epistemic uncertainties of the numerical model into account. In case the target correlation matrix is not positive definite, two powerful optimization algorithms are employed to find the closest positive definite correlation matrix.

Data Power Optimization Algorithm for Downlink Multi-ARS Small Cell Communication Systems

In this paper, we investigate a novel downlink Small Cell (SC) system based on the Cell Free (CF) model,with the deployment of Unmanned Aerial Vehicles (UAVs) as Aerial Relay Stations (ARSs) simultaneously serving users in a specific area. The new SC model concept is expressed in the user-centric idea that the user will choose only one ARS with the best conditions for communication, and the concept of “cell" or “cell boundary" will no longer exist. The Time Division Duplex (TDD) mechanism is used for system-wide communication protocol, and the Minimum Mean Squared Error (MMSE) technique is applied for downlink channel estimation. We establish the downlink user throughput expression and then propose a Bisection optimization algorithm to control power for downlink data transmission to improve system quality and save energy. We evaluate the downlink multi-ARS SC communication system with and without power optimization in different aspects, such as altering the number of users, the number of ARSs, the number of antennas, and the transmission channel estimation interval. The results also reveal that the system quality is significantly improved when applying the downlink power factor optimization problem

Inverse design machine learning model for metallic glasses with good glass-forming ability and properties

The design of metallic glasses (MGs) with good properties is one of the long-standing bottlenecks in materials science and engineering, which has been relying mostly on far less efficient traditional trial-and-error methods. Even the currently popular machine learning-based forward designs, which use manual input to navigate high dimensional compositional space, often become inefficient with the increasing compositional complexity in MGs. Here, we developed an inverse design machine learning model, leveraging the variational autoencoder (VAE), to directly generate the MGs with good glass-forming ability (GFA). We demonstrate that our VAE with the property prediction model is not only an expressive generative model but also able to do accurate property prediction. Our model allows us to automatically generate novel MG compositions by performing simple operations in the latent space. After randomly generating 3000MG compositions using the model, a detailed analysis of four typical metallic alloys shows that unreported MG compositions with better glass-forming ability can be predicted. Moreover, our model facilitates the use of powerful optimization algorithms to efficiently guide the search for MGs with good GFA in the latent space. We believe that this is an efficient way to discover MGs with excellent properties.

Multi-UAV-assisted Internet of Remote Things communication within satellite–aerial–terrestrial integrated network

Due to the limited transmission capabilities of terrestrial intelligent devices within the Internet of Remote Things (IoRT), this paper proposes an optimization scheme aimed at enhancing data transmission rate while ensuring communication reliability. This scheme focuses on multi-unmanned aerial vehicle (UAV)-assisted IoRT data communication within the satellite–aerial–terrestrial integrated network (SATIN), which is one of the key technologies for the sixth generation (6G) networks. To optimize the system’s data transmission rate, we introduce a multi-dimensional coverage and power optimization (CPO) algorithm, rooted in the block coordinate descent (BCD) method. This algorithm concurrently optimizes various parameters, including the number and deployment of UAVs, the correlation between IoRT devices and UAVs, and the transmission power of both devices and UAVs. To ensure comprehensive coverage of a large-scale randomly distributed array of terrestrial devices, combined with machine learning algorithm, we present the Dynamic Deployment based on K-means (DDK) algorithm. Additionally, we address the non-convexity challenge in resource allocation for transmission power through variable substitution and the successive convex approximation technique (SCA). Simulation results substantiate the remarkable efficacy of our CPO algorithm, showcasing a maximum 240% improvement in the uplink transmission rate of IoRT data compared to conventional methods.

Developing Uplink Power Optimization and ARS Selection Algorithm for Multi-ARS Small Cell Communication System

Small Cell (SC) models and unmanned aerial vehicles (UAVs) acting as aerial relay stations (ARSs) are both promising advancements in the development of upcoming wireless networks that contribute significantly to improving the overall service quality. In this work, we rely on the Multi-ARS Cell-Free (CF) model, where a large number of ARS coordinated by the ground base station (GBS) and cooperate to serve a large number of users within the same frequency and time resources, to develop the uplink of a multi-ARS SC system, in which each user is served by only one ARS. The time division duplex (TDD) mechanism is used for communication protocol, and the Minimum Mean Square Error (MMSE) method is implemented to estimate the uplink channel. We derive an closed-form expression for uplink user throughput. In addition, we introduce the ARS selection method based on channel conditions and propose the Bisection algorithm to optimize uplink power. The system performance is evaluated by the cumulative distribution function (CDF) of user throughput according to different parameters, such as changing the number of ARS, the number of users, the number of antennas, and the length of pilot sequences with/without power optimization. The results reveal that the ARS selection method is effectively resolved to reduce complexity and improve the practicality of the proposed system, and the power optimization problem for better throughput is non-optimization.

Оптимизационный алгоритм управления водными ресурсами водохранилищ для обоснования диспетчерских графиков

The rules for the use of reservoir water resources need to be constantly updated, considering a significant number of factors affecting the effectiveness of management. Anthropogenic changes in morphometric functions, partial siltation of dead volume, and changes in basic hydrological characteristics have a significant impact on reservoir parameters and flow regulation regime. Justification of the regulatory regime remains an urgent and traditionally difficult engineering task of the IRP, discussed at professional meetings and in scientific publications. The article discusses a methodological approach based on the introduction of a given control algorithm into the process of assigning an interval request to the reservoir. In this case, the request is formulated as a two-parameter power function of the predicted resource for the estimated time interval (month). The degree indicator and the relative length of the range of uninterrupted water requirements coverage are the arguments of the query function. Nomograms illustrating the influence of the degree indicator on the outline of the dependence of requirements on the reservoir are constructed. In our opinion, this approach significantly reduces the Pareto set, excluding a significant part of the set of formal optima. The algorithm of the proposed model is focused on two-criteria optimization, where the relative values of the total annual water output and the sum of the relative interval ordinates of water consumption are considered as criteria. The unified functional is formed by linear convolution of criteria. The model was tested on the example of the resources of the Zhizdra River for a series of two low-water years. In the process of balance modeling and optimization, Excel tools are used, including “solution search” and VBA procedures. It is noted that it is necessary to use more powerful optimization algorithms for calculations based on multiyear hydrological series.