Powerful Optimization Algorithm Research Articles

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.

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.

An Intelligent System for Dental Disease Detection Using Smart R-CNN Technique

Artificial intelligence (AI) in the medical system has more with the development of research in the field of deep learning, demand has increased. However, only a few Deep Learning models can deliver solutions in real-time applications. In these type of real time medical systems, the primary concern is data availability and the same occurred for dental dataset. In this paper the most optimized and accurate method is discussed. Dataset is classified and labeled into 5 different datasets. We propose a faster neural network with a Densenet model in this research. Densenet is a useful tool in deep learning for overcoming obstacles such as picture segmentation, accurate categorization of images with high levels of recognition, and powerful optimization algorithms that speed up convergence speeds. lowers the local computation on each subsequent outer iteration. We discovered from the current system that when there is a large dataset, the Densenet model exhibits good classification in original picture classification issues. We have identified three different diseases associated with various types of cavities, such as frontal teeth cavities, inner cavity teeth, and oral cancer, which will determine the difference between diseased and healthy teeth in teeth disease detection using this network construction framework on real clinical data at various levels. In order to incorporate the built ML model into the webapp, it is being stored and translated into JSON file format. An interface for diagnosing dental problems will be provided by the web application.

An adaptive differential evolution with opposition-learning based diversity enhancement

Differential Evolution (DE), as a powerful population-based stochastic optimization algorithm, has attracted the attention of researchers from various fields due to its advantages such as simple operation, strong robustness, and few control parameters. However, many existing DE variants often suffer from drawbacks such as premature convergence and stagnation when solving complicated optimization problems. In view of the aforementioned issues, this paper proposes an adaptive DE with opposition learning-based diversity enhancement (OLBADE). The main contributions can be summarized as follows: Firstly, a new adaptive parameter control is proposed with a non-linear weighting strategy incorporating into the framework of parameter adaptation. Secondly, a donor vector perturbation strategy is introduced to complement existing strategy for increasing population diversity. Thirdly, a novel stagnation indicator is proposed, and then opposition learning strategy is employed to renew stagnated individuals in the population when stagnation occurs. OLBADE is compared with five excellent DE variants under a large test-bed containing CEC2013, CEC2014, CEC2017 and CEC2022 test suites to verify its effectiveness. In addition, OLBADE is applied in parameter identification problem of photovoltaic model to verify its feasibility. Experimental results demonstrate that OLBADE achieves higher solution accuracy, faster convergence speed and better stability.

Optimal solution of the combined heat and power economic dispatch problem by adaptive fitness-distance balance based artificial rabbits optimization algorithm

Combined heat and power economic dispatch (CHPED) problem is one of the most widely handled, optimization problem by researchers in modern power systems. CHPED problem is a complicated, non-continuous, and non-convex optimization problem due to the constraints. Moreover, considering the valve-point loading effect (VPLE), transmission losses (TLs), and prohibited operating zones (POZs) of power-only units as constraints, the complexity of CHPED problem increases. Therefore, a powerful optimization algorithm needs to be introduced to find global solution that meets all constraints. In this paper, a novel adaptive fitness-distance balance based artificial rabbits optimization (AFDB-ARO) is developed to solve CHPED problems. AFDB-based guiding mechanism was implemented to enhance the exploration capability of ARO and to strengthen exploitation-exploration balance. A comprehensive experimental study was realized to prove the performance of the proposed algorithm on the CHPED and benchmark problems. In experimental study between AFDB-ARO variants and ARO on 40 benchmark problems, according to Wilcoxon analysis results, all AFDB-ARO variants outperformed the base ARO, and the best AFDB-ARO variant won victory in 20 of 40 problem and achieved similar results in other 20 problem. In other experimental study, AFDB-ARO algorithm was implemented on the CHPED systems with 4-, 5-, 7-, 24-, 48-, 96-, and 192-units, and fifteen case studies were considered using these systems, VPLE, TLs, and POZs. One of the important points of this study was that POZs were considered for the first time in 96- and 192-units system. The results show that AFDB-ARO achieved the best optimal solution in ten of fifteen cases, was same in one case, and obtained almost same results in four cases compared to the literature. Moreover, the stability of the AFDB-ARO and base ARO algorithms in solving the CHPED problem were tested by performing stability analysis. While the mean success rate, mean iteration number, and mean search time were obtained 87.62%, 353.63, and 2.91 sec of AFDB-ARO, respectively, ARO managed to find the optimal solution in two cases. Thus, the superior performance of AFDB-ARO algorithm is confirmed by experimental studies and analysis against ARO algorithm. The source codes of the AFDB-ARO algorithm (proposed method) can be accessed at this link: https://www.mathworks.com/matlabcentral/fileexchange/136846-afdb-aro-an-improved-aro-algorithm-for-optimization-problem.

User group-based pilot allocation and data power optimization in cell-free massive MIMO for coexistence of UAVs and ground users

To address the pilot contamination issue in cell-free massive multi-input multi-output (MIMO) systems supporting simultaneous communication between unmanned aerial vehicles (UAVs) and ground users (GUEs), a user group-based pilot allocation algorithm is proposed. Furthermore, the system performance is enhanced by optimizing the data power. Initially, the distances between users and pilots are calculated based on the principles of maximizing spectral efficiency and utilizing the large-scale fading matrix. Ground users and UAVs are randomly divided into two groups: primary users and shared users. The contamination intensity between users is measured based on the distance between users and pilots, and shared user pairs that are allocated the same orthogonal pilots are generated. Next, a user-centric approach is employed to select a subset of access points (APs) to serve the users. The scalable fractional power optimization strategy is combined as a power optimization scheme for uplink data transmission, providing a controllable trade-off between user fairness and average spectral efficiency. Simulation results demonstrate that the user group-based pilot allocation algorithm significantly improves the system’s overall spectral efficiency, and the fractional power optimization algorithm enhances the average spectral efficiency of the system.

Optimal Power Flow for Hybrid AC/DC Electrical Networks Configured With VSC-MTDC Transmission Lines and Renewable Energy Sources

This article presents the single objective optimal power flow (OPF) formulation incorporating both renewable energy sources, and voltage source converter-based multiterminal direct current transmission lines, simultaneously. To solve the formulated OPF problem, powerful metaheuristic optimization algorithms including adaptive guided differential evolution, marine predators algorithm, atom search optimization, stochastic fractal search (SFS), and fitness-distance balance-based SFS (FDB-SFS) are employed. The performance of the algorithms is tested for the minimization of fuel cost, pollutant emissions of thermal generators, voltage deviation, and active power loss in a modified IEEE 30-bus power network. The simulation results give that FDB-SFS achieved the best results on the fuel cost (786.5361 $/h), the fuel cost with valve point effect (815.6644 $/h), and the fuel cost with emission-carbon tax (820.5991 $/h). In addition, FDB-SFS reduced voltage deviation and active power loss values by 14.2587% and 6.7438% compared to SFS. The nonparametric Wilcoxon and Friedman statistical test results confirmed that FDB-SFS is an effective and robust algorithm that can be used in the optimization of the introduced OPF problem.

Adaptive differential evolution algorithm based on deeply-informed mutation strategy and restart mechanism

Differential evolution is one of the most powerful stochastic real-parameter optimization algorithms currently, and its performance depends heavily on control parameters and mutation strategy. In recent years, methods to select favorable parameters control and mutation strategy when solving various optimization problems have attracted increasing attention. To choose an appropriate mutation strategy and control parameters for a given optimization problem, in this paper, a Adaptive Differential Evolution Algorithm Based on Deeply-Informed Mutation Strategy and Restart Mechanism (ADEDMR) is proposed, and the ADEDMR algorithm has the following characteristics: First, a deeply-informed mutation strategy is proposed, which takes into account the information of suboptimal solutions discarded by selection and inherits the advantages of the powerful “DE/target-pbest/1/bin”, aiming to obtain a better perceptual landscape of the target function and improve the candidate diversity of the trial vector. Second, according to the evolution process, the segmentation method is used to control F, which alleviates the scaling of F in the wrong direction, and makes the newly generated F fit more accurately. Third, a new population restart mechanism is adopted to further enhance population diversity by adaptively enhancing the search ability of hopeless individuals and randomly replacing some inferior individuals with wavelet walks. To evaluate the performance of our proposed algorithm, comparative experiments are conducted on 72 benchmark functions from the CEC2014, CEC2017 and CEC2022 test suites. Experimental results show that the proposed ADEDMR has higher convergence accuracy, better optimization ability when solving high-dimensional complex functions, and is competitive with six recent strong DE variants.

Minimizing energy consumption in UAV assisted NOMA-MEC networks

Mobile edge computing (MEC) technology is seen as a solution to the limitation of computing capability of Internet of Things (IoT) devices. In this paper, we investigate an edge computing framework that supports energy savings for UAV in the case of coexistence of edge computing users and latency-sensitive communication users, who use uplink non-orthogonal multiple access (NOMA) technique to communicate with UAV. The users are grouped to satisfy the delay-sensitive requirements of communication users. Then, the system energy consumption is minimized by optimizing the transmission power and computational resource allocation of the computation users while satisfying the quality-of-service (QoS) of the communication users. Since the proposed problem is nonconvex, it is transformed into a convex problem using the successive convex approximation (SCA) algorithm in this paper. At last, we propose a joint global computational resource and communication power optimization algorithm (GCPA) to minimize the system energy consumption. The simulation results show the effectiveness of the proposed scheme.

Multi-Objective Optimal Power Flow Incorporating Flexible Alternating Current Transmission Systems: Application of a Wavelet-Oriented Evolutionary Algorithm

This article presents a meta-heuristic hybrid algorithm to solve multi-objective optimal power flow (MOOPF) taking into account multi-fuel constraint. Four different objective functions, which comprise total generation cost, emission, real power transmission losses, and voltage deviation, are simultaneously considered in the proposed MOOPF. Furthermore, two types of flexible alternating current transmission system (FACTS) devices are utilized including thyristor controlled series compensator (TCSC) and unified power flow controller (UPFC). Considering all of the aspects referred to above makes the traditional OPF problem more complicated; hence, it is necessary to approach the problem by a powerful optimization algorithm. Toward this end, this article develops a hybrid algorithm oriented to shuffled frog leaping algorithm (SFLA) and particle swarm optimization (PSO), called hybrid wavelet mutation (WM) based SFLA-wavelet mutation based PSO (HWM2SFLA-PSO). Obtained results validate the effectiveness of the proposed hybrid algorithm in solving different forms of the OPF problem on IEEE test systems compared to those available in the literature. Moreover, the comparison substantiates the superiority of the proposed HWM2SFLA-PSO algorithm in terms of execution time, quality of solution as well as preventing from convergence to local optima, to name but three.

UAV Anti-Jamming Communications With Power and Mobility Control

Unmanned aerial vehicle (UAV)-enabled air-ground integrated communication systems are vulnerable to jamming attack, mainly due to the high probability with line-of-sight wireless channel conditions. Although a few UAV anti-jamming transmission schemes have been recently proposed, the fundamental performance limits by simultaneously considering the UAV and jammer’s mobility have not yet been reported. These observations motivate us to formulate an interactive reward region (IRR) characterization problem for the scenario that the UAV and the jammer compete with each other to maximize their respective rewards via power and mobility control. Then, we propose a transmit and jamming power optimization algorithm to characterize the IRR with power control, by leveraging successive convex approximation technique, where two transmit power design schemes are given in closed-form expressions from a game-theoretic perspective. Furthermore, the IRR with joint power and mobility control is characterized based on alternating optimization, in which both the UAV and jammer can simultaneously adjust their power and trajectories. Finally, extensive simulation results reveal that joint transmit power and trajectory optimization can enlarge the IRR, and increasing the UAV mobility and flight time length improves the UAV reward.

Development of an advanced machine learning model to predict the pH of groundwater in permeable reactive barriers (PRBs) located in acidic terrain

This paper presents a comprehensive study of various Random Forest (RF) models for predicting increments of pH in groundwater treated by permeable reactive barriers (PRBs). This study uses data collected over 16 years from two PRBs installed in acid sulfate soils. The hyperparameters of the Random Forest have been optimised using three powerful optimisation algorithms: the Whale Optimisation Algorithm, the Tunicate Swarm Algorithm, and the Jellyfish Search Optimizer. The results show that the RF-Jellyfish Search Optimiser was the most accurate model for predicting pH increment in the downgradient. This model could predict pH increments for 80 data points as well as another PRB with a different reactive material. The model was also compared with a finite difference flow model from literature. The results showed that the RF-Jellyfish Search Optimizer was more accurate with the coefficient of determination and root mean square error values of 0.954 and 7.04, compared to 0.942 and 10.27 obtained from the finite difference model. Overall, the novel intelligent technique introduced in this study effectively predicts pH increments in treated groundwater and can also be applied to different PRBs.

Joint Particle Swarm Optimization of Power and Phase Shift for IRS-Aided D2D Underlaying Cellular Systems.

Device-to-device (D2D) communication is a promising wireless communication technology which can effectively reduce the traffic load of the base station and improve the spectral efficiency. The application of intelligent reflective surfaces (IRS) in D2D communication systems can further improve the throughput, but the problem of interference suppression becomes more complex and challenging due to the introduction of new links. Therefore, how to perform effective and low-complexity optimal radio resource allocation is still a problem to be solved in IRS-assisted D2D communication systems. To this end, a low-complexity power and phase shift joint optimization algorithm based on particle swarm optimization is proposed in this paper. First, a multivariable joint optimization problem for the uplink cellular network with IRS-assisted D2D communication is established, where multiple DUEs are allowed to share a CUE's sub-channel. However, the proposed problem considering the joint optimization of power and phase shift, with the objective of maximizing the system sum rate and the constraints of the minimum user signal-to-interference-plus-noise ratio (SINR), is a non-convex non-linear model and is hard to solve. Different from the existing work, instead of decomposing this optimization problem into two sub-problems and optimizing the two variables separately, we jointly optimize them based on Particle Swarm Optimization (PSO). Then, a fitness function with a penalty term is established, and a penalty value priority update scheme is designed for discrete phase shift optimization variables and continuous power optimization variables. Finally, the performance analysis and simulation results show that the proposed algorithm is close to the iterative algorithm in terms of sum rate, but lower in power consumption. In particular, when the number of D2D users is four, the power consumption is reduced by 20%. In addition, compared with PSO and distributed PSO, the sum rate of the proposed algorithm increases by about 10.2% and 38.3%, respectively, when the number of D2D users is four.

A review of reactive power optimization algorithm in power system

The paper analyses the significance of reactive power optimization, generalizes the current situation of power system development. Various optimization algorithms were introduced in this paper such as traditional optimization algorithm, intelligence optimization algorithm, including the methods of linear programming, Newtons method, heuristic optimization algorithm, etc. This research analyzes the advantages and disadvantages of each algorithm and its application direction by comparing their outstanding performance in solving discrete variables and continuous variables. The purpose of the research is to find the optimal solution of reactive power optimization algorithm, minimize the transport network loss of power system, and improve the quality of users.