Efficient Optimization Algorithm Research Articles

Advancements in Optimal Sensor Placement for Enhanced Structural Health Monitoring: Current Insights and Future Prospects

Structural health monitoring (SHM) is critical to maintaining safe and reliable civil infrastructure, but the optimal design of an SHM sensing system, i.e., optimal sensor placement (OSP), remains a complex challenge. Based on the existing literature, this paper presents a comprehensive review of OSP strategies for SHM. It covers the key steps in OSP, from evaluation criteria to efficient optimization algorithms. The evaluation criteria are classified into six groups, while the optimization algorithms are roughly categorized into three classes. The advantages and disadvantages of each group of methods have been summarized, aiming to benefit the OSP strategy selection in future projects. Then, the real-world implementation of OSP on bridges, high-rise buildings, and other engineering structures, is presented. Based on the current progress, the challenges of OSP are recognized; its future development directions are recommended. This study equips researchers/practitioners with an integrated perspective on state-of-the-art OSP. By highlighting key developments, persistent challenges, and prospects, it is expected to bridge the gap between theory and practice.

IBJA: An improved binary DJaya algorithm for feature selection

Feature Selection (FS) is a special preprocessing step in Machine Learning (ML) that reduces the number of unwanted features in datasets to increase the accuracy of ML classifiers. A popular binary variant of the continuous Jaya algorithm is the Discrete Jaya (DJaya) algorithm. It is commonly used for addressing optimization problems with binary design variables (also known as binary decision variables). Nevertheless, DJaya tends to prematurely converge to local optimal solutions, and its performance deteriorates as the complexity of the optimization problem grows. The DJaya algorithm is improved in this article by introducing the Improved Binary DJaya Algorithm (IBJA), which is specially designed to solve the FS problem. IBJA includes three techniques in DJaya. First, it incorporates the update equation of the Harris Hawks Optimization (HHO) algorithm into the optimization loop of DJaya to enhance DJaya’s searching process and exploration abilities. Second, it uses a new Dynamic Opposition-based Learning in the final steps of the optimization loop of DJaya to boost its searching and exploring capabilities. Third, it employs binary transfer functions to calculate binary solutions from the real-valued solutions generated by HHO and DOBL. Using 15 UCI datasets, IBJA’s performance was assessed and compared against four ML classifiers and ten efficient optimization algorithms. Besides, the Friedman statistical test was employed to investigate the reliability of the experimental findings. According to the overall experimental and statistical findings, IBJA scored the highest accuracy, best objective value, and fewest chosen features for each of the 15 UCI datasets.

Optimal floating offshore wind farms for Mediterranean islands

Offshore wind will play an important role in achieving the Green Deal's goals of green transition and renewable energy generation. The European Union (EU) has launched an initiative to support a clean energy transition towards sustainable and renewable energy production in the EU islands. In fact, the Mediterranean islands obtain most of their energy supply from imported fossil fuels, making electricity costs generally very high due to their remote location. In this paper, we compare the optimal offshore wind farm layout, type and number of wind turbines for four selected sites next to Mediterranean islands. Each layout is evaluated in terms of levelised cost of energy, taking into account aerodynamic wake and transmission losses in the productivity estimation and a detailed cost model. Aerodynamic losses are modelled using a Jensen kinematic wake model and an area overlapping model to consider partial wake shadowing between wind turbines, while transmission losses are estimated as Ohmic losses of the inter-array and export transmission cables. Compared to the state-of-the-art PyWake code, the in-house MATLAB wake model demonstrated higher computational efficiency and is integrated into an efficient optimisation algorithm consisting of several iterations of the Interior-point optimisation algorithm. Results show that offshore wind turbines can provide renewable energy at a competitive price, especially at the most energetic site with an LCOE about 80 €/MWh. The normalised optimal LCOE and capacity factor as a function of the number of wind turbines are not significantly influenced by the chosen location and reach a maximum difference of 2–3 %, showing that they mainly depend on the type of wind turbine. The lowest optimal LCOE depending on the number of wind turbines is reduced by about 10 % and 25 % by the higher rated wind turbine.

Computational Design of Flexible Planar Microstructures

Mechanical metamaterials enable customizing the elastic properties of physical objects by altering their fine-scale structure. A broad gamut of effective material properties can be produced even from a single fabrication material by optimizing the geometry of a periodic microstructure tiling. Past work has extensively studied the capabilities of microstructures in the small-displacement regime, where periodic homogenization of linear elasticity yields computationally efficient optimal design algorithms. However, many applications involve flexible structures undergoing large deformations for which the accuracy of linear elasticity rapidly deteriorates due to geometric nonlinearities. Design of microstructures at finite strains involves a massive increase in computation and is much less explored; no computational tool yet exists to design metamaterials emulating target hyperelastic laws over finite regions of strain space. We make an initial step in this direction, developing algorithms to accelerate homogenization and metamaterial design for nonlinear elasticity and building a complete framework for the optimal design of planar metamaterials. Our nonlinear homogenization method works by efficiently constructing an accurate interpolant of a microstructure's deformation over a finite space of macroscopic strains likely to be endured by the metamaterial. From this interpolant, the homogenized energy density, stress, and tangent elasticity tensor describing the microstructure's effective properties can be inexpensively computed at any strain. Our design tool then fits the effective material properties to a target constitutive law over a region of strain space using a parametric shape optimization approach, producing a directly manufacturable geometry. We systematically test our framework by designing a catalog of materials fitting isotropic Hooke's laws as closely as possible. We demonstrate significantly improved accuracy over traditional linear metamaterial design techniques by fabricating and testing physical prototypes.

Two-step affinity matrix learning for multi-view subspace clustering

Multi-view subspace clustering aims to learn an appropriate affinity matrix to investigate the relationship between data. However, the learned affinity matrix always has limited representation ability and unclear intrinsic structure. To address the problem, we propose a novel two-step learning strategy to enhance the quality of the affinity matrix. First, we design a generalized framework to calculate the affinity matrix for each view. Unlike most subspace clustering methods that focus on constructing the coefficient matrices, the proposed framework combines the subspace-based and graph-based models to simultaneously learn the coefficient and similarity matrices under a consistency constraint. Thus, the data structure information and multiple views correlation can be captured and propagated between these matrices, which is conducive to generating an informative affinity matrix. Further, we present a post-processing technology called unified good neighbors learning to make the obtained affinity matrix have good sparsity and connectivity. Specifically, we find the good neighbors of each sample by all the views and reassign their coefficients in the final affinity matrix to reflect the essential correlation between samples. Finally, we develop an efficient optimization algorithm and provide the theoretical analyses for the proposed problem. Extensive experiments conducted on six popular multi-view datasets demonstrate the effectiveness of our method. The source code is available at https://github.com/taozhang1223/TAML.

Optimal configuration of dynamic wireless charging facilities considering electric vehicle battery capacity

Dynamic wireless charging facilities deployed on the road network offer an effective charging method to alleviate the electric vehicle users’ range anxiety and thus facilitate the promotion of electric vehicles. The main benefit of wireless charging is that it extends EV’s driving range by enabling EV en-route recharging, which consequently reduces the required battery size for EVs. Basically, given densely distributed wireless charging lanes on the road network, smaller and less expensive batteries would be able to meet the travel demand. From the societal point of view, the reduction of battery size/capacity is capable of justifying more construction of wireless charging lanes. While previous research works on optimal deployment of wireless charging facilities ignored this benefit, this study aims to explicitly consider the tradeoff between the costs of building recharging infrastructure and manufacturing batteries from the societal point of view in the optimal configuration of dynamic wireless charging facilities. The problem is formulated into a bi-level programming model, wherein the upper-level model seeks to minimize the total social cost, and the lower-level model captures EV drivers’ choices in terms of battery size, travel route, and driving and charging behavior. To handle the problem, we first relax the path cost constraint and apply the linearization techniques to reformulate the problem into a mixed-integer linear programming, from which a lower bound of the problem can be obtained. An efficient surrogate optimization algorithm is then developed to solve the problem.

An efficient reconfigurable optimal source detection and beam allocation algorithm for signal subspace factorization

<span lang="EN-US">Now a <span>days, huge amount of data is communicated through channels in wireless network. It requires an efficient parallel operation for the optimal utilization of frequency, time allocation and coding model for signal subspace factorization in smart antenna. In view of this requirement, an efficient reconfigurable optimal source detection and beam allocation algorithm (RoSDBA) is proposed. The proposed algorithm is able to allocate desired signal to the user space to reduce the noise and also for efficient allocation of subspace to remove disturbance in all directions. The proposed method efficiently utilizes the antenna array elements by accurate identification and allocation of antenna array elements such as individual radiators, radiation beam, signal strength, and disturbance factor. With respect to simulation analysis, the proposed method shows better performance for the resolution, radiation beam allocations, identification bias, distribution factor and time taken for the detection of various</span> array arrangements and source numbers.</span>

Energy-Based Continuous Inverse Optimal Control.

The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study this fundamental problem in the framework of energy-based model (EBM), where the observed expert trajectories are assumed to be random samples from a probability density function defined as the exponential of the negative cost function up to a normalizing constant. The parameters of the cost function are learned by maximum likelihood via an "analysis by synthesis" scheme, which iterates: 1) synthesis step: sample the synthesized trajectories from the current probability density using the Langevin dynamics via backpropagation through time and 2) analysis step: update the model parameters based on the statistical difference between the synthesized trajectories and the observed trajectories. Given the fact that an efficient optimization algorithm is usually available for an optimal control problem, we also consider a convenient approximation of the above learning method, where we replace the sampling in the synthesis step by optimization. Moreover, to make the sampling or optimization more efficient, we propose to train the EBM simultaneously with a top-down trajectory generator via cooperative learning, where the trajectory generator is used to fast initialize the synthesis step of the EBM. We demonstrate the proposed methods on autonomous driving tasks and show that they can learn suitable cost functions for optimal control.

Multi-View Discrete Clustering: A Concise Model.

In most existing graph-based multi-view clustering methods, the eigen-decomposition of the graph Laplacian matrix followed by a post-processing step is a standard configuration to obtain the target discrete cluster indicator matrix. However, we can naturally realize that the results obtained by the two-stage process will deviate from that obtained by directly solving the primal clustering problem. In addition, it is essential to properly integrate the information from different views for the enhancement of the performance of multi-view clustering. To this end, we propose a concise model referred to as Multi-view Discrete Clustering (MDC), aiming at directly solving the primal problem of multi-view graph clustering. We automatically weigh the view-specific similarity matrix, and the discrete indicator matrix is directly obtained by performing clustering on the aggregated similarity matrix without any post-processing to best serve graph clustering. More importantly, our model does not introduce an additive, nor does it has any hyper-parameters to be tuned. An efficient optimization algorithm is designed to solve the resultant objective problem. Extensive experimental results on both synthetic and real benchmark datasets verify the superiority of the proposed model.

An efficient many-objective optimization algorithm for computation offloading in heterogeneous vehicular edge computing network

Vehicular Edge Computing (VEC) provides flexible distributed computing paradigm for vehicular network through computation offloading. With the advent of a growing of modern vehicle applications, the challenge for VEC network to fulfill the expansive demands from various suppliers, users and environments is increasingly prominent. This paper formulates a many-objective computation offloading problem in heterogeneous VEC network aiming to fulfill the diversified optimization requirements, including minimizing the task completion time, energy consumption and resource costs as well as load balance. A many-objective optimization algorithm named MaOITGO-CO is proposed to solve the formulated problem based on ITGO (Invasive Tumor Growth Optimization) by simulating the growth patterns of tumor cells. Specifically, considering the characteristics of computation offloading in VEC scenarios such as mobility, real-time requirements and the variety of tasks and resources, four types of tumor cells are equipped with different search strategies to enhance the search effectiveness and efficiency. The simulation results show that the proposed approach can provide high quality Pareto solutions for computation offloading problem, which outperforms other widely used algorithms in terms of convergency and diversity. Furthermore, the results of scalability experiments validate the availability of MaOITGO-CO when the problem is extended to different scales of both tasks and computing resources.

The discrete q-Gaussian distribution [formula omitted]: Properties and parameters estimation

We introduce a new discrete distribution, called the centered reduced discrete q-Gaussian Nq(0,1). This distribution connects classical Gaussian, discrete Uniform, and quantum q-Gaussian distributions. In this paper, we extend Nq(0,1) to Nq(μ,σ2), overcoming a limitation of some q-distributions like Diaz and Pariguan's q-Gaussian. Notably, Nq(0,1) has distinct shapes and parameters from the classical counterpart, providing additional flexible modeling approach. Results show the suggested discrete q-Gaussian as a useful alternative to the classical Gaussian for modeling data with hollow values or heavy-tailed tails. We explore properties of Nq(μ,σ2) and apply moments and maximum likelihood methods to estimate its parameters. Our analysis yields a key result on the concavity of the likelihood function, enabling efficient optimization algorithms for parameters estimation. Furthermore, we investigate a finite mixture of discrete q-Gaussians and apply the EM algorithm for parameters estimation. Finally, we conduct a simulation study to evaluate the model and estimation methods.

Predicting slope safety using an optimized machine learning model

The hazards and consequences of slope collapse can be reduced by obtaining a reliable and accurate prediction of slope safety, hence, developing effective tools for foreseeing their occurrence is crucial. This research aims to develop a state-of-the-art hybrid machine learning approach to estimate the factor of safety (FOS) of earth slopes as precisely as possible. The current research’s contribution to the body of knowledge is multifold. In the first step, a powerful optimization approach based on the artificial electric field algorithm (AEFA), namely the global-best artificial electric field algorithm (GBAEF), is developed and verified using a number of benchmark functions. The aim of the following step is to utilize the machine learning technique of support vector regression (SVR) to develop a predictive model to estimate the slope’s safety factor (FOS). Finally, the proposed GBAEF is employed to enhance the performance of the SVR model by appropriately adjusting the hyper-parameters of the SVR model. The model implements 153 data sets, including six input parameters and one output parameter (FOS) collected from the literature. The outcomes show that implementing efficient optimization algorithms to adjust the hyper-parameters of the SVR model can greatly enhance prediction accuracy. A case study of earth slope from Chamoli District, Uttarakhand is used to compare the proposed hybrid model to traditional slope stability techniques. According to experimental findings, the new hybrid AI model has improved FOS prediction accuracy by about 7% when compared to other forecasting models. The outcomes also show that the SVR optimized with GBAEF performs wonderfully in the disciplines of training and testing, with a maximum R2 of 0.9633 and 0.9242, respectively, which depicts the significant connection between observed and anticipated FOS.

Energy Consumption Minimization in Unmanned Aerial Vehicle-Enabled Secure Wireless Sensor Networks

In wireless sensor networks (WSNs), unmanned aerial vehicles (UAVs) are considered an effective data collection tool. In this paper, we investigate the energy-efficient data collection problem in a UAV-enabled secure WSN without knowing the instantaneous channel state information of the eavesdropper (Eve). Specifically, the UAV collected the information from all the wireless sensors at the scheduled time and forward it to the fusion center while Eve tries to eavesdrop on this confidential information from the UAV. To surmount this intractable and convoluted mixed-integer non-convex problem, we propose an efficient iterative optimization algorithm using the block coordinate descent (BCD) method to minimize the maximum energy consumption of the ground sensor nodes (GSNs) under the constraints of secrecy outage probability (SOP), connection outage probability (COP), minimum secure data, information causality, and UAV trajectory. Numerical results demonstrate the superiority of the algorithm we proposed in energy consumption and secrecy rate compared with other schemes.

Two-stage decomposition and temporal fusion transformers for interpretable wind speed forecasting

Contemporary wind speed prediction research methodologies often employ two-stage decomposition preprocessing techniques to leverage the temporal correlation of wind speed. However, they frequently neglect to investigate the interpretability within the wind speed prediction model. To this end, a novel and interpretable hybrid forecasting model that combines two-layer decomposition, adaptive differential evolution with optional external archive (JADE), and temporal fusion transformers (TFT) is proposed. Primarily, on the basis of the linear-nonlinear decomposition criterion, a set of subcomponents is obtained using two-stage decomposition to fully extract the wind speed series prediction information for high-fluctuation and multi-resolution modes. Utilizing the JADE algorithm for intelligent and efficient optimization of parameter combinations in the TFT model guarantees the stability and reliability of the prediction model. Later, the obtained two-layer decomposition subseries are used as historical variables, and the meteorological and temporal data are entered into the TFT model as future known inputs. Empirical studies show that the proposed model demonstrates remarkable suitability and effectiveness in short-term wind speed forecasting. The utilization of the interpretable model has catalyzed significant advancements in wind speed prediction, while the analysis of its interpretable results empowers managers in formulating effective policies.

Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand response

Industrial loads are usually energy intensive and inefficient. The optimization of energy efficiency management in steel plants is still in the early stage of development. Considering the topology of power grid, it is an urgent problem to improve the operation economy and load side energy efficiency of steel plants. In this paper, a two-level collaborative optimization method is proposed, which takes into account the dynamic reconstruction cost, transmission loss cost, energy cost and demand response benefit. The upper level objective is the optimization of topology in the grid structure to optimize the power loss and dynamic reconstruction costs of the grid. The lower level is the energy cost considering demand response, real time price and dynamic demand response price. Firstly, the mathematical models of stable load, impact load and the steel production line load are built. The key parameters are identified by the Back Propagation neural network algorithm according to the actual production data. Secondly, considering the constraints of grid structure and load operation capacity, the impact of dynamic grid loss and real-time dynamic electricity price on the energy efficiency of the whole grid are analyzed in depth. The optimal operation model considering the dynamic reconfiguration and grid tramission loss of distribution network is built. Taking a steel plant park in Northeast China as an example, it is proved that the optimization model can improve energy efficiency on the load side by optimizing energy consumption and demand response participation time on load side. The energy cost is reduced by 17.77% on the load side, the network loss is reduced by 1.8%, and the operating cost of the power grid is reduced by 26.2%, which has a positive effect on improving energy utilization efficiency, reducing distribution network loss, and improving overall economic efficiency.

Optimization based on performance of lungs in body: Lungs performance-based optimization (LPO)

The development of efficient optimization algorithms is crucial across various scientific disciplines. As the complexity and diversity of optimization problems continue to grow, researchers seek faster and stronger algorithms capable of optimizing a wide range of functions. This paper introduces Lung performance-based optimization (LPO), a novel and efficient algorithm inspired by the regular and intelligent performance of lungs in the human body. LPO draws inspiration from the intricate mechanisms and adaptability of the respiratory system. The lungs exhibit remarkable efficiency in oxygen exchange, demonstrating a high level of optimization in their function. The forced oscillation technique measures air pressure and airflow rate to evaluate the respiratory system as an electrical impedance. The impedance curves have two distinct components, respiratory resistance (ZR) and respiratory reactance (ZX), which can be analyzed clinically and from an engineering perspective to gain insights into the respiratory system's workings. LPO aims to provide an innovative approach to solving complex optimization problems by emulating and harnessing this natural efficiency. To evaluate the effectiveness of LPO, experiments were conducted using the unconstrained optimization functions CEC2005 and CEC2014, as well as engineering design optimization problems. These problems were compared against numerous contemporary algorithms proposed in the literature. The results demonstrate that LPO excels in handling these optimization problems and exhibits the potential to tackle a wide range of modern optimization challenges. The findings highlight the power and effectiveness of LPO as a new optimization algorithm. With its inspiration rooted in the sophisticated performance of the lungs, LPO offers a unique perspective in the optimization landscape. Its ability to handle diverse optimization problems and its potential for application in various domains make LPO a promising algorithm for future research and practical implementation. Note that the source code of the LPO is publicly available at https://www.optim-app.com/projects/lpo.

An efficient adaptive-mutated Coati optimization algorithm for feature selection and global optimization

The feature selection (FS) problem has occupied a great interest of scientists lately since the highly dimensional datasets might have many redundant and irrelevant features. FS aims to eliminate such features and select the most important ones that affect classification performance. Metaheuristic algorithms are the best choice to solve this combinatorial problem. Recent researchers invented and adapted new algorithms, hybridized many algorithms, or enhanced existing ones by adding some operators to solve the FS problem. In our paper, we added some operators to the Coati optimization algorithm (CoatiOA). The first operator is the adaptive s-best mutation operator to enhance the balance between exploration and exploitation. The second operator is the directional mutation rule that opens the way to discover the search space thoroughly. The final enhancement is controlling the search direction toward the global best. We tested the proposed mCoatiOA algorithm in solving) in solving challenging problems from the CEC'20 test suite. mCoatiOA performance was compared with Dandelion Optimizer (DO), African vultures optimization algorithm (AVOA), Artificial gorilla troops optimizer (GTO), whale optimization algorithm (WOA), Fick's Law Algorithm (FLA), Particle swarm optimization (PSO), Harris hawks optimization (HHO), and Tunicate swarm algorithm (TSA). According to the average fitness, it can be observed that the proposed method, mCoatiOA, performs better than the other optimization algorithms on 8 test functions. It has lower average standard deviation values compared to the competitive algorithms. Wilcoxon test showed that the results obtained by mCoatiOA are significantly different from those of the other rival algorithms. mCoatiOA has been tested as a feature selection algorithm. Fifteen benchmark datasets of various types were collected from the UCI machine-learning repository. Different evaluation criteria are used to determine the effectiveness of the proposed method. The proposed mCoatiOA achieved better results in comparison with other published methods. It achieved the mean best results on 75% of the datasets.

An efficient optimization algorithm for nonlinear 2D fractional optimal control problems

An efficient optimization algorithm for nonlinear 2D fractional optimal control problems

Concentration Prediction of Multi-component Gases Based on Improved Sparrow Search Algorithm

The electronic nose consists of a sensor array and software algorithms, which can be used for gas identification and concentration prediction. The least-squares support vector machine (LSSVM) is often used for gas concentration prediction. However, the effectiveness of its performance is largely influenced by the chosen parameters. Hence, it is essential to integrate efficient optimization algorithms to enhance the predictive capabilities of LSSVM. This work introduces an improved sparrow search algorithm (ISSA) to enhance the precision of gas concentration prediction by LSSVM. The ISSA approach is evaluated against the particle swarm optimization algorithm (PSO) and the sparrow search algorithm (SSA). Response data of different concentrations of CH4, CO, H2 and their binary gases mixture have been measured using an electronic nose composed of six gas sensors. After processing feature extraction and principal component analysis (PCA) on the original data, it is used as a training and test dataset for prediction models. The results demonstrate that ISSA can significantly enhance the precision of the LSSVM model for gas concentration prediction.

Energy efficiency optimization algorithm based on RIS and D2D for multi-user MIMO system

Reconfigurable Intelligent Surface (RIS) is composed of reconfigurable passive components, which can realize on-demand customization of wireless communication environment. Device-to-Device (D2D) communication is one of the essential technologies to improve the capacity and spectrum efficiency of network system. In this paper, we propose an energy efficiency optimization algorithm based on RIS and D2D assisted multi-user MIMO system. The purpose is to maximize the energy efficiency of the system by jointly optimizing subcarrier allocation, power allocation and passive beamforming at RIS. We first formulate the energy efficiency maximization problem with the constraints of different user rate, power, subcarrier allocation and phase. Since it is a non-convex problem, we use an alternating optimization (AO) algorithm to decouple it into three sub-problems. The solutions of these three sub-problems are solved by subcarrier allocation based on channel strength, SCA algorithm, Dinkelbach algorithm and Riemannian gradient algorithm respectively. Simulation results show that the proposed scheme can significantly improve the energy efficiency.