Efficient Optimization Algorithm Research Articles

Learning ordinal constraint binary codes for fast similarity search

Similarity search with hashing has become one of the fundamental research topics in computer vision and multimedia. The current researches on semantic-preserving hashing mainly focus on exploring the semantic similarities between pointwise or pairwise samples in the visual space to generate discriminative hash codes. However, such learning schemes fail to explore the intrinsic latent features embedded in the high-dimensional feature space and they are difficult to capture the underlying topological structure of data, yielding low-quality hash codes for image retrieval. In this paper, we propose an ordinal-preserving latent graph hashing (OLGH) method, which derives the objective hash codes from the latent space and preserves the high-order locally topological structure of data into the learned hash codes. Specifically, we conceive a triplet constrained topology-preserving loss to uncover the ordinal-inferred local features in binary representation learning. By virtue of this, the learning system can implicitly capture the high-order similarities among samples during the feature learning process. Moreover, the well-designed latent subspace learning is built to acquire the noise-free latent features based on the sparse constrained supervised learning. As such, the latent under-explored characteristics of data are fully employed in subspace construction. Furthermore, the latent ordinal graph hashing is formulated by jointly exploiting latent space construction and ordinal graph learning. An efficient optimization algorithm is developed to solve the resulting problem to achieve the optimal solution. Extensive experiments conducted on diverse datasets show the effectiveness and superiority of the proposed method when compared to some advanced learning to hash algorithms for fast image retrieval. The source codes of this paper are available at https://github.com/DarrenZZhang/OLGH .

A novel nomad migration-inspired algorithm for global optimization

Nature-inspired computing (NIC) has been widely studied for many optimization scenarios. However, miscellaneous solution space of real-world problem causes it is challenging to guarantee the global optimum. Besides, cumbersome structure and complex parameters setting-up make the existed algorithms hard for most users who are not specializing in NIC, to understand and use. To alleviate these limitations, this paper devises a succinct and efficient optimization algorithm called Nomad Algorithm (NA). It is inspired by the migratory behavior of nomadic tribes on the prairie. Extensive experiments are implemented with respects to accuracy, rate, stability, and cost of optimization. Mathematical proof is given to guarantee the global convergence, and the nonparametric tests are conducted to confirm the significance of experiment results. The statistical results of optimization accuracy denote NA outperforms its rivals for most cases (23/28) by orders of magnitude significantly. It is considered as a promising optimizer with excellent efficiency and adaptability.

UAV-assisted cooperative offloading energy efficiency system for mobile edge computing

Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure. Mitigating this has greatly developed the application and integration of UAV and Mobile Edge Computing (MEC) to the Internet of Things (IoT). However, problems such as multi-user and huge data flow in large areas, which contradict the reality that a single UAV is constrained by limited computing power, still exist. Due to allowing UAV collaboration to accomplish complex tasks, cooperative task offloading between multiple UAVs must meet the interdependence of tasks and realize parallel processing, which reduces the computing power consumption and endurance pressure of terminals. Considering the computing requirements of the user terminal, delay constraint of a computing task, energy constraint, and safe distance of UAV, we constructed a UAV-Assisted cooperative offloading energy efficiency system for mobile edge computing to minimize user terminal energy consumption. However, the resulting optimization problem is originally nonconvex and thus, difficult to solve optimally. To tackle this problem, we developed an energy efficiency optimization algorithm using Block Coordinate Descent (BCD) that decomposes the problem into three convex subproblems. Furthermore, we jointly optimized the number of local computing tasks, number of computing offloaded tasks, trajectories of UAV, and offloading matching relationship between multi-UAVs and multiuser terminals. Simulation results show that the proposed approach is suitable for different channel conditions and significantly saves the user terminal energy consumption compared with other benchmark schemes.

An Efficient Electric Charged Particles Optimization Algorithm for Numerical Optimization and Optimal Estimation of Photovoltaic Models

The electric charged particles optimization (ECPO) technique is inspired by the interaction (exerted forces) between electrically charged particles. A developed version of ECPO called MECPO is suggested in this article to enhance the capability of searching and balancing the exploitation and exploration phases of the conventional ECPO. To let the search agent jumps out from the local optimum and avoid stagnation in the local optimum in the proposed MECPO, three different strategies in the interaction between ECPs are modified in conjunction with the conventional ECPO. Therefore, the convergence rate is enhanced and reaches rapidly to the optimal solution. To evaluate the effectiveness of the MECPO, it is executed on the test functions of the CEC’17. Furthermore, the MECPO technique is suggested to estimate the parameters of different photovoltaic models, such as the single-diode model (SDM), the double-diode model (DDM), and the triple-diode model (TDM). The simulation results illustrate the validation and effectiveness of MECPO in extracting parameters from photovoltaic models.

Particle Swarm Optimization Algorithm Based on Information Sharing in Industry 4.0

Intelligent manufacturing is an important part of Industry 4.0; artificial intelligence technology is a necessary means to realize intelligent manufacturing. This requires the exploration of pattern recognition, computer vision, intelligent optimization, and other related technologies. Particle swarm optimization (PSO) algorithm is an optimization algorithm inspired by the foraging behavior of birds. PSO was an intelligent technology and an efficient optimization algorithm verified by a lot of research and experiments. In this paper, the traditional PSO algorithm is compared with genetic algorithms (GA) to illustrate the performance of the traditional PSO algorithm. By analyzing the advantages and disadvantages of the traditional PSO algorithm, the traditional PSO algorithm is improved through introducing into the sharing information mechanism and the competition strategy, called information sharing based PSO (IPSO). The novel algorithm IPSO was the rapid convergence speed similar to the traditional PSO and enhanced the global search capability. Our experimental results show that IPSO has better performance than the traditional PSO and the GA algorithm on benchmark functions, especially for difficult functions.

A comprehensive analysis of grid-based wind turbine layout using an efficient binary invasive weed optimization algorithm with levy flight

Wind energy has attracted great attention in recent years due to the increasing demand for alternative energy sources. Gathering the maximum amount of energy from wind energy is directly related to the layout of wind turbines in wind farm. This study focuses on grid-based wind turbine layout problem in an area of 2 km × 2 km. In order to solve this problem, 9 novel grids of 11 × 11, 12 × 12, … and 19 × 19 are proposed in this paper in addition to 10 × 10 and 20 × 20 turbine grids which have already used in the literature. A new repair operator is recommended, taking into account the distance constraint between two adjacent cells in turbine layout problem, and thus the obtained solutions are made feasible. Furthermore, a levy flight-based IWO (LFIWO) algorithm is developed to optimize the layout of the turbines in wind farm. The basic IWO algorithm and the proposed LFIWO algorithm are compared on 11 different turbine layouts. Experimental studies are carried out for both algorithms under equal conditions with the help of 10 different binary versions. According to the comparisons performed using 11 different grids, LFIWO demonstrates a much superior success than the IWO algorithm. In addition, the proposed 19 × 19 grid reveals the best success among the other grids. When compared to the other studies in the literature, it is seen that LFIWO surpasses the other algorithms in terms of solution quality. As a result, it can be clearly stated that the proposed binary version of LFIWO is a competitive and effective binary algorithm.

IRS-assisted covert communication with eavesdropper’s channel and noise information uncertainties

This paper considers an intelligent reflecting surface (IRS)-assisted covert communication system, where an IRS is deployed for the covert transmission from a legitimate transmitter (Alice) to a legitimate receiver (Bob) with the presence of an eavesdropper (Willie). Since Willie is not a legitimate communication node and cannot control the IRS’s reflection, it has uncertainty about the channel information of the Alice-IRS and IRS-Willie links. Meanwhile, Willie also has uncertainty about its noise power. Having such channel and noise information uncertainties, Willie finds an optimal power detection threshold to minimize its false detection probability. Under this system setup, we investigate maximizing the covert rate of the legitimate communication from Alice to Bob, by jointly optimizing Alice’s transmit power and the IRS’s reflecting phase shifts. We have proposed two efficient joint optimization algorithms for the continuous and discrete IRS reflecting phase shift cases, respectively. The proposed algorithm for the previous case is based on the semidefinite relaxation technique, and that for the latter case is based on the alternating optimization technique. Simulation results have demonstrated the superiority and necessity of jointly optimizing the transmit power and IRS reflecting phase shifts on improving the covert rate performance, as compared to other benchmark schemes.

An effective multi-objective optimization approach for groundwater remediation considering the coexisting uncertainties of aquifer parameters

An accurate groundwater simulation model and an efficient optimization algorithm are critical for building a reliable remediation scheme using simulation–optimization method. The hydraulic conductivity (K) and the porosity (n) of aquifers are two main controlling parameters for groundwater flow and contaminant transport, whose uncertainties are high and often coexists. Such coexisting uncertainties have not been fully considered in previous studies on the optimization of groundwater remediation system. In this work, a new multi-objective optimization algorithm, namely the probabilistic multi-objective fast harmony search and genetic algorithm (PMOFHS-GA), is developed, capable of finding Pareto optimal solutions with high reliability. The algorithm is applied on a case study in designing a groundwater remediation system based on pumping and treating (PAT) technology considering different scenarios with varying K and n realizations. Furthermore, a novel decision-making strategy based on the reliability of different objective functions is proposed to help designers determining the final best remediation scheme from Pareto optimal solutions. Results show that the coexisting uncertainties of K and n will cause significant impacts on final optimization results. In addition, if there is a correlation between uncertainties of K and n, the reliability and variability of optimization results will be different from that under no correlation condition. Thus, accurate information of aquifer parameters is critical for the final optimization results. Furthermore, the proposed decision-making strategy can help decision-makers to identify the final contaminant remediation scheme more reasonably. The PMOFHS-GA and decision-making strategy developed in this study are promising methods which can help decision-makers to design a groundwater contaminant remediation system effectively.

Supervised Multivariate Learning with Simultaneous Feature Auto-Grouping and Dimension Reduction

Abstract Modern high-dimensional methods often adopt the ‘bet on sparsity’ principle, while in supervised multivariate learning statisticians may face ‘dense’ problems with a large number of nonzero coefficients. This paper proposes a novel clustered reduced-rank learning (CRL) framework that imposes two joint matrix regularizations to automatically group the features in constructing predictive factors. CRL is more interpretable than low-rank modelling and relaxes the stringent sparsity assumption in variable selection. In this paper, new information-theoretical limits are presented to reveal the intrinsic cost of seeking for clusters, as well as the blessing from dimensionality in multivariate learning. Moreover, an efficient optimization algorithm is developed, which performs subspace learning and clustering with guaranteed convergence. The obtained fixed-point estimators, although not necessarily globally optimal, enjoy the desired statistical accuracy beyond the standard likelihood setup under some regularity conditions. Moreover, a new kind of information criterion, as well as its scale-free form, is proposed for cluster and rank selection, and has a rigorous theoretical support without assuming an infinite sample size. Extensive simulations and real-data experiments demonstrate the statistical accuracy and interpretability of the proposed method.

Adaptive Dimensional Learning with a Tolerance Framework for the Differential Evolution Algorithm

The Differential Evolution (DE) algorithm, which is an efficient optimization algorithm, has been used to solve various optimization problems. In this paper, adaptive dimensional learning with a tolerance framework for DE is proposed. The population is divided into an elite subpopulation, an ordinary subpopulation, and an inferior subpopulation according to the fitness values. The ordinary and elite subpopulations are used to maintain the current evolution state and to guide the evolution direction of the population, respectively. The inferior subpopulation learns from the elite subpopulation through the dimensional learning strategy. If the global optimum is not improved in a specified number of iterations, a tolerance mechanism is applied. Under the tolerance mechanism, the inferior and elite subpopulations implement the restart strategy and the reverse dimensional learning strategy, respectively. In addition, the individual status and algorithm status are used to adaptively adjust the control parameters. To evaluate the performance of the proposed algorithm, six state-of-the-art DE algorithm variants are compared on the benchmark functions. The results of the simulation show that the proposed algorithm outperforms other variant algorithms regarding function convergence rate and solution accuracy.

Robust Tensor Recovery in Impulsive Noise Based on Correntropy and Hybrid Tensor Sparsity

Robust tensor recovery aims to reconstruct a multidimensional tensor from its observations contaminated by noise. In this brief, a new formulation based on correntropy and hybrid tensor sparsity measure is proposed for robust tensor recovery in the environment of impulsive noise. The robust correntropy measure has shown satisfactory robustness against large outliers in various scenarios recently. Meanwhile, in this formulation, the hybrid tensor sparsity measure combining the advantages of Tucker and CP tensor rank can better characterize the tensor sparsity. To solve the proposed formulation effectively, an efficient large-scale optimization algorithm is derived based on the framework of alternating direction method of multipliers (ADMM) and half-quadratic optimization technique. The results of multispectral image (MSI) data recovery indicate that the proposed algorithm can achieve robust tensor recovery in the environment of impulsive noise.

Efficient Robust Production Optimization with Reduced Sampling

Summary Model-based robust production optimization is performed by propagating the uncertainty in the reservoir model description through the nonlinear fluid flow dynamics to predict the stochastic production response and the objective function of interest. Propagation of uncertainty is often accomplished through Monte Carlo simulation by sampling from the distribution of uncertain input parameters and computing the corresponding value of the objective function after performing reservoir simulation. The computational burden of performing hundreds of reservoir simulation runs to characterize the distribution of the objective function (or its point statistics) over the sampled input parameters makes the process prohibitive for large-scale deployment. To reduce the computational load of robust production optimization under geologic uncertainty, we propose an efficient stochastic production optimization algorithm based on a reduced sampling strategy. In this approach, at each iteration of the optimization procedure, instead of evaluating the objective function and its gradient over a large number of fixed realizations, we approximate them by using a small subset of model realizations that are selected randomly. This approximation leads to solving a series of “small” subproblems that require significantly fewer reservoir simulation runs, thereby reducing the computational complexity of the robust optimization workflow. Because the samples are selected randomly, over several iterations of the optimization process, a large number of samples are included. For a fixed set of realization, the proposed approach results in each sample being used multiple times during the optimization process. The proposed method is applied to several numerical examples, including a field-scale reservoir model, to show its computational efficiency over traditional robust production optimization under geologic uncertainty.

Reducing MRI-guided radiotherapy planning and delivery times via efficient leaf sequencing and segment shape optimization algorithms

Objective. Extended treatment session times are an operational limitation in magnetic resonance imaging guided adaptive radiotherapy (MRIgRT). In this study a novel leaf sequencing algorithm called optimal fluence levels (OFL) and an optimization algorithm called pseudo gradient descent (PGD) are evaluated with respect to plan quality, beam complexity, and the ability to reduce treatment session times on the Elekta Unity MRIgRT system. Approach. Ten total patients were evaluated on this Institutional Review Board approved study: three with prostate cancer, three with oligometastases, two with pancreatic cancer, and two with liver cancer. Plans were generated using the clinical Monaco Hyperion optimizer and leaf sequencer and then re-optimized using OFL and PGD (OFL + PGD) while holding all IMRT constraints and planning parameters constant. All plans were normalized to ensure 95% of the PTV received the prescription dose. A paired t-test was used to evaluate statistical significance. Main Results. Plan quality in terms of dosimetric OAR sparing was found to be equivalent between the OFL + PGD and conventional Monaco Hyperion optimizer plans. The OFL + PGD plans had a reduction in optimization time of 51.4% ± 5.0% (p = 0.002) and reduction in treatment delivery time of 10.6% ± 7.5% (p = 0.005). OFL + PGD generated plans had on average 13.2% ± 12.6% fewer multi-leaf collimator (MLC) segments (p = 0.009) and 0.1 ± 0.1 lower plan averaged beam modulation (PM) (p = 0.004) relative to the Monaco Hyperion plans. Significance. The OFL + PGD algorithms more quickly generate Unity treatment plans that are faster to deliver than with the conventional approach and without compromising dosimetric plan quality. This is likely due to a delivery complexity reduction enabled by OFL + PGD relative to the Monaco Hyperion plans.

Optimization Design of Planar Circle Coil for Limited-Size Wireless Power Transfer System

Power Transfer Efficiency (PTE) and Transferred Power (TP) are two crucial indicators of the wireless power transfer (WPT) system. However, in most compensatory topologies, the ideal coils design parameters of PTE and TP are inconsistent, implying that optimizing for one indicator would make another indicator less effective. As a result, in this article, the Thompson sampling efficient multi-objective optimization (TSEMO) algorithm is used to simultaneously optimize PTE and TP in the S-S compensation topology. Through the co-calculation of MATLAB and COMSOL, the coils can be optimized in the environment of magnetic material (ferrite) and conductive material (aluminum). Coils larger than the underwater vehicle’s size are deleted during the data interaction between MATLAB and COMSOL to guarantee that the optimal PTE and TP can be attained within the constrained area. Due to the open standard external software packages between MATLAB and COMSOL, the entire optimization process may be automated. The results show that for WPT systems with restricted input voltage, such as the battery, this design strategy can achieve better TP at the expense of lower PTE, which is highly beneficial in improving the system’s overall performance.

Reliability-Based Design Optimization of Structures Considering Uncertainties of Earthquakes Based on Efficient Gaussian Process Regression Metamodeling

The complexity of earthquakes and the nonlinearity of structures tend to increase the calculation cost of reliability-based design optimization (RBDO). To reduce computational burden and to effectively consider the uncertainties of ground motions and structural parameters, an efficient RBDO method for structures under stochastic earthquakes based on adaptive Gaussian process regression (GPR) metamodeling is proposed in this study. In this method, the uncertainties of ground motions are described by the record-to-record variation and the randomness of intensity measure (IM). A GPR model is constructed to obtain the approximations of the engineering demand parameter (EDP), and an active learning (AL) strategy is presented to adaptively update the design of experiments (DoE) of this metamodel. Based on the reliability of design variables calculated by Monte Carlo simulation (MCS), an optimal solution can be obtained by an efficient global optimization (EGO) algorithm. To validate the effectiveness and efficiency of the developed method, it is applied to the optimization problems of a steel frame and a reinforced concrete frame and compared with the existing methods. The results show that this method can provide accurate reliability information for seismic design and can deal with the problems of minimizing costs under the probabilistic constraint and problems of improving the seismic reliability under limited costs.

Bi-subgroup optimization algorithm for parameter estimation of a PEMFC model

Proton exchange membrane fuel cell (PEMFC) has the advantages of cleanliness, environmental protection, and high stability, and is widely used in the fields of astronautics, military, and so on. How to extract the PEMFC model parameters more precisely is a key problem, which can be transformed into a highly nonlinear optimization problem. Therefore, an efficient optimization algorithm is needed. Swarm intelligence algorithms are used for solving various complex optimization problems. In this paper, a bi-subgroup optimization algorithm (BSOA) is proposed. The BSOA divides the population into two different sub-populations, one of which is to develop more optimal solutions and the other is to mine more optimal solutions within the population. Hence, it can overcomes the drawback that a single population search tends to fall into local optimal solutions and enhances the diversity of the population. By constructing and analyzing the Markov model, it is proved that the BSOA is a global convergence algorithm. Finally, the BSOA is applied to the estimation of the unknown parameters of the PEMFC modules and compared with the state-of-the-art algorithms. The simulation results show that BSOA is an effective algorithm for estimating the unknown parameters of the PEMFC model.

Real time power management strategy for an all‐electric ship using a predictive control model

This study proposes a power management strategy (PMS) that can efficiently and accurately address the nonlinear dynamics of ship power systems for all-electric ships (AESs). The design of a PMS can be considered naturally in a model predictive control framework. However, the control error caused by a model mismatch is a challenge of the PMS for physical system implementation. The proposed PMS is based on a complex system-level nonlinear model that ensures a minimum mismatch. In addition, an efficient optimization algorithm is proposed to efficiently apply the PMS. The proposed method is verified using real-time simulations with physical systems in various scenarios. The experimental results demonstrate the superior efficiency of the proposed optimization algorithm and the accuracy of the developed PMS for physical system implementation.

Fast, closed-loop iterative system-on-chip of deflection efficiency enhancement for a liquid crystal optical phased array.

To implement a liquid crystal optical phased array (LC-OPA) on a practical free-space laser communication terminal, there are two essential parameters: insertion loss and the closed-loop bandwidth required to meet the dynamic linking condition of the acquisition-tracking-pointing sub-system. Real-time hardware platforms and deflection efficiency optimization algorithms have been suggested since the invention of LC-OPA. In this paper, the so-called ZYNQ platform, a field-programmable-gate-array-based heterogeneous system-on-chip (SoC), is utilized to keep real-time response and accelerate data generation, such as beam steering, beamforming, beam enhancement, etc. In addition, a novel, to the best of our knowledge, optimization algorithm is proposed on the concept of dimension reduction of the number of objective variables. After deploying on this heterogeneous SoC platform, numerical simulations and experimental results both verify that, compared to the conventional PC-based system, the integrated SoC platform offers 15.8 times faster iterative speed, a rapid convergence rate, and excellent robustness, yet with less usage of power, physical size, and monetary cost. The efficiency enhancement process costs only a few seconds at any angle, laying the foundation for practical in-line applications.

Multi-view unsupervised feature selection with tensor low-rank minimization

To describe objects more comprehensively and accurately, multi-view learning has attracted considerable attention. Recently, graph embedding based multi-view feature selection methods have been proposed and shown efficient in many real applications. The existing methods generally construct one common graph matrix to exploit the local structure of multi-view data via the linear weight fusion or learning one common graph matrix across all views. However, since all views share the identical graph structure, this emphasizes the consistency too much, resulting in restricting the diversity among different views. In this paper, a tensor low-rank constrained graph embedding method is proposed for multi-view unsupervised feature selection. To embody the view-specific information of each view, our model constructs the graph structure for each corresponding view, respectively. To capture the consistency across views, a tensor low-rank regularization constraint is imposed on the tensor data formed by these graph matrices. An efficient optimization algorithm with theoretical convergence guarantee is designed to solve the proposed method. Extensive experimental results validate that the proposed method outperforms some state-of-the-art methods. The code of our model can be found athttps://www.researchgate.net/publication/353902948_demoTLR.

Snake Optimizer: A novel meta-heuristic optimization algorithm

In recent years, several metaheuristic algorithms have been introduced in engineering and scientific fields to address real-life optimization problems. In this study, a novel nature-inspired metaheuristics algorithm named as Snake Optimizer (SO) is proposed to tackle a various set of optimization tasks which imitates the special mating behavior of snakes. Each snake (male/female) fights to have the best partner if the existed quantity of food is enough and the temperature is low. This study mathematically mimics and models such foraging and reproduction behaviors and patterns to present a simple and efficient optimization algorithm. To verify the validity and superiority of the proposed method, SO is tested on 29 unconstrained Congress on Evolutionary Computation (CEC) 2017 benchmark functions and four constrained real-world engineering problems. SO is compared with other 9 well-known and newly developed algorithms such as Linear population size reduction-Success-History Adaptation for Differential Evolution (L-SHADE), Ensemble Sinusoidal incorporated with L-SHADE (LSHADE-EpSin), Covariance matrix adaptation evolution strategy (CMAES), Coyote Optimization Algorithm (COA), Moth-flame Optimization, Harris Hawks Optimizer, Thermal Exchange optimization, Grasshopper Optimization Algorithm, and Whale Optimization Algorithm. Experimental results and statistical comparisons prove the effectiveness and efficiency of SO on different landscapes with respect to exploration–exploitation balance and convergence curve speed. The source code is currently available for public from: https://se.mathworks.com/matlabcentral/fileexchange/106465-snake-optimizer