Global Optimization Strategy Research Articles

Multi-population-based adaptive sine cosine algorithm with modified mutualism strategy for global optimization

The sine cosine algorithm (SCA) is a population-based metaheuristic strategy that has been demonstrated competitive performance and has received significant attention from scientists in various fields. Regardless, like other population-based techniques, SCA also has a tendency to get stuck in adjacent optima and uneven exploitation. Given the shortcomings of SCA, a new modified SCA variation, MAMSCA, with a balanced global and local search, is presented in this work. The new method divides the population into two equal halves for updating using a sine or cosine strategy. To provide additional variation to the population, a modified mutualism phase is adopted. To increase convergence speed and accuracy of the solution, a one-to-one mapping between the individuals of each half is maintained. The new algorithm is compared with state-of-the-art algorithms and modified algorithms using classical benchmark functions and IEEE CEC 2019 functions. The method’s real-world relevance is demonstrated by tackling five engineering design challenges. The comparison of numerical results and analysis of performance measures of the algorithm from the statistical aspect, time complexity, and speed of generating the solution demonstrates the considerable improvement of the proposed algorithm to solve real-world challenges.

Reliability-based optimization of multiple Folded Pendulum TMDs through Efficient Global Optimization

Pendulum-tuned mass dampers (PTMDs) are attractive to control horizontal vibrations in buildings. In addition to their performance, they present relatively low cost, and they are easy to install and maintain. Nevertheless, PTMD may be too long when the fundamental frequency of the host structure is low. A solution that retains these associated advantages while reducing the headroom required on the top floor is the so-called Folded Pendulum Tuned Mass Dampers (FPTMDs). In this system, the TMD mass is hung by an interposed pendulum frame, reducing the total length by approximately half. Even so, a literature review shows that reliability-based design optimization (RBDO) studies of single and multiple FPTMDs cannot be found to the best of the authors’ knowledge. In this context, this article performs a RBDO of multiple FPTMDs installed in a tall building subjected to turbulent wind excitation. A 120 m tall steel building is taken as the case study. Uncertainties in the system parameters and the wind excitation are taken into account. Due to the nonconvex and multimodal nature of the objective function when multiple FPTMDs are considered, a Kriging-based Efficient Global Optimization (EGO) scheme is employed to speed up convergence of the global search. The EGO algorithm could find the optimum results with reduced computational cost for the cases considered. In addition, scenarios with multiple FPTMDs were indeed more reliable than with single FPTMDs, leading to a reduction in the probability of failure.

An adaptive dimension differential evolution algorithm based on ranking scheme for global optimization.

In recent years, evolutionary algorithms based on swarm intelligence have drawn much attention from researchers. This kind of artificial intelligent algorithms can be utilized for various applications, including the ones of big data information processing in nowadays modern world with heterogeneous sensor and IoT systems. Differential evolution (DE) algorithm is one of the important algorithms in the field of optimization because of its powerful and simple characteristics. The DE has excellent development performance and can approach global optimal solution quickly. At the same time, it is also easy to get into local optimal, so it could converge prematurely. In the view of these shortcomings, this article focuses on the improvement of the algorithm of DE and proposes an adaptive dimension differential evolution (ADDE) algorithm that can adapt to dimension updating properly and balance the search and the development better. In addition, this article uses the elitism to improve the location update strategy to improve the efficiency and accuracy of the search. In order to verify the performance of the new ADDE, this study carried out experiments with other famous algorithms on the CEC2014 test suite. The comparison results show that the ADDE is more competitive.

Numerical treatment for the nonlinear fifth kind of multi-singular differential model: a neuro-swarming approach

In this study, a numerical scheme is proposed for the fifth order (FO) singular differential model (SDM), FO-SDM. The solutions of the singular form of the differential models are always considered difficult to solve and huge important in astrophysics. A neural network study together with the hybrid combination of global particle swarm optimization and local sequential quadratic programming schemes is provided to find the numerical simulations of the FO-SDM. An objective function is constructed using the differential FO-SDM along with the boundary conditions. The correctness of the scheme is observed by providing the comparison of the obtained and exact solutions. The justification of the proposed scheme is authenticated in terms of absolute error (AE), which is calculated in good measures for solving the FO-SDM. The efficiency and reliability of the stochastic approach are observed using the statistical performances to solve the FO-SDM.

System parameter exploration of ship maneuvering model for automatic docking/berthing using CMA-ES

Accurate maneuvering estimation is essential to establish autonomous berthing control. The system-based mathematical model is widely used to estimate the ship’s maneuver. Commonly, the system parameters of the mathematical model are obtained by the captive model test (CMT), which is time-consuming to construct an accurate model suitable for complex berthing maneuvers. System identification (SI) is an alternative to constructing the mathematical model. However, SI on the mathematical model of ship’s maneuver has been only conducted on much simpler maneuver: turning and zig-zag. Therefore, this study investigates the SI on a mathematical model capable of berthing maneuver. The main contributions of this study are as follows: (i) construct the system-based mathematical model on berthing by optimizing system parameters with a reduced amount of model tests than the CMT-based scheme; (ii) Find the favorable choice of objective function and type of training data for optimization. Global optimization scheme CMA-ES explored the system parameters of the MMG model from the free-running model’s trajectories. The berthing simulation with the parameters obtained by the proposed method showed better agreement with the free-running model test than parameters obtained by the CMT. Furthermore, the proposed method required fewer data amounts than a CMT-based scheme.

Learning-Based Stereoscopic View Synthesis with Cascaded Deep Neural Networks

Depth image-based rendering (DIBR) is an important technique in the 2D to 3D conversion process, which renders virtual views with a texture image and the associated depth map. However, certain problems, such as disocclusion, still exist in current DIBR systems. In this study, a new learning-based framework that models conventional DIBR synthesis pipelines is proposed to solve these problems. The proposed model adopts a coarse-to-fine approach to realize virtual view prediction and disocclusion region refinement sequentially in a unified deep learning framework that includes two cascaded joint filter block-based convolutional neural networks (CNNs) and one residual learning-based generative adversarial network (GAN). An edge-guided global looping optimization strategy is adopted to progressively reconstruct the scene structures on the novel view, and a novel directional discounted reconstruction loss is proposed for better training. In this way, our framework performs well in terms of virtual view quality and is more suitable for 2D to 3D conversion applications. The experimental results demonstrate that the proposed method can generate visually satisfactory results.

A Cluster Based Algorithm Coupled With Shooting Method for Estimation of Parametric Clusters Yielding Optimal Stable Periodic Solutions in Nonlinear Vibrating Systems

Abstract This work presents a novel cluster based optimization procedure for estimating parameter values that yield stable, periodic responses with desired amplitude in nonlinear vibrating systems. The parameter values obtained by conventional nonlinear optimization schemes, with minimization of amplitude as the objective, may not furnish periodic and stable responses. Moreover, global optimization strategies may converge to isolated optima that are sensitive to parametric perturbations. In practical engineering systems, unstable or isolated optimal orbits are not practically realizable. To overcome these limitations, the proposed method tries to converge to a cluster in the r-dimensional parameter space in which the design specifications including the specified optimality, periodicity, stability and robustness are satisfied. Thus, it eliminates the need for computationally expensive bifurcation studies to locate stable, periodic parameter regimes before optimization. The present method is based on a hybrid scheme which involves the algebraic form of the governing equations in screening phase and its differential form in the selection phase. In the screening phase, force and energy balance conditions are used to rephrase the nonlinear governing equations in terms of the design parameter vector. These rephrased equations are reduced to algebraic form using a harmonic balance procedure which also specifies the desired amplitude and frequency of the response. An error norm based on this algebraic form is defined and is used to contract the search bounds in the parameter space leading to convergence to a cluster. The selection phase of the algorithm uses shooting method coupled with evaluation of Floquet multipliers to retain only those vectors in the arrived cluster yielding stable periodic solutions. The method is validated with Den Hartog's vibration absorbers and is then applied to vibration absorbers with material nonlinearity and vibration isolators with geometric nonlinearity. In both the cases, the converged cluster is shown to yield stable, periodic responses satisfying the amplitude condition. Parametric perturbation studies are conducted on the cluster to illustrate its robustness. The use of algebraic form of governing equations in the screening phase drastically reduces the computational time needed to converge to the cluster. The fact that the present method converges to a cluster in the parameter space rather than to a single parameter value offers the designer more freedom to choose the design vector from inside the cluster. It also ensures that the design is robust to small changes in parameter values.

Two-level model predictive control energy management strategy for hybrid power ships with hybrid energy storage system

Compared with the load power characteristics of ground power systems, the intermittent and random fluctuation of ship load power demand brings challenges to the energy management system of ship power systems. To achieve fuel economy and solve the problem of power fluctuation, the hybrid energy storage system (HESS) composed of the battery pack and ultra-capacitor is applied to the diesel-electric hybrid ship. A two-level model predictive control (MPC) strategy consisting of two optimization stages is proposed. The first stage aims to determine the power distribution solution of the diesel generator and battery at a large time-scale. In the second stage, the operation strategy of the ultracapacitor is determined at a small time-scale based on the power distribution solution of the first stage. The conventional MPC-based strategy and global optimization strategy based on dynamic programming (DP) are compared with the proposed strategy. The simulation results show that the proposed strategy has better fuel saving ability, and realizes the clear power frequency division between the supercapacitor and battery. In addition, the proposed strategy can also approach the performance of global optimization.

Multivariate optimization of the electrochemical degradation for COD and TN removal from wastewater: An inverse computation machine learning approach

An inverse computation machine learning (ML) framework that couples genetic algorithm (GA) to feedforward neural network was developed to optimize the operating parameters for maximizing electrochemical removal of chemical oxygen demand (COD) and total nitrogen (TN) from wastewater. Conventional neural networks (NN) are generally unable to implement inverse computation from a desired abatement to feasible input conditions, and NN trained from small-scale datasets tends to be unreliable for multivariate simulation. To address this issue, we employed GA for tuning the weights and biases of the network to enhance the stability and generalization performance of NN, the optimization of multiple operating conditions was performed by the GA strategy of iterative evolution and global search.In this work, we investigated and analyzed the performance of multivariate optimization approaches such as orthogonal design, response surface methodology (RSM), traditional NN, and the developed neuro-genetic model (GANN). Significance and error analysis demonstrate that GANN exhibits superior stability and generalization ability with the highest R2 of 0.946 (COD), 0.874 (TN), and the lowest RMSE of 0.022 (COD), 0.028 (TN). The introduction of GA is significant for improving the prediction accuracy of NN derived from small-scale datasets. The average error of the GANN trained by 25 data points is lower than that of the RSM model derived from 54 data points. According to the validation outcomes, the scheme suggested by GANN achieves the best COD (93.6%) and TN (62.8%) degradation efficiencies, which are 6.1% and 9.9% higher than the optimal values in the original dataset, respectively. The proposed multivariate global optimization strategy can be extended to other cases, and the computational framework of GANN also contributes to the progress of more reliable ML models.

Enhanced slime mould algorithm with multiple mutation strategy and restart mechanism for global optimization

Slime mould algorithm (SMA) is a new metaheuristic algorithm proposed in 2020, which has attracted extensive attention from scholars. Similar to other optimization algorithms, SMA also has the drawbacks of slow convergence rate and being trapped in local optimum at times. Therefore, the enhanced SMA named as ESMA is presented in this paper for solving global optimization problems. Two effective methods composed of multiple mutation strategy (MMS) and restart mechanism (RM) are embedded into the original SMA. MMS is utilized to increase the population diversity, and the RM is used to avoid the local optimum. To verify the ESMA’s performance, twenty-three classical benchmark functions are employed, as well as three well-known engineering design problems, including welded beam design, pressure vessel design and speed reducer design. Several famous optimization algorithms are also chosen for comparison. Experimental results show that the ESMA outperforms other optimization algorithms in most of the test functions with faster convergence speed and higher solution accuracy, which indicates the merits of proposed ESMA. The results of Wilcoxon signed-rank test also reveal that ESMA is significant superior to other comparative optimization algorithms. Moreover, the results of three constrained engineering design problems demonstrate that ESMA is better than comparative algorithms.

Exploring the Clinical Point Selection Rules of Acupuncture and Moxibustion in the Treatment of Infantile Enuresis Based on Data Mining Technology.

In order to explore the clinical point selection rules of acupuncture and moxibustion in the treatment of infantile enuresis based on data mining technology, first, it introduces the principle of data mining technology, the global optimization strategy of information extraction + genetic search. In China Biomedical Literature Database, CNKI, Weipu, Wanfang. Use “enuresis,” “enuresis,” “leathering,” “urinary discharge,” “drowning,” “bedwetting,” “nocturia,” “drowning,” “moxibustion,” “moxibustion,” and “moxibustion” as search terms, take (enuresis + enuresis + urination + urination + addiction + bedwetting), (moxibustion + moxibustion + moxibustion)” as the retrieval style. The analysis of the use of moxibustion, the law of acupoint selection by different moxibustion methods, and the frequency analysis of meridian use show that most of the moxibustion methods are used (62 articles, of which 3 articles use 2 moxibustion methods), including non-suppurative moxibustion, ginger moxibustion, medicinal cake moxibustion, salt moxibustion, mild moxibustion, warm acupuncture moxibustion, bird pecking moxibustion, circling moxibustion, and warm moxibustion; among them, mild moxibustion has the highest frequency of use 24 and non-suppurative moxibustion and salt-separated moxibustion have the lowest frequency of use1; there are 4 non-moxibustion methods, which are medicated thread moxibustion methods. Different moxibustion methods for acupoint selection have their own emphasis. The 63 included literatures included 63 prescriptions for moxibustion, 11 prescriptions for acupoints. Analyzing the frequency of acupoint selection, a total of 18 ear acupoints are involved, and the total frequency of auricular acupoints is 54 times; 44 points are involved (including 1 odd point outside the meridian), the total frequency of acupoints is 300 times, and the total frequency of meridian points is 298, and the total frequency of Jingwaiqi acupoints (Sishencong) is 2. The results can provide great help for the clinical selection of acupuncture points for the treatment of infantile enuresis.

A virtual laboratory based on full-field crystal plasticity simulation to characterize the multiscale mechanical properties of AHSS

In this work, we proposed a virtual laboratory based on full-field crystal plasticity (CP) simulation to track plastic anisotropy and to calibrate yield functions for multiphase metals. The virtual laboratory, minimally, only requires easily accessible EBSD data for constructing the highly-resolved microstructural representative volume element and macroscopic flow stress data for identifying the micromechanical parameters of constituent phases. An inverse simulation method based on a global optimization scheme was developed to identify the CP parameters, and a nonlinear least-squares method was employed to calibrate yield functions. Mechanical tests of advanced high strength steel sheet under various loading conditions were conducted to validate the virtual laboratory. Three well-known yield functions, the quadratic Hill48 and non-quadratic Yld91 and Yld2004-18p yield functions, were selected as the validation benchmarks. All the studied functions, calibrated by numerous stress points of arbitrary loading conditions, successfully captured both the deformation and strength anisotropies. The full-field CP modeling correlated well the microscopic deformation mechanism and plastic heterogeneity with the macromechanical behavior of the sheet. The proposed virtual laboratory, which is readily extended with physically based CP models, could be a versatile tool to explore and predict the mechanical property and plastic anisotropy of advanced multiphase materials.

Energy-saving service management technology of internet of things using edge computing and deep learning

The purpose is to solve the problems of high transmission rate and low delay in the deployment of mobile edge computing network, ensure the security and effectiveness of the Internet of things (IoT), and save resources. Dynamic power management is adopted to control the working state transition of Edge Data Center (EDC) servers. A load prediction model based on long-short term memory (LSTM) is creatively proposed. The innovation of the model is to shut down the server in idle state or low utilization in EDC, consider user mobility and EDC location information, learn the global optimal dynamic timeout threshold strategy and N-policy through trial and error reinforcement learning method, reasonably control the working state switching of the server, and realize load prediction and analysis. The results show that the performance of AdaGrad optimization solver is the best when the feature dimension is 3, the number of LSTM network layers is 6, the time series length is 30–45, the batch size is 128, the training time is 788 s, the number of units is 250, and the number of times is 350. Compared with the traditional methods, the proposed load prediction model and power management mechanism improve the prediction accuracy by 4.21%. Compared with autoregressive integrated moving average (ARIMA) load prediction, the dynamic power management method of LSTM load prediction can reduce energy consumption by 12.5% and realize the balance between EDC system performance and energy consumption. The system can effectively meet the requirements of multi-access edge computing (MEC) for low delay, high bandwidth and high reliability, reduce unnecessary energy consumption and waste, and reduce the cost of MEC service providers in actual operation. This exploration has important reference value for promoting the energy-saving development of Internet-related industries.

Ridge regression with adaptive additive rectangles and other piecewise functional templates

We propose a penalization algorithm for functional linear regression models, where the coefficient function β is shrunk towards a data-driven shape template γ. To the best of our knowledge, we employ the nonzero centered L2 penalty in a novel manner, as the center of the penalty γ is also optimized while being constrained to belong to a class of piecewise functions Γ, by restricting its basis expansion. This indirect penalization allows the user to control the overall shape of β, by imposing his prior knowledge on γ through the definition of Γ, without limiting the flexibility of the estimated model. In particular, we focus on the case where γ is expressed as a sum of q rectangles that are adaptively positioned with respect to the regression error. As the problem of finding the optimal knot placement of a piecewise function is nonconvex, we also propose a novel parametrization that allows to reduce the number of variables in the global optimization scheme, resulting in a fitting algorithm that alternates between approximating a suitable template and solving a convex ridge-like problem. The predictive power and interpretability of our method is shown on multiple simulations and two real world case studies.

Spare Parts Stocking Decision Strategy and Service Logistics Cost Optimization of Two-Echelon Service Logistics System considering Multifailure Mode.

For the repair level and spare parts stocking decision problems, generally METRIC type methods and level of repair analysis (LORA) are used separately. In practical engineering, the repair level of large-scale systems is usually judged according to the failure modes. The method of judging the repair level by the maintenance success rate is no longer applicable. In the case of multiple failure modes of a large-scale system, considering the requirements of system availability, we build a spare parts stocking decision and service logistics cost optimization model of a two-echelon service logistics system. Aiming at the spare parts stocking allocation problem caused by multiple failure modes, we improve the iterative greedy heuristic algorithm to find the global optimal stocking allocation strategies. Finally, through the analysis of typical examples, the correctness and effectiveness of the model and algorithm are verified. The impact of multifailure mode spare parts stocking allocation strategies on availability and service logistics cost is analyzed. The research results are helpful to simplify the support engineering design process of system engineers and have certain theoretical and application value.

Global dynamic optimization with Hammerstein–Wiener models embedded

Hammerstein–Wiener models constitute a significant class of block-structured dynamic models, as they approximate process nonlinearities on the basis of input–output data without requiring identification of a full nonlinear process model. Optimization problems with Hammerstein–Wiener models embedded are nonconvex, and thus local optimization methods may obtain suboptimal solutions. In this work, we develop a deterministic global optimization strategy that exploits the specific structure of Hammerstein–Wiener models to extend existing theory on global optimization of systems with linear dynamics. At first, we discuss alternative formulations of the dynamic optimization problem with Hammerstein–Wiener models embedded, demonstrating that careful selection of the optimization variables of the problem can offer significant numerical advantages to the solution approach. Then, we develop convex relaxations for the proposed optimization problem and discuss implementation aspects to obtain the global solution focusing on a control parametrization technique. Finally, we apply our optimization strategy to case studies comprising both offline and online dynamic optimization problems. The results confirm an improved computational performance of the proposed solution approach over alternative options not exploiting the linear dynamics for all considered examples. They also underline the tractability of deterministic global dynamic optimization when using few control intervals in online applications like nonlinear model predictive control.

Diversified Position Update Equation-Based SSA with Refreshing-Gap Strategy for global optimization

This study, incorporating three improvements into the structure of the original SSA algorithm to increase its exploration and exploitation capabilities, suggests an SSA variant algorithm named ”Diversified Position Update Equation-Based SSA with Refreshing-Gap Strategy (DSSA-R)”. Unlike the original SSA, DSSA-R accepts half of the population as the leader instead of using a single leader salp strategy. Nevertheless, the suggested algorithm includes two different equations to update the positions of the leader salps and follower salps. Furthermore, Refreshing Gap strategy was included in DSSA-R to avoid the algorithm from being stuck in the local optimum. The final improvement made on the original SSA was the addition of ”Linear Population Reduction Strategy” to strengthen the exploitation aspect of the algorithm. The performance of the DSSA-R algorithm was tested using the functions of the CEC 2014 and CEC 2015 benchmark suites and compared with five SSA variants and four state-of-the-art algorithms. Test results show that DSSA-R obtained better and competitive results compared to the algorithms it was compared with.

Inverse Problem in Resonant Ultrasound Spectroscopy With Sampling and Optimization: A Comparative Study on Human Cortical Bone.

The Bayesian inference with prior knowledge has been proposed recently to solve the inverse problem in resonant ultrasound spectroscopy. It allows inferring the elastic properties of high damping materials, such as cortical bone with less dependence on the initial guessed values. In this method, the estimation of the stiffness coefficients is expressed as a probabilistic solution to the inverse problem, which can be achieved by sampling or optimization methods. However, the detailed performance comparison of these two strategies applied to high damping materials has not been fully studied. In this work, the full stiffness tensor of 52 transversely isotropic cortical bone specimens was obtained using Markov chain Monte Carlo (MCMC) sampling and particle swarm optimization (PSO), respectively. Results showed that the local probability distributions of stiffness coefficients estimated by the two methods are consistent. Compared with MCMC, the average calculation speed of PSO is ten times faster [614 s ± 59 s (MCMC) versus 53 s ± 22 s (PSO)]. The mean standard error between theoretical and experimental resonant frequencies was slightly smaller for PSO compared with MCMC. In conclusion, PSO, a global optimization strategy, is suitable to solve the inverse problem for high damping materials.

GREEN: A Global Optimization Scheme for Transportation Efficiency by Mining Taxi Mobility

Taxi business, with its ubiquitous availability, route flexibility and comfortable travel experience, offers a complementary service for the public transportation system. Among the existing methods of doing taxi business, an meaningful issue is to mine efficient seeking strategies for taxi drivers, in order to improve transportation efficiency. Recent efforts have been made mainly on the individual recommendation with respect to shorter seeking time and higher seeking efficiency, whereas the global transportation efficiency will greatly be reduced once each driver only pays attention to his local optimization. Rather than the individual recommendation, in this paper we conduct research on mining the taxis mobility from large-scale taxi data, thereby proposing a novel solution, namely GREEN (short for A Global RoutEs rEcommeNdation), to improve the seeking strategies and optimize the global transportation situation. Specifically, we first investigate how the drop-off information affects seeking strategies and conduct quantitive analysis, revealing the impact of seeking efficiency, passenger density and top drivers’ experience. Moreover, to deal with the conflict between local optimization and global optimization, we dynamically adjust the weights of road segments based on the number of vacant taxis passing through each road segment. Also, to well evaluate the transportation efficiency, we define the seeking efficiency, net revenue and operation efficiency. Extensive experiments on the real-world dataset demonstrate that our scheme can work well, which not only improves the overall seeking efficiency by reducing total vacant driving time, but also increases the global operation efficiency, thereby optimizing the global transportation efficiency.

Multi-Hop Task Offloading With On-the-Fly Computation for Multi-UAV Remote Edge Computing

The dramatic growth in computing capability and the inherent mobility of the unmanned aerial vehicles (UAVs) foster the recent surge of interests in incorporating UAVs into edge computing systems to facilitate on-demand deployment and extended coverage. Nonetheless, due to the limited communication capability of the UAVs, single-UAV edge computing systems may still be incompetent when serving remote users. Although the traditional multi-UAV relay network can be a viable solution, it fails to exploit the computing capability of the UAVs. With this consideration, a multi-hop task offloading with on-the-fly computation scheme is proposed in this work to enable a more powerful multi-UAV remote edge computing network. To solve the corresponding joint resource allocation and deployment problem, two efficient algorithms are proposed. One of them can find the global optimal strategy in a special case, while the other can obtain a good local optimal strategy in the general cases. Both algorithms have a complexity only linear in the number of UAVs and admit distributed implementation. In addition to analysis, numerical results are provided to corroborate the effectiveness of the proposed scheme.