Local Optimization Research Articles

Optimization of Coreless PCB Coils Based on a Modified Taguchi Tuning Method for WPT of Pedelec

The printed circuit board (PCB) winding coil offers advantages such as small size, high precision, high repeatability, and low cost, making it conducive to the miniaturization of electronic equipment and a popular choice in wireless power transmission systems. This paper aims to clarify the correlation between induction parameters and inductive capabilities using the orthogonal array of the modified Taguchi method for Pedelec applications. The conventional Taguchi method typically achieves only local optimization; however, this paper considers practical application conditions and combines experimental data to establish the initial values of the orthogonal array, thereby achieving global optimization. Additionally, the tuning process of the Taguchi method replaces physical experiments with simulations, enhancing optimization speed and reducing hardware implementation costs. The performance index for the proposed modified Taguchi tuning method is selected as a combination of the quality factor (Q) and coupling coefficient (k) to minimize AC resistance and improve system efficiency. To validate the proposed method, the designed coils were implemented and tested in a WPT system based on S–S compensation with a half-bridge topology. The experimental results demonstrate that the optimized PCB coil parameters derived from the proposed tuning method accurately validate the method’s effectiveness and accuracy. From the measured results with the proposed modified tuning method, the system efficiency is increased by 43.87% and the system transmitting power is increased by 28.51%.

Application of intelligent algorithms in optimal operation of pumping stations

Abstract An optimal operation model of pumping stations is established in this paper, with the minimum energy consumption of the pumping station as the optimization objective, considering the constraints of the pumping station operating head, flowrate of the pumping station and so on. Simulated annealing is introduced into genetic algorithm, particle swarm optimization, wolf pack algorithm and grey wolf optimization to solve the model. The minimum value, average value, relative standard deviation of the objective function and infeasibility of constraint conditions are selected as the evaluation indexes of the algorithms performance. The calculation results show that simulated annealing ensures that the intelligent algorithms can find the optimal solution in the feasible solution space and avoid them falling into local optimization. The operation schemes obtained by solving the example model with the improved algorithm can reduce the energy consumption of the pumping station by 5.759%∼6.682%, which provides guidance for the operation schemes of the example pumping station. The results can be further applied in other pumping stations.

Construction Method of Information Security Detection based on Clustering Algorithm

A method for K-prototype clustering, which can process mixed data types, is proposed first. An algorithm for information security evaluation of hybrid clusters based on K-prototypes was constructed. This method makes full use of the excellent global optimization performance of PSO and effectively solves the defect that the K-protection function quickly falls into local optimization. Simulation results show that the proposed method can effectively prevent local extreme values and improve overall performance.

Exomod: A Python Library for Exoplanet Transit Light Curve Fitting and Stacking with Background Simulation and Optimization

Abstract From the light curve taken during exoplanet transit, important parameters such as the radius ratio of the star-planet, impact parameter, inclination, and relative tangential velocity could be derived. A module was developed in the form of a Python library based on modeling using background simulation and fitting by utilizing global and local optimization, such as differential evolution and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method. For more accurate fitting, the effects of linear limb darkening on exoplanets are also included. The performance testing for the program was done by using dummy data to test its accuracy, followed by validation tests using WASP-12b transit curves to find out any discrepancies between our model and real exoplanet data. While performing a characterization test on WASP-12b, we found an interesting result regarding its impact parameter and the exoplanet’s velocity, which might be an interesting subject for further analysis.

Analysis and comparison of high-performance computing solvers for minimisation problems in signal processing

Several physics and engineering applications involve the solution of a minimisation problem to compute an approximation of the input signal. Modern hardware and software use high-performance computing to solve problems and considerably reduce execution time. In this paper, different optimisation methods are compared and analysed for the solution of two classes of non-linear minimisation problems for signal approximation and denoising with different constraints and involving computationally expensive operations, i.e., (i) the global optimisers divide rectangle-local and the improved stochastic ranking evolution strategy, and (ii) the local optimisers principal axis, the Limited-memory Broyden, Fletcher, Goldfarb, Shanno, and the constrained optimisation by linear approximations. The proposed approximation and denoising minimisation problems are attractive due to their numerical and analytical properties, and their analysis is general enough to be extended to most signal-processing problems. As the main contribution and novelty, our analysis combines an efficient implementation of signal approximation and denoising on arbitrary domains, a comparison of the main optimisation methods and their high-performance computing implementations, and a scalability analysis of the main algebraic operations involved in the solution of the problem, such as the solution of linear systems and singular value decomposition. Our analysis is also general regarding the signal processing problem, variables, constraints (e.g., bounded, non-linear), domains (e.g., structured and unstructured grids, dimensionality), high-performance computing hardware (e.g., cloud computing, homogeneous vs. heterogeneous). Experimental tests are performed on the CINECA Marconi100 cluster at the 26th position in the “top500” list and consider several parameters, such as functional computation, convergence, execution time, and scalability. Our experimental tests are discussed on real-case applications, such as the reconstruction of the solution of the fluid flow field equation on an unstructured grid and the denoising of a satellite image affected by speckle noise. The experimental results show that principal axis is the best optimiser in terms of minima computation: the efficiency of the approximation is 38% with 256 processes, while the denoising has 46% with 32 processes.

An Efficient Optimization Model and Tabu Search–Based Global Optimization Approach for the Continuous p-Dispersion Problem

Continuous p-dispersion problems with and without boundary constraints are NP-hard optimization problems with numerous real-world applications, notably in facility location and circle packing, which are widely studied in mathematics and operations research. In this work, we concentrate on general cases with a nonconvex multiply connected region that are rarely studied in the literature due to their intractability and the absence of an efficient optimization model. Using the penalty function approach, we design a unified and almost everywhere differentiable optimization model for these complex problems and propose a tabu search–based global optimization (TSGO) algorithm for solving them. Computational results over a variety of benchmark instances show that the proposed model works very well, allowing popular local optimization methods (e.g., the quasi-Newton methods and the conjugate gradient methods) to reach high-precision solutions due to the differentiability of the model. These results further demonstrate that the proposed TSGO algorithm is very efficient and significantly outperforms several popular global optimization algorithms in the literature, improving the best-known solutions for several existing instances in a short computational time. Experimental analyses are conducted to show the influence of several key ingredients of the algorithm on computational performance. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72122006, 71821001, and 72471100] Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0089 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0089 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Heuristic Optimization Algorithm of Black-Winged Kite Fused with Osprey and Its Engineering Application.

Swarm intelligence optimization methods have steadily gained popularity as a solution to multi-objective optimization issues in recent years. Their study has garnered a lot of attention since multi-objective optimization problems have a hard high-dimensional goal space. The black-winged kite optimization algorithm still suffers from the imbalance between global search and local development capabilities, and it is prone to local optimization even though it combines Cauchy mutation to enhance the algorithm's optimization ability. The heuristic optimization algorithm of the black-winged kite fused with osprey (OCBKA), which initializes the population by logistic chaotic mapping and fuses the osprey optimization algorithm to improve the search performance of the algorithm, is proposed as a means of enhancing the search ability of the black-winged kite algorithm (BKA). By using numerical comparisons between the CEC2005 and CEC2021 benchmark functions, along with other swarm intelligence optimization methods and the solutions to three engineering optimization problems, the upgraded strategy's efficacy is confirmed. Based on numerical experiment findings, the revised OCBKA is very competitive because it can handle complicated engineering optimization problems with a high convergence accuracy and quick convergence time when compared to other comparable algorithms.

Fuzzy optimization of the photo-Fenton process on o-toluidine degradation in the aspect wastewater treatment

Significant volumes of wastewater, particularly from the textile industry, pose environmental concerns due to the presence of hazardous substances such as ortho-toluidine (OT). The photo-Fenton process can be used to break down and remove this hazardous organic compound. Previous studies on the photo-Fenton process have focused on local optimization of operating variables without considering cost factors. The photo-Fenton process is studied in this paper with UVA irradiation, Fe2+ dosage, and H2O2 concentration considered as variables. The study uses fuzzy optimization in a multi-objective framework for making decisions to determine the optimal values of OT degradation with its corresponding cumulative uncertainty error (YA), and the total operating cost (CT), both of which are essential for assessing the techno-economic feasibility of the process. The Pareto front was generated from the objective functions to establish the boundary limits for YA and CT. The results show an overall satisfaction level of 71.81% for the objective functions, indicating a partially satisficing solution for maximizing OT degradation while minimizing operating cost. The optimum conditions of the variables require 85.70 W m−3 UVA irradiation, 0.5177 mM for Fe2+ dosage, and 7.85 mM for the H2O2 concentration. These conditions yielded an OT degradation value of 83.22% and a total operating cost of 768.61 USD·m−3. Comparison with previous literature showed an OT degradation efficiency that was 16.78% lower. However, this tradeoff in the process efficiency is offset by a total operating cost that is 2.28 times cheaper, emphasizing the cost-effectiveness of the fuzzy optimized solution.

A Reinforcement Learning-Based Bi-Population Nutcracker Optimizer for Global Optimization.

The nutcracker optimizer algorithm (NOA) is a metaheuristic method proposed in recent years. This algorithm simulates the behavior of nutcrackers searching and storing food in nature to solve the optimization problem. However, the traditional NOA struggles to balance global exploration and local exploitation effectively, making it prone to getting trapped in local optima when solving complex problems. To address these shortcomings, this study proposes a reinforcement learning-based bi-population nutcracker optimizer algorithm called RLNOA. In the RLNOA, a bi-population mechanism is introduced to better balance global and local optimization capabilities. At the beginning of each iteration, the raw population is divided into an exploration sub-population and an exploitation sub-population based on the fitness value of each individual. The exploration sub-population is composed of individuals with poor fitness values. An improved foraging strategy based on random opposition-based learning is designed as the update method for the exploration sub-population to enhance diversity. Meanwhile, Q-learning serves as an adaptive selector for exploitation strategies, enabling optimal adjustment of the exploitation sub-population's behavior across various problems. The performance of the RLNOA is evaluated using the CEC-2014, CEC-2017, and CEC-2020 benchmark function sets, and it is compared against nine state-of-the-art metaheuristic algorithms. Experimental results demonstrate the superior performance of the proposed algorithm.

Underwater image restoration via spatially adaptive polarization imaging and color correction

Polarization imaging is extensively employed in underwater image restoration owing to its effectiveness in eliminating backscattered light. However, the existing polarization-based imaging methods rely on the strong assumption that the degrees of polarization (DoPs) of the target and backscattered light remain constant. Additionally, these methods ignore wavelength-specific absorption phenomena. To address these challenges, we propose an underwater image restoration method via spatially adaptive polarization imaging and color correction. First, based on optimal polarization difference images, we propose a method for calculating the spatial DoP of the target. In this method, the polarization characteristics of the target and background are fully exploited, and a scoring function is designed to achieve an optimal differential image. Second, we propose an adaptive local optimization method to estimate the spatial DoP of backscattering, which involves dividing the image into multiple local regions, and then employing improved particle swarm optimization (IPSO) algorithms to obtain the optimal value of DoP for each region. Finally, we introduce an adaptive compensation method based on the channel with minimal color absorption to correct color shifts during underwater polarization imaging. Extensive experiments demonstrate that our method exhibits superior generalization capability and outstanding restoration performance compared to existing methods.

Empi: GPU-Accelerated Matching Pursuit with Continuous Dictionaries

This article introduces an effective approach to performing matching pursuit calculations with continuous (quasi-infinite) dictionaries. Simulating continuous parameter space is accomplished by combining optimal dictionary construction as introduced previously with local parameter optimization. At the same time, the calculations can be performed not only with Gabor atoms, but with any type of oscillating atoms, or even multiple types of atoms at once. The proposed method is confirmed by introducing a first stable version of a high-performance implementation empi , supporting calculations with continuous as well as discrete dictionaries, further accelerated by the use of multi-threading and GPU support.

A combined Markov Chain Monte Carlo and Levenberg–Marquardt inversion method for heterogeneous subsurface reservoir modeling

In this study, we systematically investigate an inverse method for heterogeneous porous media to obtain porosity and permeability fields considering only production well data. The forward modeling of the input data, namely pressure and saturation fields, is based on the motion equations of a coupled Darcy flow system involving two phases of isothermal fluid flow. We discretize these equations using a multiscale finite volume simulation technique in the spatial domain, and the backward Euler method in time domain. In the inversion procedure, we combine a global optimization method, the Markov Chain Monte Carlo (MCMC) method, with a local optimization method, the Levenberg–Marquardt (LM) method. The MCMC was implemented as the Random Walk algorithm, and to generate the samples of the porosity and permeability fields, we employed Karhunen–Loève (KL) expansion of second-order stationary fields with Gaussian covariance. The coefficients of the KL expansion are estimated by minimizing the norm of the residual dependent on these fields. At the end of the MCMC iterations, we refine the KL coefficients associated with the porosity and permeability fields using the LM method. We verify in the numerical experiments that the accuracy of the porosity and permeability fields is improved by the LM refinement step.

Quantifying the impact of flexibility asset location on services in the distribution grid: Power system and local flexibility market co-simulation

This research investigates the effectiveness of incorporating locational sensitivity factors into local flexibility market clearing mechanisms for effective congestion management and voltage regulation in distribution grids. A centralized local flexibility market optimization model is developed that considers technical and economic constraints. The study aims to explore the requirements for data availability, data quality, and reliable data exchange that can facilitate a broader range of flexibility services, thereby promoting the development of a local flexibility market. Sensitivity factors, including power transfer distribution factors, voltage sensitivity coefficients and transformer sensitivity coefficients, are used to quantify the impact of flexibility asset locations on congestion management and inform clearing rules. These static metrics are insufficient for establishing effective local flexibility market clearing rules when flexibility is procured by distribution system operators. The approach considers the state of the grid when calculating the sensitivity coefficients, which leads to a more accurate evaluation of flexibility bids, especially with regard to the impact of location on congestion management. The proposed mechanism for clearing the local flexibility market assumes continuous communication between the proposed local flexibility market operator and the distribution system operator for dynamic, iterative market clearing, which ensures the protection of grid data and a more accurate bid evaluation. The study demonstrates that the inclusion of locational information significantly increases the effectiveness of the proposed local flexibility market-based congestion management. The developed simulator for the proposed local flexibility market provides valuable insights into the interaction between the proposed local flexibility market and the distribution grid. The research results, derived from selected use cases, emphasize the importance of location-based sensitivity factors in the proposed local flexibility market clearing for distribution grids. The proposed approach offers a promising solution for optimizing congestion management and voltage regulation while ensuring efficient integration of distributed energy resources into distribution grids.

Inverse Design of Singlet Fission Materials with Uncertainty-Controlled Genetic Optimization.

Singlet fission has shown potential for boosting the power conversion efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty-controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and singlet exciton size for the discovery of singlet fission materials. The ucGA allows us to efficiently explore the chemical space spanned by the reFORMED fragment database, which consists of 45,000 cores and 5,000 substituents derived from crystallographic structures assembled in the FORMED repository. Running the ucGA in an exploitative setup performs local optimization on variations of known singlet fission scaffolds, such as acenes. In an explorative mode, hitherto unknown candidates displaying excellent excited state properties for singlet fission are generated. We suggest a class of heteroatom-rich mesoionic compounds as acceptors for charge-transfer mediated singlet fission. When included in larger conjugated donor-acceptor systems, these units exhibit strong localization of the triplet state, favorable diradicaloid character and suitable triplet energies for exciton injection into semiconductor solar cells. As the proposed candidates are composed of fragments from synthesized molecules, they are likely synthetically accessible.

Inverse Design of Singlet Fission Materials with Uncertainty‐Controlled Genetic Optimization

Singlet fission has shown potential for boosting the power conversion efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty‐controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and singlet exciton size for the discovery of singlet fission materials. The ucGA allows us to efficiently explore the chemical space spanned by the reFORMED fragment database, which consists of 45,000 cores and 5,000 substituents derived from crystallographic structures assembled in the FORMED repository. Running the ucGA in an exploitative setup performs local optimization on variations of known singlet fission scaffolds, such as acenes. In an explorative mode, hitherto unknown candidates displaying excellent excited state properties for singlet fission are generated. We suggest a class of heteroatom‐rich mesoionic compounds as acceptors for charge‐transfer mediated singlet fission. When included in larger conjugated donor‐acceptor systems, these units exhibit strong localization of the triplet state, favorable diradicaloid character and suitable triplet energies for exciton injection into semiconductor solar cells. As the proposed candidates are composed of fragments from synthesized molecules, they are likely synthetically accessible.

Improving QoS Management Using Associative Memory and Event-Driven Transaction History

Managing modern, web-based, distributed applications effectively is a complex task that requires coordinating several aspects, including understanding the relationships among their components, the way they interact, the available hardware, the quality of network connections, and the providers hosting them. A distributed application consists of multiple independent and autonomous components. Managing the application involves overseeing each individual component with a focus on global optimization rather than local optimization. Furthermore, each component may be hosted by different resource providers, each offering its own monitoring and control interfaces. This diversity adds complexity to the management process. Lastly, the implementation, load profile, and internal status of an application or any of its components can evolve over time. This evolution makes it challenging for a Quality of Service (QoS) manager to adapt to the dynamics of the application’s performance. This aspect, in particular, can significantly affect the QoS manager’s ability to manage the application, as the controlling strategies often rely on the analysis of historical behavior. In this paper, the authors propose an extension to a previously introduced QoS manager through the addition of two new modules: (i) an associative memory module and (ii) an event forecast module. Specifically, the associative memory module, functioning as a cache, is designed to accelerate inference times. The event forecast module, which relies on a Weibull Time-to-Event Recurrent Neural Network (WTTE-RNN), aims to provide a more comprehensive view of the system’s current status and, more importantly, to mitigate the limitations posed by the finite number of decision classes in the classification algorithm.

State of Health Estimation of Lithium‐Ion Batteries Based on Differential Thermal Voltammetry and Improved Gray Wolf Optimizer Optimizing Gaussian Process Regression

Accurate estimation of the state of health (SOH) of lithium‐ion batteries (LIBs) is essential for their safe operation. Therefore, herein, a novel approach that combines Gaussian process regression (GPR) optimized using an improved gray wolf optimizer (IGWO) with differential thermal voltammetry (DTV) is introduced. In this approach, the peak and valley information of the DTV curves are used to reveal the battery‐aging mechanisms, with the slopes and durations between peaks and valleys used as health characteristics. The correlation between the characteristics and SOH of the battery is analyzed to build a health feature dataset. IGWO optimizes the GPR hyperparameters to address their dependence on the initial values and susceptibility to local optimization and employs a dimension‐learning strategy to enhance the population diversity and prevent premature convergence. DTV curves and an IGWO‐GPR model for SOH estimation using four cells from the NASA LIB public aging dataset are developed and validated. The results show root mean square errors below 0.007 and mean absolute errors under 0.006 for all cells. The coefficient of determination exceeds 0.92 for three cells, with one battery exhibiting a value of 0.866. This method provides accurate and efficient SOH estimation, essential for safe battery operation.

A dichotomous angle refinement discrete material optimization method for fiber composite materials

The discrete material optimization method optimizes the distribution of materials by setting candidate materials using a multi-phase material penalty model. Addressing the issue of reduced efficiency in fiber orientation optimization due to a large number of candidate materials, this paper proposes a dichotomous angle refinement discrete material optimization method to enhance solution speed. This method leverages the efficiency of the bisection approach to rapidly refine the angle in discrete optimization results, thereby narrowing the range of candidate fiber orientations. Furthermore, by introducing fiber orientations perpendicular to the optimized orientation, it mitigates potential local optimization issues during the optimization process, thereby enhancing the method’s optimization capability. Compared to traditional discrete material optimization methods, this method employs a special dichotomy method to determine the candidate materials for each element, thereby decreasing the number of design variables. This method demonstrates higher computational efficiency when solving fiber orientation optimization problems with a large number of candidate materials. Several numerical examples provided in the paper validate the efficiency and stability of the proposed method in addressing optimization problems.

Density-guided and Adaptive Update Strategy for Multi-objective Particle Swarm Optimization

Abstract As a powerful optimization technique, multi-objective particle swarm optimization (MOPSO) has been paid more and more attention by scientists. However, in more complex problems, MOPSO faces the challenges of weak global search ability and easy to fall into local optimality. To address these challenges and obtain better solutions, people have proposed many variants. In this study, a density-guided and adaptive update strategy for multi-objective particle swarm optimization (DAMOPSO) is proposed. First, an adaptive grid is used to determine the mutation particles and guides. Then the Cauchy mutation operator is performed for the poorly distributed particles to expand the search space of the population. Additionally, the strategy of non-dominated sorting and hyper-region density are devised for maintaining external archives, which contribute to the uniform distribution of optimal solutions. Finally, an adaptive detection strategy based on the adjustment coefficient and conversion efficiency is designed to update the flight parameters. These approaches not only speed up the convergence of algorithms, but also balance exploitation and exploration more effectively. The proposed algorithm is compared with several representative multi-objective optimization algorithms on 22 benchmark functions; meanwhile, statistical tests, ablation experiments, analysis of stability and complexity are also performed. The experimental results demonstrate DAMOPSO is more competitive than other comparison algorithms.

Regional Differences in Carbon Budgets and Inter-Regional Compensation Zoning: A Case Study of Chongqing, China

Carbon compensation can guide human activities in reducing carbon emissions or increasing carbon sequestration and also represents an important approach for coordinating regional development. In this paper, Chongqing Municipality, whose internal development is varied, was selected as a case study. The annual carbon emissions, carbon sequestration, carbon deficits, and inter-regional carbon compensation costs from 2000 to 2021 were continuously estimated via local optimization methods, and a carbon compensation zoning scheme was proposed that integrates the present situation and trend analysis. The results show that (1) Chongqing’s total carbon emissions were greater than the total carbon sequestration, and the carbon deficit was approximately 556.24 × 104 t~3621.58 × 104 t. (2) County-level carbon budgets have large regional differences; the counties that should always receive carbon compensation are from the southeast and northeast regions, and the counties that should always pay carbon compensation are from central urban areas and the surrounding new urban areas. (3) All the counties were zoned into key payment areas, basic payment areas, key recipient areas, and basic recipient areas. The key payment areas, which account for 39.47%, maintain and grow payment status and are the main sources of carbon compensation costs, while the key recipient areas, which account for 44.74%, maintained a negative compensation status and a continuous downward trend, meaning that they may receive increasing carbon compensation costs. This paper revealed inequities in carbon compensation and proposed a novel zoning solution, which can provide scientific reference and data support for further establishing inter-regional carbon compensation mechanisms.