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The parameters of a lithium-ion battery are important to construct an effective battery management system. Parameter estimation assisted by the pseudo-two-dimensional (P2D) model is much more cost-effective than direct measurement methods. However, this is a nontrivial task, because the simulation of the P2D model is time-consuming. Alternatively, surrogate models such as reduced-order/data-driven models are often used to accelerate the parameter estimation process. Each category of surrogate models has its own strengths and weaknesses. Traditionally, reduced-order models run faster than data-driven models, while data-driven models are more accurate than reduced-order models. To leverage the complementary advantages of these two kinds of surrogate models, we make an interesting attempt to combine them compactly, thus proposing a two-phase surrogate model-assisted parameter estimation algorithm (TPSMA-PEAL). In the first phase, a fast reduced-order model is designed for parameter prescreening. In the second phase, a high-fidelity data-driven model is developed for fine estimation. In TPSMA-PEAL, except the time-consuming simulation, the other two challenges (i.e., the overfitting problem and the low observability of some parameters) are also considered from the perspective of optimization. Note that TPSMA-PEAL takes advantage of differential evolution and parameter sensitivity analysis to address them. Simulations and experiments verify that TPSMA-PEAL is efficient and accurate. [ABSTRACT FROM AUTHOR]

The job-shop scheduling problem (JSP) is NP hard, which has very important practical significance. Because of many uncontrollable factors, such as machine delay or human factors, it is difficult to use a single real-number to express the processing and completion time of the jobs. JSP with fuzzy processing time and completion time (FJSP) can model the scheduling more comprehensively, which benefits from the developments of fuzzy sets. Fuzzy relative entropy leads to a method that can evaluate the quality of a feasible solution following the comparison between the actual value and the ideal value (the due date). Therefore, the multiobjective FJSP can be transformed into a single-objective optimization problem and solved by a hybrid adaptive differential evolution (HADE) algorithm. The maximum completion time, the total delay time, and the total energy consumption of jobs will be considered. HADE adopts a mutation strategy based on DE-current-to-best. Its parameters (CR and F) are all made adaptive and normally distributed. The new individuals are selected according to the fitness value (FRE) obtained from a population consisting of N parents and N children in HADE. The algorithm is analyzed from different viewpoints. As the experimental results demonstrate, the performance of the HADE algorithm is better than those of some other state-of-the-art algorithms (namely, ant colony optimization, artificial bee colony, and particle swarm optimization). [ABSTRACT FROM AUTHOR]

With the emergence of the multicore architecture and the increase of chip operating frequency, power optimization has become a key step of circuit logic synthesis. Aiming at the XNOR/OR circuits, with the goal of minimizing power, construct the optimal polarity fixed-polarity Reed–Muller (FPRM) circuits power optimization scheme. However, the power optimization for FPRM circuits is a multipeak combinatorial optimization problem, we first propose a metaheuristic optimization algorithm (MOA), which includes the global exploration optimizer, local deep exploitation optimizer, and initial population and uses the proposed differential evolution optimization, fierce wolf siege algorithm-based tabu search, and improved skew tent map to make the population evolve. Based on the proposed Huffman tree construction algorithm and MOA, we propose an efficient power optimization approach (EPOA) to find the minimum power FPRM circuit. Experimental results on the benchmark circuits confirm the effectiveness of EPOA. [ABSTRACT FROM AUTHOR]

This work presents an automated design methodology for time-mode proportional and integral (PI) controllers aimed for an on-chip switched-capacitor (SC) dc/dc converter system. The basis of this design is the use of evolutionary optimization algorithms to find the near-optimal set of sizings for the time-mode PI controller. It is motivated due to the difficulty faced when tuning the controller parameters at a circuit level, which arise as a result of the presence of modeling inaccuracies and the small region for the linearized model where it is defined. Moreover, this design proposes the required modifications for the original design presented for the inductor-based dc/dc converter. These modifications are necessary to operate the SC dc/dc converter in slow switching limit (SSL). The addition of a pulse-width-modulated (PWM)-to-pulse frequency-modulated (PFM) conversion block is presented and elaborated in this article. The controller is codesigned using the differential evolution algorithm for the circuit level implementation to mitigate the issues prior mentioned. The optimized controller is then tested in a simulation environment using TSMC $0.18~\mu \text{m}$ technology. The results of the optimized controller were superior to those of a conventional controller. The optimized system achieved an overall efficiency of 79.1%. [ABSTRACT FROM AUTHOR]

Nonlinear equation systems (NESs) are ubiquitous, and solving them is an important yet challenging task in numerical computation. Most of the NESs usually contain multiple roots. To locate multiple roots, the use of evolutionary algorithms attracts more attention recently. For the purpose of finding different roots within a limited computational budget in a single run simultaneously, in this article, a hybrid niching-based differential evolution with two archives, referred to as HNDE/2A, is proposed. It can be featured as: 1) two niching techniques, i.e., crowding and speciation, are combined to balance the diversity and convergence; 2) a root archive is used to save the found roots during the run. Additionally, if a root is saved into the root archive, it will be reinitialized immediately to further promote the population diversity; and 3) an inferior offspring archive is presented to utilize the useful information of the inferior offspring. To evaluate the performance of our proposal, 30 problems are chosen as the test suite. Moreover, HNDE/2A is also used to solve two real-world problems. Compared with other algorithms, HNDE/2A yields better results in terms of the root ratio and the success rate with a less computational budget. [ABSTRACT FROM AUTHOR]

Randomized neural networks (NNs), such as random vector functional link (RVFL) and extreme learning machine (ELM), have been widely applied in various classification problems owing to their computational efficiency and universal approximation capability. However, such approaches are designed for regular Euclidean data and lack the ability to generalize to complex structured data. Moreover, their randomly generated parameters often lead to a suboptimal decision boundary with a growing requirement of hidden neurons. In this article, we first propose a plain framework, termed randomized graph convolutional networks (RGCNs), to generalize the classic randomized NNs to the non-Euclidean domain. Then, a hybrid framework called evolution-driven RGCN (EvoRGCN) is presented by using adaptive differential evolution with novelty search strategy to seek the globally optimal graph embedding for the plain RGCN. Finally, we recast the classic ELM and RVFL under the proposed frameworks, resulting in four novel semi-supervised models, including the plain models [i.e., graph convolutional extreme learning machines (GCELMs) and graph convolutional RVFL (GCRVFL)] and the optimized models (i.e., O-GCELM and O-GCRVFL). We show that our approaches are the natural generalization of the traditional randomized NNs in the non-Euclidean domain. Furthermore, our approaches not only retain the advantages of the classic approaches but also enable them to handle graph data. We compare our approaches against many existing methods across regular datasets and graph benchmarks, demonstrating that the proposed approaches dramatically outperform the compared methods with better generalization ability and robustness. Particularly, we quantitatively show the performance ranking of different randomized NNs, i.e., O-GCRVFL $> $ O-GCELM $\approx $ GCRVFL $> $ GCELM $\approx $ RVFL $> $ ELM. [ABSTRACT FROM AUTHOR]

The multimission selective maintenance problem (MSMP) for repairable systems has received increasing attention in recent years. The problem amounts to selecting a subset of feasible maintenance actions, in view of the resource limitations. For considering the realistic case of the imperfect maintenance, this article introduces a hybrid imperfect maintenance model, which is more realistic to evaluate the system reliability. The challenge of solving such kind of problems lies not only in the reliability estimation, but also in the solution method of the maintenance selection. Such decision-making problem can be effectively formulated using the Markov decision process, but it is difficult to apply current methods for solving the engineering systems with large action decision spaces. In order to solve this issue, this work puts forth a novel hybrid algorithm for the MSMP in a large multicomponent system. In the proposed method, a discrete differential evolution algorithm is developed for searching the optimal maintenance action in large-scale discrete action spaces and the deep Q-network method is utilized to approximate the effectiveness of maintenance actions and facilitate the agent training. The experiments, based on a large-scale coal transportation system, verify the effectiveness of the proposed method compared with LSDQN and differential evolution. [ABSTRACT FROM AUTHOR]

An effective electromagnetic interference (EMI) source reconstruction method based on dynamic differential evolution (DDE) is proposed in this article. In this method, an unknown EMI source is represented by an equivalent dipole array by using the scanned phaseless magnetic near fields. Different from the conventional differential evolution (CDE) method, DDE individually updates every equivalent dipole instead of the whole dipole array for each iteration. It converges faster and takes less computing time than CDE. The equivalent dipole model reconstructed by DDE can thereafter predict the near field and 3-m far field efficiently. The results of numerical and measurement examples have verified the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

The incremental capacity analysis (ICA) method is a promising method in battery state of health (SOH) estimation studies. The incremental capacity (IC) curve determination is one of the critical parts of the ICA method. However, the uncertain and incomplete charging conditions of real-world electric vehicles (EVs) significantly limit the IC curve determination. This article provides a thorough analysis of the practicality and limitations of four IC curve determination methods based on the datasets collected from real-world operating EVs with a comprehensive comparison scheme. The Lorentz function fitting method is improved by the differential evolution algorithm, breaking the limitation of fixed parameter boundary constraints. A novel PCHIP method is further proposed to determine the IC curve, with higher robustness to realistic uncertain and incomplete charging conditions. The proposed method is validated by real-world data from 40 EVs with low sampling frequency. The results show the extracted features from the IC curves, determined by the proposed method have a stronger correlation with the SOH, allowing the accurate SOH estimation with a 2% error. With less computational resources and sampling frequency requirements, this method shows great potential for the realistic battery management system implementation. [ABSTRACT FROM AUTHOR]

Recent studies show that deep neural networks are vulnerable to adversarial attacks in the form of subtle perturbations to the input image, which leads the model to output wrong prediction. Such an attack can easily succeed by the existing white-box attack methods, where the perturbation is calculated based on the gradient of the target network. Unfortunately, the gradient is often unavailable in the real-world scenarios, which makes the black-box adversarial attack problems practical and challenging. In fact, they can be formulated as high-dimensional black-box optimization problems at the pixel level. Although evolutionary algorithms are well known for solving black-box optimization problems, they cannot efficiently deal with the high-dimensional decision space. Therefore, we propose an approximated gradient sign method using differential evolution (DE) for solving black-box adversarial attack problems. Unlike most existing methods, it is novel that the proposed method searches the gradient sign rather than the perturbation by a DE algorithm. Also, we transform the pixel-based decision space into a dimension-reduced decision space by combining the pixel differences from the input image to neighbor images, and two different techniques for selecting neighbor images are introduced to build the transferred decision space. In addition, six variants of the proposed method are designed according to the different neighborhood selection and optimization search strategies. Finally, the performance of the proposed method is compared with a number of the state-of-the-art adversarial attack algorithms on CIFAR-10 and ImageNet datasets. The experimental results suggest that the proposed method shows superior performance for solving black-box adversarial attack problems, especially nontargeted attack problems. [ABSTRACT FROM AUTHOR]

In recent years, NAND flash memory has been widely used in mobile devices, laptops, desktops, and data center storage systems due to its low-power consumption, high performance, high density, lightweight, shock resistance, and high-reliability natures. However, as the stacked layers and the storage density increase, flash memory also suffers from a higher raw bit error rate (RBER) and shorter lifetime. Observing that reliable cell states suffer from less data retention errors and program disturbance, we propose a differential evolution coding scheme to increase the probability of storing data in more reliable cell states, thereby reducing the RBER. We conducted the experiments over a development platform of SSD storage device with 3D-TLC charge trap (CT) NAND flash memory. The experimental results showed that the proposed differential evolution algorithm with asymmetric coding scheme could averagely reduce RBER by 48.88%, 65.45%, 52.61%, 61.99%, 80.19%, and 33.18% compared with baseline, asymmetric coding algorithm, asymmetric coding scheme with stripe-pattern elimination algorithm, UAC- $n$ LC, word-line batch score modulation programming, and SCB schemes. [ABSTRACT FROM AUTHOR]

A growing number of organizations are hosting their software applications in distributed data centers (DCs) in the cloud, for faster response time and higher energy efficiency. The dramatic increase of user tasks, however, poses a significant challenge on DC providers to retain users’ expectations on both aspects. To tackle this challenge, this work first formulates the problem into a constrained biobjective optimization problem. A biobjective algorithm, named simulated-annealing-based adaptive differential evolution (SADE), is presented to simultaneously reduce both the response time of tasks and energy cost. Meanwhile, a method of minimal Manhattan distance is adopted to search for a final knee, for achieving a good balance between response time minimization and energy cost reduction. Experimental results on real-life datasets, i.e., the electricity prices and tasks collected from a Google cluster trace, have proved that SADE yields less task response time and lower energy cost compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Affine nonlinear systems are special kinds of nonlinear systems, and they can represent many practical systems. Dealing with affine nonlinear system based on T–S fuzzy model has drawn much attention in the literature recently. In this article, a robust control method is proposed for affine nonlinear system with disturbance based on T–S fuzzy model. First, an optimization T–S fuzzy model is constructed focusing on optimizing the parameters of the weight functions by using differential evolution algorithm. Second, in order to reconstruct the disturbance, an interval observer is designed for the affine nonlinear system, and then a novelty disturbance reconstruction method based on the interval observer is developed. The disturbance reconstruction value can asymptotically approach the actual disturbance value, and furthermore, it decouples the control input. Third, by taking the optimization T–S fuzzy model as design model and by introducing the reconstruction of disturbance into controller, a feedback robust controller with disturbance compensation is designed to stabilize the affine nonlinear system. Finally, a simulation example of the crane model is given and some comparisons are provided to verify the effectiveness and show the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

A new dual-mechanical-port (DMP) electric machine for hybrid electric vehicle applications, particularly in the power-split continuously variable transmission systems, is proposed in this paper. In order to comprehensively and quantitatively evaluate the pros and cons of the proposed machine, a comparative study of four DMP electric machines with different topologies is conducted. These four investigated DMP electric machines include a conventional DMP machine, a DMP machine with spoke-type permanent magnets, a DMP machine with reluctance rotor, and a DMP machine with open slots which is the proposed machine in this paper. Even though these four machines have similar topologies, they have different operating principles, which are demonstrated in detail. The comparison results indicate that the DMP machine with open slots outperforms the others in terms of torque/power density, efficiency, magnet utilization, etc. Accordingly, the DMP machine with open slots is selected for further investigation and optimization. A large-scale multi-objective optimization is carried out for this machine, where the differential evolution algorithm serves as a global search engine to target optimal performance. Finally, an optimal design is prototyped, and the experimental results are performed to verify the effectiveness of the analysis and simulation results in this paper. [ABSTRACT FROM AUTHOR]

In this article, a design and synthesis method to realize filtering phase shifter with tunable insertion phase and passband center frequency is proposed. The filtering phase shifter is based on a circuit consisting of three resonators in a triplet topology. Coupling matrix analysis is employed to obtain the detailed relationship between the insertion phase and the amplitude response of the filter. Then, differential evolution (DE) algorithm-based optimization method is proposed to obtain the coupling matrix with an arbitrary prespecified insertion phase. The proposed optimization method can not only obtain the coupling matrix with desired insertion phase but also can align the phase slope of different phase states, resulting in a low-phase error over the filter passband. Furthermore, a method is proposed to extend the phase shift range to full 360° by switching the signs of specific terms in the coupling matrix, significantly simplifying the required optimization procedure. Finally, a filtering phase shifter using lumped elements is designed, fabricated, and measured to validate the design concept. The phase-shifting range of the circuit can achieve 360° with continuously reconfigurable center frequency covering from 650 to 860 MHz. [ABSTRACT FROM AUTHOR]

Inspired by the evolution of biological systems, genetic algorithms have been applied to generate solutions for optimization problems in a variety of scientific and engineering disciplines. For a given problem, a suitable genome representation must be defined along with a mutation operator to generate subsequent generations. Unlike natural systems, which display a variety of complex rearrangements (e.g., mobile genetic elements), mutation for genetic algorithms commonly utilizes only random pointwise changes. Furthermore, generalizing beyond pointwise mutations poses a key difficulty as useful genome rearrangements depend on the representation and problem domain. To move beyond the limitations of manually defined pointwise changes, here we propose the use of techniques from masked language models to automatically generate mutations. As a first step, common subsequences within a given population are used to generate a vocabulary. The vocabulary is then used to tokenize each genome. A masked language model is trained on the tokenized data in order to generate possible rearrangements (i.e., mutations). In order to illustrate the proposed strategy, we use string representations of molecules and use a genetic algorithm to optimize for drug-likeness and synthesizability. Our results show that moving beyond random pointwise mutations accelerates genetic algorithm optimization. [ABSTRACT FROM AUTHOR]

Knowledge transfer plays a vastly important role in solving multitask optimization problems (MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality solution from one task to other tasks to enhance the optimization ability, which, however, may not work well or even have a negative effect if the tasks have very different task-specific knowledge. Hence, this article proposes a meta-knowledge transfer (MKT)-based differential evolution (MKTDE) algorithm by using a more general MKT method to solve MTOPs more efficiently. The meta-knowledge defined in this article refers to the knowledge that can evolve task-specific knowledge during the evolutionary search. That is, the meta-knowledge is a kind of “knowledge of knowledge,” which denotes the knowledge of “how to solve problem via evolution” and “the feature/way/method of evolving high-quality solution.” The evolutionary search for solving different tasks can share common meta-knowledge even though these tasks involve heterogeneous data and have very different task-specific knowledge. Therefore, the MKT can associate the heterogeneous multisource data of different tasks via transferring the meta-knowledge to help solve MTOPs more efficiently in a more general way. Moreover, to further enhance the MKTDE, two novel and efficient methods are proposed. One is multiple populations for the multiple tasks framework using a unified search space for making knowledge transfer flexibly. The other is an elite solution transfer method for achieving positive high-quality solution transfer. The superior performance of the proposed MKTDE is verified via extensive numerical experiments on both widely used MTOP benchmark problems and real-world robot navigation problems, with comparisons with some state-of-the-art and the latest well-performing algorithms. [ABSTRACT FROM AUTHOR]

Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, thereby reducing the time cost of the optimization process. However, with the increased number of decision variables in EMOPs, the prediction accuracy of surrogate models will deteriorate, which inevitably worsens the performance of SAEAs. To deal with this issue, this article suggests an ensemble surrogate-based framework for tackling EMOPs. In this framework, a global surrogate model is trained under the entire search space to explore the global area, while a number of surrogate submodels are trained under different search subspaces to exploit the subarea, so as to enhance the prediction accuracy and reliability. Moreover, a new infill sampling criterion is designed based on a set of reference vectors to select promising samples for training the models. To validate the generality and effectiveness of our framework, three state-of-the-art evolutionary algorithms [nondominated sorting genetic algorithm III (NSGA-III), multiobjective evolutionary algorithm based on decomposition with differential evolution (MOEA/D-DE) and reference vector-guided evolutionary algorithm (RVEA)] are embedded, which significantly improve their performance for solving most of the test EMOPs adopted in this article. When compared to some competitive SAEAs for solving EMOPs with up to 30 decision variables, the experimental results also validate the advantages of our approach in most cases. [ABSTRACT FROM AUTHOR]

The double-sided tubular linear machine (DSTLM) has a more compact structure than general single-sided motors, resulting in a higher power density, typically at the cost of a higher cogging force. To reduce this aspect and make it more suitable for applications in wave energy conversion systems or elevator driver systems, a new tooth-cutting topology is proposed in this article. The Schwarz–Christoffel mapping method is used to model and analyze the magnetic field and cogging force of the DSTLM. Based on the analytical model and the relationships between the cutting dimensions and the force performances, optimization is carried out by using a differential evolution algorithm. The optimization and the experiment results show that the proposed tooth-cutting topology can effectively reduce the cogging force at the small cost of weakening average thrust. [ABSTRACT FROM AUTHOR]

Bistatic synthetic aperture radar (BiSAR) has received more and more attention because of its forward-looking imaging capability and configuration flexibility. For BiSAR moving target imaging (MTIm), its noncooperative motion leads to unknown range cell migration (RCM) and additional phase modulation. Consequently, MTIm in BiSAR faces two main challenges: 1) the unknown RCM correction and Doppler parameter estimation are tightly coupled and 2) the Doppler parameters of the extended moving target’s different scattering points are different, i.e., the Doppler parameters are spatially variant. To cope with these problems, an optimal polar format refocusing method for bistatic SAR MTIm is proposed. First, the main part of tight coupling and spatial variation effects caused by the BiSAR platforms is eliminated, while the moving target is 2-D defocused and shifted. Then, we analyze the characteristics of 2-D defocused and shifted of the moving target in BiSAR and give the analytical expressions. Basis this, a new bistatic polar format transformation is introduced, in which the degree of freedom of defocusing result is reduced from 2-D to only 1-D. After that, the parameter estimation and refocusing issues are transformed into a constrained optimization problem (COP), and differential evolution (DE) is applied to solve the COP and obtain the refocusing results. Finally, considering the spatial variation of the extended moving target, compensation processing is performed to relocate each scattering point. Numerical simulations verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The continuing growth in space debris has posed a great threat to on-orbit operations. It is urgent to implement reliable and lasting monitoring of space debris. Safety and diversity in monitoring devices and business demands makes scheduling system resources increasingly complicated. This article proposes a novel adaptive multipopulation differential evolutionary algorithm based on a theoretical model specialized in the scheduling of space debris monitoring resources. Using Q-learning, the proposed algorithm adapts self-learning and dynamic adjustment properties in population proportion parameters. Experiments are performed with practical batch tasks and monitoring data to verify the effectiveness and reliable utility of the proposed algorithm to ensure the safety of on-orbit operation. [ABSTRACT FROM AUTHOR]

To efficiently implement space debris removal missions on geostationary Earth orbit (GEO), a scheduling optimization scheme of multiple hybrid-propulsive servicing spacecraft (SSc) is proposed. In this scheme, a many-to-many task allocation coding model is established to formulate the removal sequences of debris targets with respect to different SSc. And a hybrid-propulsive orbit transfer strategy is developed to implement the debris removal maneuvers to save the propellant consumption and removal time simultaneously via using both electric and chemical propulsion systems. Then, the multiple GEO space debris removal scheduling optimization problem is formulated to minimize the total propellant consumption by exploring the optimal removal sequences and orbit maneuver parameters, subject to the available velocity increment, removal time, and many-to-many task allocation constraints. To effectively solve the scheduling optimization problem involving discrete-continuous mixed variables, a clustering based adaptive differential evolution algorithm with potential individual reservation (CADE-PIR) is proposed. In CADE-PIR, the k-means clustering method and potential individual reservation mutation strategy are employed to promote the convergence rate and performance of exploration and exploitation. In the end, a real-world GEO space debris removal mission with multiple SSc is investigated to demonstrate the effectiveness and practicality of the proposed optimization scheme. [ABSTRACT FROM AUTHOR]

Previous research proposed the uniform mutation inside the sphere as a new mutation operator for evolution strategies (continuous evolutionary algorithms), with a case study of the elitist algorithm on the SPHERE. For that landscape, one-step success probability and expected progress were estimated analytically, and further proved to converge, as space dimension increases, to the corresponding asymptotics of the algorithm with normal mutation. This article takes the analysis further by considering the RIDGE, an asymmetric landscape almost uncovered in the literature. For the elitist algorithm, estimates of expected progress along the radial and longitudinal axes are derived, then tested numerically against the real behavior of the algorithm on several functions from this class. The global behavior of the algorithm is predicted correctly by iterating the one-step analytical formulas. Moreover, experiments show identical mean value dynamics for the algorithms with uniform and normal mutation, which implies that the derived formulas apply also to the normal case. Essential to the whole analysis is $\theta $ , the inclination angle of the RIDGE. The behavior of the algorithm on the SPHERE and HYPERPLANE is also obtained, at the limits of the $\theta $ interval (0°, 90°]. [ABSTRACT FROM AUTHOR]

Machine learning-assisted global optimization methods for speeding up analog integrated circuit sizing is attracting much attention. However, often a few typical analog integrated circuit design specifications are considered in most relevant research. When considering the complete set of specifications, two main challenges are yet to be addressed: 1) the prediction error for some performances may be large and the prediction error is accumulated by many performances. This may mislead the optimization and fail the sizing, especially when the specifications are stringent and 2) the machine learning cost could be high considering the number of specifications, considerably canceling out the time saved. A new method, called efficient surrogate model-assisted sizing method for high-performance analog building blocks (ESSAB), is proposed in this article to address the above challenges. The key innovations include a new candidate design ranking method and a new artificial neural network model construction method for analog circuit performance. Experiments using two amplifiers and a comparator with a complete set of stringent design specifications show the advantages of ESSAB. [ABSTRACT FROM AUTHOR]

Solving constrained multiobjective optimization problems brings great challenges to an evolutionary algorithm, since it simultaneously requires the optimization among several conflicting objective functions and the satisfaction of various constraints. Hence, how to adjust the tradeoff between objective functions and constraints is crucial. In this article, we propose a dynamic selection preference-assisted constrained multiobjective differential evolutionary (DE) algorithm. In our approach, the selection preference of each individual is suitably switching from the objective functions to constraints as the evolutionary process. To be specific, the information of objective function, without considering any constraints, is extracted based on Pareto dominance to maintain the convergence and diversity by exploring the feasible and infeasible regions; while the information of constraint is used based on constrained dominance principle to promote the feasibility. Then, the tradeoff in these two kinds of information is adjusted dynamically, by emphasizing the utilization of objective functions at the early stage and focusing on constraints at the latter stage. Furthermore, to generate the promising offspring, two DE operators with distinct characteristics are selected as components of the search algorithm. Experiments on four test suites including 56 benchmark problems indicate that the proposed method exhibits superior or at least competitive performance, in comparison with other well-established methods. [ABSTRACT FROM AUTHOR]

Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncertain nature of financial time-series data. In contrast, online learning mode evolving-structured ML models could be promising for financial time-series forecasting. For real-time deployment of such models, low memory demand is a must. Besides, the model’s explainability plays a crucial role in forecasting financial time-series. Considering all the requirements, a rule-based autonomous neuro-fuzzy learning algorithm called the parsimonious learning machine (PALM) is proposed here to forecast time-varying stock indices. To provide efficient automation of the proposed algorithm by maintaining the model explainability in terms of limited number linguistic IF-THEN rules, two popular multiobjective evolutionary algorithms (MEAs), such as a real-coded genetic algorithm (GA) and a self-adaptive differential evolution (DE) algorithm are utilized here. In addition, fuzzy type-2 variants of PALMs’ are considered here due to better uncertainty handling capacity than their type-1 counterparts. To evaluate the proposed algorithm’s performance, the closing stock price of fifteen (15) different stock market indices are predicted here. From the results, it is observed that the MEA-based PALMs are performing better than the state-of-the-art benchmark online ML models and providing a rule-based explainable model to the end-user. [ABSTRACT FROM AUTHOR]

The enormous energy consumed by clouds becomes a significant challenge for cloud providers and smart grid operators. Due to performance concerns, applications typically run in different clouds located in multiple sites. In different clouds, many factors, including electricity prices, available servers, and task service rates, exhibit spatial variations. Therefore, it is important to manage and schedule tasks among multiple clouds in a high-quality-of-service and low-energy-cost manner. This work proposes a task scheduling method to jointly minimize energy cost and average task loss possibility (ATLP) of clouds. A problem is formulated and tackled with an adaptive biobjective differential evolution based on simulated annealing to determine a real-time and near-optimal set of solutions. A final knee solution is further chosen to specify suitable servers in clouds and task allocation among web portals. Simulation results based on realistic data prove that less average loss possibility of tasks, and smaller energy cost is obtained with it than its widely used peers. Note to Practitioners—This work considers joint optimization of both ATLP and average energy cost of all clouds. It is of great significance to execute tasks among multiple clouds by jointly allocating all tasks among multiple web portals and specifying suitable servers in different clouds. Yet, it is challenging to achieve joint optimization in a market where factors, including prices of electricity and available servers, show spatial variations. Current studies are coarse-grained and fail to jointly achieve average energy cost minimization and quality-of-service optimization of tasks. In this work, a novel algorithm named adaptive simulated-annealing-based biobjective differential evolution is proposed for an energy cost and quality-of-service-optimized task scheduling strategy in a real-time manner. Experiments prove that it realizes lower energy cost and ATLP compared with its typical widely used peers. It can also be applied to other industrial areas, including smart manufacturing, Internet of Things, and smart city. [ABSTRACT FROM AUTHOR]

One of the most promising technologies to be employed in beyond 5G networks is based on the concept of cell-free (CF) massive MIMO, in which a predetermined set of access points (APs) jointly cooperate in the data transmission and reception to/from the user equipment (UE). In order to efficiently manage radio resources, the CF central processing unit implement uplink power control policies. These policies aim to optimize a given network utility function. In this paper, we investigate the max-min fairness optimization problem, in which the spectral efficiency performance of the UE with the worst channel conditions is prioritized, taking into account a per-UE power constraint and assuming linear maximum ratio combining at APs. Existing solutions have typically relied on second-order cone programming with convex approximations, which exhibit high computational complexity and scalability issues. Therefore, meta-heuristics (MHs) are explored as alternative optimization schemes, capable of providing (near)-optimal solutions with reasonable computational effort. Three MH approaches with different operation principles are compared. Numerical results show that the differential evolution algorithm exhibits the best trade-off between solution quality and run time, being able to reach (near)-optimal solutions faster than the bisection approach while coping with the scalability issues of the geometric programming-based algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel technique for human motion denoising by jointly optimizing kinematic and anthropometric constraints for a noisy skeleton data. Specifically, we are focused on depth-sensor-based motion capture (D-Mocap) data that are often prone to error, outliers and distortion. To capture human kinematics, we first propose a joint-level Tobit particle filter (TPF) that incorporates a unique observation model to characterize the censored measurement of D-Mocap data. A skeleton-level Differential Evolution (DE) algorithm is then integrated with the sequential Monte Carlo sampling in the TPF, allowing joint-level particles to be re-distributed and re-weighted according to the stability and consistency of skeletal bone lengths as well as the suitability of joint kinematics. This leads to an integrated TPF-DE algorithm that significantly improves the quality of D-Mocap data by making 3D joint trajectories more kinematically admissible and anthropometrically stable. Experimental results on both simulated and real-world D-Mocap show that the errors of joint positions and the bone lengths have been reduced by 30-60%, and the accuracy of joint angles has been improved by 40-60%. The proposed TPF-DE method outperforms the recent filtering-based and deep learning methods and demonstrate the synergy between the TPF and DE algorithms for effective human motion enhancement. [ABSTRACT FROM AUTHOR]

Quantum-inspired differential evolution (QDE) is an evolutionary algorithm, which can effectively solve complex optimization problems. However, sometimes, it easily leads to premature convergence and low search ability and falls to local optima. To overcome these problems, based on the MSIQDE (improved QDE with multistrategies) algorithm, an enhanced MSIQDE algorithm based on mixing multiple strategies, namely, EMMSIQDE is proposed in this article. In the EMMSIQDE, a new differential mutation strategy of a difference vector is proposed to enhance the search ability and descent ability. Then, a new multipopulation mutation evolution mechanism is designed to ensure the relative independence of each subpopulation and the population diversity. The feasible solution space transformation strategy is used to achieve the optimal solution by mapping the quantum chromosome from a unit space to solution space. Finally, some multidimensional unimodal and multimodal functions are selected to demonstrate the optimization performance of EMMSIQDE. The results demonstrate that the EMMSIQDE is significantly better than the DE, QDE, QGA, and MSIQDE, and has better optimization ability, scalability, efficiency, and stability. [ABSTRACT FROM AUTHOR]

Current pulses are convenient to be actively implemented by a battery management system. However, the short-term features (STF) from current pulses originate from various sensors with uneven qualities, which hinder one powerful and strong learner with STF for the battery state of health (SOH) estimation. This article, thus, proposes an optimized weak learner formulation procedure for lithium-ion battery SOH estimation, which further enables the automatic initialization and integration of the weak learners with STF into an efficient SOH estimation framework. A Pareto front-based selection strategy is designed to select the representative solutions from the nondominated solutions fed by a knee point driven evolutionary algorithm, which guarantees both the diversity and accuracy of the weak learners. Afterward, the weak learners, whose coefficients are obtained by self-adaptive differential evolution, are integrated by a weight-based structure. The proposed method utilizes the weak learners with STF to boost the overall performance of the SOH estimation. The validation of the proposed method is proved by LiFePO4/C batteries under accelerated cycling ageing test including one mission profile providing primary frequency regulation service to the grid and one constant current profile. [ABSTRACT FROM AUTHOR]

The degradation data of highly reliable products are usually analyzed by stochastic process models, such as Wiener process, gamma process and inverse Gaussian process models. If such a specific degradation model is wrongly assumed, then poor analysis results of reliability assessment would be obtained. Therefore, a class of exponential-dispersion processes, named Tweedie exponential-dispersion process (TEDP), is proposed to describe the products’ degradation paths. The TEDP model which comprises the aforementioned stochastic processes as its special cases, is more flexible and applicable for degradation modeling. Considering the nonlinear characteristics of degradation paths and the unit-to-unit variability among the product units, random-effect models are established based on the nonlinear TEDP models with random drift and dispersion parameters. To improve the mathematical tractability of these models, the variational inference, expectation maximization algorithm and differential evolution algorithm are used to estimate the unknown model parameters. Furthermore, two nonlinear TEDP models with accelerated factors and random effects are developed for accelerated degradation analysis. Finally, a simulation study and three real applications are presented to show the effectiveness and superiority of the proposed models and methods. [ABSTRACT FROM AUTHOR]

The aim of this research is to present the studies carried out to design three-phase core type distribution transformers, with the aid of optimization techniques. The minimization of losses is presented in two steps, first through the use of a mono-objective function, and second with the use of multi-objective function to minimize losses and the total mass of the active part of the transformer. The algorithms used are: Differential Evolution and Particle Swarm Optimization, their performances are compared through the results obtained. The main project parameters, such as core dimensions, total losses, no load current and the energizing current are estimated analytically through OCTAVE software. The analysis of magnetic flux density in the core is simulated using the Finite Element Method. Multiobjective optimization allows working with two or more conflicting objectives, and at each iteration, it stores the various non-dominant Pareto Front solutions, helping designers to choose the solution that best meets their needs. The results obtained with the mono-objective and multiobjective optimization techniques were interesting to minimize the losses and/or cost of the project [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) includes numerous sensing nodes that constitute a large scale-free network. Optimizing the network topology to increase resistance against malicious attacks is a complex problem, especially on 3-dimension (3D) topological deployment. Heuristic algorithms, particularly genetic algorithms, can effectively cope with such problems. However, conventional genetic algorithms are prone to falling into premature convergence owing to the lack of global search ability caused by the loss of population diversity during evolution. Although this can be alleviated by increasing population size, the additional computational overhead will be incurred. Moreover, after crossover and mutation operations, individual changes in the population are mixed, and loss of optimal individuals may occur, which will slow down the population’s evolution. Therefore, we combine the population state with the evolutionary process and propose an Adaptive Robustness Evolution Algorithm (AREA) with self-competition for scale-free IoT topologies. In AREA, the crossover and mutation operations are dynamically adjusted according to population diversity to ensure global search ability. A self-competitive mechanism is used to ensure convergence. We construct a 3D IoT topology that is optimized by AREA. The simulation results demonstrate that AREA is more effective in improving the robustness of scale-free IoT networks than several existing methods. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms and swarm intelligence algorithms have been widely used for constrained optimization problems for decades and numerous techniques for constraint handling have been proposed. The ${\varepsilon }$ -constrained method is a very effective one. In the literature, the ${\varepsilon }$ value was usually controlled via an exponential function, which is not competent for solving certain types of constrained optimization problems, e.g., whose global optima are located near the boundary of the feasible and infeasible regions. To solve this problem, this article proposes a new adaptive ${\varepsilon }$ control method and incorporate it into a basic differential evolution (DE) algorithm: (DE/rand/1/exp). Based on the information of constraint violation in the current population, the adaptive method controls the value of ${\varepsilon }$ through a simple heuristic rule. Compared with the traditional exponential function-based control methods, the proposed adaptive method can prevent the algorithm from being trapped into local optima while retaining the obtained near-optimal candidate solutions in the infeasible region for generating promising searching paths. Besides, we set the crossover rate (CR) as a more reasonable value for DE/rand/1/exp, which can enhance the efficiency significantly. The well-known 2006 IEEE Congress on Evolutionary Computation (CEC 2006) competition on real-parameter single-objective constrained optimization benchmark is adopted to evaluate the effectiveness of the proposed adaptive ${\varepsilon }$ -constrained DE. Fifteen constrained engineering optimization problems are collected from the literature to test the proposed algorithm. Moreover, the adaptive ${\varepsilon }$ control method is extended to an adaptive algorithm to solve the benchmark problems from CEC 2017. The comparison results confirm the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this article, a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel inverter (MLI). The APSO-GA is applicable to all levels of MLI. In the proposed method, ring topology based APSO is hybrid with GA. APSO is applied for exploration and GA is used for the exploitation of the best solutions. In this article, optimized switching angles are calculated using APSO-GA for seven-level and nine-level inverter, and results are compared with GA, PSO, APSO, bee algorithm (BA), differential evolution (DE), synchronous PSO, and teaching–learning-based optimization (TLBO). Simulation results show that APSO-GA can easily find feasible solutions particularly when the number of switching angles is high; however, the rest of all stuck at local minima due to less exploration capability. Also, the APSO-GA is less computational complex than GA, BA, TLBO, and DE algorithms. Experimentally, the performance of APSO-GA is validated on a single-phase seven-level inverter. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) have been recognized as a promising approach for bilevel optimization. However, the population-based characteristic of EAs largely influences their efficiency and effectiveness due to the nested structure of the two levels of optimization problems. In this article, we propose a transfer learning-based parallel EA (TLEA) framework for bilevel optimization. In this framework, the task of optimizing a set of lower level problems parameterized by upper level variables is conducted in a parallel manner. In the meanwhile, a transfer learning strategy is developed to improve the effectiveness of each lower level search (LLS) process. In practice, we implement two versions of the TLEA: the first version uses the covariance matrix adaptation evolutionary strategy and the second version uses the differential evolution as the evolutionary operator in lower level optimization. The experimental studies on two sets of widely used bilevel optimization benchmark problems are conducted, and the performance of the two TLEA implementations is compared to that of four well-established evolutionary bilevel optimization algorithms to verify the effectiveness and efficiency of the proposed algorithm framework. [ABSTRACT FROM AUTHOR]

This research presents an original and efficient elite archive-assisted adaptive memetic algorithm (EAMA) to deal with a realistic hybrid differentiation flowshop scheduling problem (HDFSP) with the objective of total completion time minimization. In this scheduling problem, each job consists of multiple parts and the jobs are divided into different types. The manufacturing of a job is comprised of three consecutive stages: 1) parts fabrication on first-stage parallel machines; 2) parts assembly on second-stage single machine; and 3) job differentiation on one of third-stage dedicated machines. We provide a mixed-integer programming model, derive three lower bounds, and further present the EAMA metaheuristic for HDFSP. The EAMA is initialized heuristically, and its global exploration is performed by a differential evolution, which includes three newly designed operators: 1) elite-driven discretized differential mutation; 2) probability crossover; and 3) biased selection. To enhance the local search, an external elite archive is set and evolved in parallel with global exploration by a meta-Lamarckian learning-based adaptive multistage local search and a variable length-based adaptive block-insertion local search. After the global exploration and local exploitation, an elite sharing strategy is used to exchange the excellent information between population and elite archive, and an adaptive restart strategy is used to diversify the population. The influence of parameter setting on EAMA is surveyed by using an improved design-of-experiment. The statistical results from extensive computational experiments demonstrate the effectiveness of the special designs and show that EAMA performs more efficient than the existing algorithms in solving the problem under consideration. [ABSTRACT FROM AUTHOR]

In recent decades, recommender systems have been well studied and widely applied. However, most recommenders unilaterally optimize the results from the buying customers’ views without considering expectations of other participants, e.g., merchants. Unfortunately, the expectations of customers and merchants in recommendation are different or even conflicted. Especially for popular group-trading markets, customers and merchants are competing in trading, i.e., customers want to meet their preferences or obtain gains with personal favorite items, while merchants want to recommend wholesale items with setting group-trading terms or conditions. In addition, some practical constraints are not fully considered by prior systems. In this article, we propose a cooperative–competitive evolutionary algorithm (i.e., CoEA) for the bidirectional recommendations in group-trading markets. Specifically, we, respectively, formalize two subproblems with designed objectives for two-sided participants in markets, and integrate the cooperative–competitive optimizations into one framework. Second, CoEA designs a binary encoding matrix for individual representation to integrate the two subproblems. Furthermore, by assembling game evolution process, CoEA designs cooperative–competitive evolution operators, i.e., the cooperative crossover and competitive mutation, which guide the solutions to equilibrium by, respectively, bridging communication between two populations of subproblems and optimizing distinctive objective in each population. Finally, we construct two real applications involving bidirectional recommendations, i.e., the group buying and P2P lending, and conduct extensive experiments with the real-world datasets. By comparing CoEA with several representative recommendation algorithms and evolutionary algorithms, the experimental results clearly demonstrate the effectiveness of CoEA. [ABSTRACT FROM AUTHOR]

Differential evolution is a powerful nature-inspired real-parameter optimization algorithm that has been successfully used to solve a number of hard optimization problems. It has been used to tackle both continuous and discrete optimization problems. The application of a continuous method to discrete problems involves several challenges, including solution representation and search space–solution space mapping. In this work, we study random key encoding, a popular encoding scheme that is used to represent permutations in high-dimensional continuous spaces. We analyze the search space it constitutes, study its structure and properties, and introduce two novel modifications of the encoding. We investigate the proposed encoding strategies in the context of four variants of the differential evolution algorithm and demonstrate their usefulness for two widespread permutation problems: 1) the linear ordering problem and 2) the traveling salesman problem. [ABSTRACT FROM AUTHOR]

Bayesian optimization is a promising methodology for analog circuit synthesis. However, the sequential nature of the Bayesian optimization framework significantly limits its ability to fully utilize real-world computational resources. In this article, we propose an efficient parallelizable Bayesian optimization algorithm via multiobjective acquisition function ensemble (MACE) to further accelerate the optimization procedure. By sampling query points from the Pareto front of the probability of improvement (PI), expected improvement (EI), and lower confidence bound (LCB), we combine the benefits of state-of-the-art acquisition functions to achieve a delicate tradeoff between exploration and exploitation for the unconstrained optimization problem. Based on this batch design, we further adjust the algorithm for the constrained optimization problem. By dividing the optimization procedure into two stages and first focusing on finding an initial feasible point, we manage to gain more information about the valid region and can better avoid sampling around the infeasible area. After achieving the first feasible point, we favor the feasible region by adopting a specially designed penalization term to the acquisition function ensemble. The experimental results quantitatively demonstrate that our proposed algorithm can reduce the overall simulation time by up to $74\times $ compared to differential evolution (DE) for the unconstrained optimization problem when the batch size is 15. For the constrained optimization problem, our proposed algorithm can speed up the optimization process by up to $15\times $ compared to the weighted EI-based Bayesian optimization (WEIBO) approach, when the batch size is 15. [ABSTRACT FROM AUTHOR]

Acoustic remote sensing with multibeam echo-sounder systems (MBESs) has been extensively used for coastal and ocean survey works. The imperfect integration of multibeam echo sounder and motion sensor can introduce integration errors, which manifest as high-frequency wobbles in swaths and hinder the accurate expression of high-resolution seabed topographic maps. To address this issue, a precise method is developed to calibrate these integration errors based on a simplified georeferenced model. First, an equivalent attitude coordinate (EAC) system is defined to represent mutual transformation between equivalent attitudes and the beam launch vector; then, with the help of equivalent attitudes, a simplified footprint georeferenced model is deduced that considers the effect of the parameterized integration errors (including time delay, motion scale, yaw misalignment, and lever arm errors); finally, in a selected flat region, integration errors are inverted by regressing the bathymetric data to the corresponding fitted plane via the differential evolution (DE) algorithm. The results indicate that the proposed method can effectively eliminate wobbles in multibeam bathymetries caused by single or multiple integration errors in both shallow- and deep-water areas. By comparing the data before and after calibration with the in situ measurements, the accuracy of the calibrated shallow- and deep-water data is controlled within approximately 0.15% and 0.06% of the water depth, respectively. [ABSTRACT FROM AUTHOR]

Massive machine-type communications (mMTC) is one of the three generic services for the fifth-generation (5G) wireless communications system. To utilize fully the high rate transmission feature of the 5G system to support massive MTC devices (MTCDs), we propose a group-based random access and data transmission scheme, where the data packets of MTCDs are first aggregated by the MTC gateways (MTCGs) and then forwarded to the base station. The access process of the MTC network is divided into two phases, namely the intra-group transmission phase and MTCG forwarding phase. The entire resources are also partitioned for the two phases. We employ the discrete-time nonhomogenous Markov model to characterize the joint queue-length evolution of multiple MTCGs, which cannot be analyzed directly due to the exponential complexity and time-nonhomogeneity. To facilitate the analysis, we approximately decompose the joint nonhomogenous queue-length evolution process into multiple independent nonhomogenous queue-length evolution processes with the same state transition probabilities. Then, we establish an equivalent single queue-length evolution based homogenous Markov chain by constructing a virtual queue and determine the corresponding stationary distribution by using the Gauss-Jordan elimination method. An optimization problem is formulated to maximize the average network throughput subject to the constraints on the resource partition for the two phases and the MTCG forwarding threshold. By developing a modified differential evolution algorithm, we provide an efficient solution to the formulated problem, which can be arbitrarily close to the optimal solution. Simulation results show that the proposed scheme can efficiently improve the network performance over the existing schemes. [ABSTRACT FROM AUTHOR]

Periodic disturbances occur during repetitive operations, and compensation for the periodic disturbances is an important issue to realize precise machine works because the periodic disturbances deteriorate the control precision. In addition, the periodic disturbance becomes a frequency-varying periodic disturbance when the periodicity of the operations changes, which makes the compensation difficult. To eliminate the frequency-varying periodic disturbance, an adaptive periodic-disturbance observer (APDOB) was proposed. However, the APDOB has a problem that the design of the APDOB is complicated with six design parameters. This article proposes a differential evolutionary algorithm with local search that optimizes the six design parameters of the APDOB for the optimal frequency-varying periodic disturbance compensation. The proposed method based on a memetic algorithm framework can explore globally using the differential evolutionary algorithm and explore locally using the local search including the Lévy flight. Moreover, the proposed method can reduce the number of the design parameters. [ABSTRACT FROM AUTHOR]

In this article, a method for solving cable layout optimization problems in electronic systems based on ensemble learning (EL) and improved differential evolution (DE) algorithm is proposed. Using this method, we produced two cable layout schemes with the considerations of the electromagnetic compatibility performance and the total length of cables. First, several different individual machine learning (ML) models for all output variables were established. On this basis, we established EL models with better performance than previous individual ML models. Then, aiming at the optimization problem presented in this article, an improved DE algorithm was proposed. On this basis, a method for solving cable layout optimization problems in electronic systems based on EL and the improved DE algorithm was proposed. Finally, the effectiveness of the proposed method was verified by two examples of electronic system cable layout optimization with different routing modes. Each example problem was solved using four different methods. The results indicate that the method proposed here outperformed existing methods. [ABSTRACT FROM AUTHOR]

In this article, a 2-D bistatic attributed scattering center (BASC) model is developed to represent the bistatic return from a perfect electric conducting (PEC) target, based on the derived analytic scattering solutions of seven canonical primitives. Because of its unified and concise form, the BASC model is useful for extracting the geometrical features of targets from synthetic aperture radar (SAR) echoes. However, estimating the parameters of the BASC model is an ill-posed inverse problem. To reduce the ill-posedness, we present a sparse method for bistatic inverse scattering that incorporates differential evolution (DE) into doubly orthogonal matching pursuit (DOMP). The experimental results not only verify the validity of the parameter inversion method but also demonstrate the applicability of the proposed model. [ABSTRACT FROM AUTHOR]

Phase-only nulling with low sidelobe level is a problem of interest in array synthesis which is a tedious problem without an analytical solution. In this communication, a novel framework for the phase-only nulling based on radial basis function neural network (RBFNN) is proposed to predict the phase adjustment for the array pattern nulling with sidelobe control. In the process of network training, the parameters of the RBFNN are optimized simultaneously based on the self-adaptive differential evolution (SADE) algorithm, which aims to improve the approximation ability and reduce the complexity of the network. Simulation results of the optimized RBFNN models show compact network structure and form the array pattern under desired performance with good generalization capability. [ABSTRACT FROM AUTHOR]

In electric rotating machines, the stator winding design plays a crucial role on their overall performance. Novel approaches for winding design optimization, including multilayer configurations, stator shifting, and phase shifting, have been presented in recent studies. Most of them focus on a single strategy, which is generally applied to one winding topology only. In this paper, a unified and generalized methodology is proposed to solve a two-objective constrained winding design optimization problem, which can be applied to polyphase winding configurations with any number of slots per pole. The novelty concerns the combination of the phase shifting concept with an enhanced end-winding connection strategy, enabled by multiple coil pitches. The framework developed allows for the customization of the objective functions and constraints to the characteristics of the study. Two application examples are provided for validation purposes of the methodology, where time-step finite-element analysis simulations of a three-phase squirrel-cage induction motor and a six-phase permanent-magnet synchronous motor were carried out. The proposed methodology is capable of generating novel winding designs that can potentially improve the machine efficiency and reduce the amount of copper in the stator windings. [ABSTRACT FROM AUTHOR]