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This paper discusses the process of optimizing the shape of systems that are controlled by the Stokes flow with threshold leak boundary conditions. In the theoretical part it focuses on studying the stability of solutions to the state problem in relation to a specific set of domains. In order to facilitate computation, the slip term and impermeability condition are regulated. In the computational part, the optimized portion of the boundary is defined using Bézier polynomials, in order to create a finite dimensional optimization problem. The paper also includes numerical examples to demonstrate the computational efficiency of this approach. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to optimize passive damper system (PDS) design by configuring its parameters to improve its performance and behavior in permanent magnet synchronous machines (PMSM). Design/methodology/approach: First, the principle and effectiveness of the PDS are recalled. Second, the impact of different PDS parameters on its operation is analyzed. Third, a multi-objective optimization is proposed to explore a compromise design of PDS. Finally, the transient finite element method simulation is performed to validate the optimized design, which can ensure an excellent noise reduction effect and weaker negative impacts. Findings: A suitable capacitance value in PDS is a key to realizing the damping effect. A larger copper wire can improve the noise reduction performance of PDS and reduce its Joule losses. A compromise solution obtained from a multi-objective optimization remains the excellent noise reduction and reduces Joule losses. Originality/value: This paper explores the impact of PDS parameters on its operation and provides an orientation of PDS optimization, which is favorable to extend its application in different electrical machines. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents the determination of the maximum power point of a bifacial photovoltaic (PV) system using two different cell models. The optimal power point is determined by using genetic algorithm (GA), as an optimisation tool. The purpose of this paper is to find which of the two analysed models gives better results in the determination of the maximum power point of a bifacial PV system for different solar irradiations. The quality of the results gained from both models is analysed based on the value of the objective function. Design/methodology/approach: In this research work, the maximum power point of bifacial PV modules is determined by using two different PV cell models, such as the simplified and two-diode models of PV cells. Based on the input electrical data for the analysed bifacial PV module as well as the mathematical model of the two PV cell presentations, the values for the current and the voltage at the maximum power point for a given solar irradiation and working temperature are determined by the algorithm for each solution in the population and generation. Findings: From the presented results and the performed analysis, it can be concluded that GA is quite appropriate for this purpose and gives adequate results for both models and for all working conditions. The two-diode model was found to be more suitable compared with the simplified model due to its complexity. Therefore, although the power difference for each of the scenarios for the two compared models does not differ significantly among the two models, it is in favour of the two-diode model. Which implicates that the for fast and simple calculation the simplified model can also do the job. Practical implications: This approach can be very successfully applied in the design process of a PV plant to forecast the output characteristics of the PV system if there is enough information about the weather conditions for a given location. This procedure can be very helpful in the process of selection of right PV module and inverter for a given location. Originality/value: An optimisation technique using GA as an optimisation tool has been developed and successfully applied in the determination of the maximum power point for a bifacial PV module using to different models of solar cell. The results are compared with the analytically determined values as well as with the values given from the producer and they show good agreement. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to gain a better understanding on how metasurfaces behave, in terms of currents in each unit cell. A better knowledge of their behavior could lead to an ad-hoc design for specific applications. Design/methodology/approach: The methodology used is both theoretical and numerical; it is based on circuit theory and on an optimization procedure. Findings: The results show that when the knowledge of the current in each unit cell of a metasurface is needed, the most common approximations currently used are often not accurate. Furthermore, a procedure for the termination of a metasurface, with application-driven goals, is given. Originality/value: This paper investigates the distribution of the currents in a 2D metamaterial realized with magnetically coupled resonant coils. Different models for the analysis of these structures are illustrated, and the effects of the approximations they introduce on the current values are shown and discussed. Furthermore, proper terminations of the resonators on the boundaries have been investigated by implementing a numerical optimization procedure with the purpose of achieving a uniform distribution of the resonator currents. The results show that the behavior of a metasurface (in terms of currents in each single resonator) depends on different properties; as a consequence, their design is not a trivial task and is dependent on the specific applications they are designed for. A design strategy, with lumped impedance termination, is here proposed. [ABSTRACT FROM AUTHOR]

This paper introduces the Gooseneck Barnacle Optimisation Algorithm (GBO) as a novel evolutionary method inspired by the natural mating behaviour of gooseneck barnacles, which involves sperm casting and self-fertilization. GBO is mathematically modelled, considering the hermaphroditic nature of these microorganisms that have thrived since the Jurassic period. In contrast to the previously published Barnacle Mating Optimizer (BMO) algorithm, GBO more accurately captures the unique static and dynamic mating behaviours specific to gooseneck barnacles. The algorithm incorporates essential factors, such as navigational sperm casting properties, food availability, food attractiveness, wind direction, and intertidal zone wave movement during mating, creating two vital optimization stages: exploration and exploitation. Real-world case studies and mathematical test functions serve as qualitative and quantitative benchmarks. The results demonstrate that GBO outperforms well-known algorithms, including the previous BMO, by effectively improving the initial random population for a given problem, converging to the global optimum, and producing significantly better optimization outcomes. • Proposed Gooseneck Barnacle Optimizer mimics gooseneck barnacles' mating behaviour. • Inspiration: sperm casting, wind direction, intertidal wave action during mating. • Address the issues with original Barnacle Mating Optimizer. • Standard benchmark functions evaluate GBO's performance. • Hybridize with Least Square Support Vector Machine for real time-series forecasting. [ABSTRACT FROM AUTHOR]

Constrained integer stochastic optimization problems (CISOP) are optimization problems where the cost function is stochastic and the constraints are deterministic. Solving the CISOP using conventional optimization techniques is time-consuming when the design space is huge. Ordinal optimization offers an efficient technique suitable for solving the CISOP. In this paper, an approach that uses golden jackal optimization assisted by ordinal optimization (GJOO) is developed for resolving the CISOP in a relatively short time. The GJOO is composed of three phases: metamodel, global search, and ranking and selection. At first, the polynomial chaos expansion is used as a metamodel to rapidly assess a solution. Next, advanced golden jackal optimization is adopted to determine N excellent solutions from the entire design space. At last, the modified optimal computing budget allocation is adopted to determine a distinguished solution from the N excellent solutions. The GJOO is utilized to the shipping/receiving docks optimization problem of a container freight station in air cargo. The performances of the GJOO are compared with those of five meta-heuristic methods. Empirical results demonstrate the efficiency and robustness of the GJOO algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the Inner–Outer Direct Search (IODS) optimization technique is extended in the fuzzy environment to solve a fuzzy system of nonlinear equations. The proposed approach of fuzzy IODS converts the fuzzy system of nonlinear equations to an unconstrained fuzzy optimization problem. Then, the unconstrained fuzzy optimization problem is studied through the IODS technique. To validate the proposed algorithm, convergence analysis is performed. Further, three different fuzzy system of nonlinear equations are considered to demonstrate the algorithm. The fuzzy system of nonlinear equations is divided into various cases viz. fully fuzzy (when both the coefficient matrix and right-side vector are fuzzy) and only fuzzy (when either of the coefficient matrix or the right-side vector is fuzzy). For each individual case the numerical and graphical convergence of the obtained solutions were established. One electrical circuit problem is investigated to obtain the current in fuzzy environment and the same is compared with an existing method. It is observed that the closed form of the fuzzy solution contains the solution of the corresponding crisp system as well as the compared method. Finally, it can be noted that the proposed IODS approach can be applied to find the united fuzzy solutions for various science and engineering problems with easy implementation. [ABSTRACT FROM AUTHOR]

Label missing and class imbalance problems are two hot research topics in machine learning, and they have been impeding the improvement of model performance, especially in the multi-label learning. Although some existing methods have proven to be effective, they are suitable for only one case. How to effectively address above two issues simultaneously is a challenging problem. In this paper, we propose a novel model named Imbalanced and Missing multi-Label data learning with Global and Local structure (IMLGL) to address the aforementioned challenge. There are following three advantages. At the empirical risk level, we introduce the label correlation matrix C into the loss function and devise a dynamic weighting method to address the aforementioned challenge. At the data level, we analyze the structural characteristics of the data, and introduce local low-rank and global high-rank term to enhance the generalization performance of the model. At the label level, a smoothing term is also introduced for learning the constraint classifier coefficient matrix W . Our method utilizes alternative optimization technique and alternating minimization method for solving. Extensive experiments on six datasets demonstrate the competitiveness of our approach. [ABSTRACT FROM AUTHOR]

Concept factorization (CF) is a powerful tool in subspace learning. Recently graph-based CF and local coordinate CF have been proposed to exploit the intrinsic geometrical structure of data, and have been shown quite successful in improving performance. However, these methods have limited robustness and might be sensitive to noises and disturbances in practical applications. In this paper, we propose a novel robust sparse CF framework (RSCF) for subspace learning. Specifically, we present a robust loss function to effectively eliminate the impact of the large outliers. The local coordinate constraint and the graph regularization term are incorporated into RSCF to simultaneously guarantee the sparsity of the coefficient matrix and maintain the local structure of the data. We prove that the local coordinate constraint implies the orthogonality of the coefficient matrix. By using the half-quadratic optimization technique, we transform the objective function of RSCF into a quadratic form and provide the iterative updating rules and the convergence analysis. Extensive experiments demonstrate the robustness and superiority of the proposed RSCF in comparison to state-of-the-art CF methods. [ABSTRACT FROM AUTHOR]

In conventional sparse Space-Time Adaptive Processing (STAP) algorithms, the l 0 -norm is often used instead of the l 1 -norm to relax convexity constraints. The approach presented in this paper can be mathematically reformulated as a linear problem, which can be efficiently solved using various convex optimization techniques. While this method effectively circumvents the NP-Hard complexity associated with the l 0 -norm, it does come with certain potential challenges. For example, the observation matrix must meet the criteria of the Restricted Isometry Property (RIP) and other stringent conditions. In this research, a novel STAP algorithm is introduced, which is based on a non-convex penalty function. This innovative algorithm substitutes the traditional l 0 -norm with a custom-designed non-convex penalty function and is applied to the Direct Filter Processor (DFP), solved using the recursive least squares (RLS) algorithm. The resulting algorithm, known as g p -RLS, is demonstrated through Monte Carlo simulations to outperform other l 1 -based and reduced-rank STAP algorithms in terms of faster convergence, improved signal-to-clutter-plus-noise ratio (SCNR), and enhanced target detection performance. [ABSTRACT FROM AUTHOR]

Some mathematical theorems represent ideas that are discovered again and again in different forms. One such theorem is Hall's marriage theorem. This theorem is equivalent to several other theorems in combinatorics and optimization theory, in the sense that these results can easily be derived from each other. Remarkably, this equivalence extends to Strassen's theorem, a celebrated result on couplings of probability measures. In this paper the equivalence between Strassen's theorem and Hall's theorem is investigated from a combinatorial perspective. A novel combinatorial lemma will be introduced that can be used to deduce both Hall's theorem and a finite version of Strassen's theorem, providing a simple proof of their equivalence. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time cluster optimization problem of first-order multi-agent systems (MASs) under directed detail-balanced networks, in which the optimization objective is a linear combination of local objective functions. Two piecewise distributed control protocols are proposed to solve the optimization problem with time-invariant and time-varying objective functions, respectively. Under the two proposed piecewise control protocols, the fixed-time cluster optimization problem can be solved on the basis of maintaining the cluster consensus unchanged. Furthermore, the initial states of all the agents need not be restricted to the minimum of their local cost functions. Finally, we provide two examples to illustrate the effectiveness of the proposed control protocols. [ABSTRACT FROM AUTHOR]

• A multi-objective IES planning model with DR and uncertainty is constructed. • A novel multi-objective optimization framework (MOOF) for IES planning is proposed. • A constrained multi-objective coevolutionary algorithm is developed and verified. • The effectiveness of the proposed MOOF is verified by case studies. The planning of integrated energy systems (IES) faces significant challenges due to the presence of multiple uncertainties caused by stochastic demands and renewable energy generation. Particularly, how to balance different conflicted objectives in IES planning under multiple uncertainties is a major obstacle with few studies. Thus, this paper proposes a novel multi-objective optimization framework (MOOF) for uncertain IES planning with demand response to achieve the synergistic enhancement of multiple performance indicators of the system while ensuring its flexibility and safety. The proposed MOOF encompasses several key steps. Firstly, a multi-objective optimization model under various uncertainties is constructed, with the minimization of investment and operating costs, maximization of exergy efficiency, and minimization of carbon emissions as optimization objectives. Secondly, the chance constraint approach is used to convert the constraint conditions into deterministic ones. Subsequently, the Pareto dominance concept is incorporated into robust, interval, and opportunistic optimization techniques to obtain three deterministic transformation methods for multi-objective optimization with uncertainty. Further, a high-efficiency constrained multi-objective coevolutionary algorithm (CMCA) is developed to solve the proposed planning model, which has the characteristics of nonlinearity, and high-dimensional complexity. Finally, the effectiveness of the proposed MOOF and CMCA is verified through numerous case studies. [ABSTRACT FROM AUTHOR]

Real problems in control engineering usually involve many uncertainties and delays. In the search for solutions which deal more adequately with these problems, this paper presents contributions to the theory of the μ -analysis and synthesis applied to uncertain linear fixed time time-delay systems by using Padé approximations. A new necessary and sufficient condition for robust stability for a class of uncertain time-delay systems is presented. From this condition, a novel robust controller synthesis technique is obtained. Furthermore, contributions are presented on the convergence theory of Padé approximations applied to the μ -theory via parametrized optimization technique. In order to better understand the theory presented and verify the effectiveness of the results, examples and comparisons are proposed. Finally, in the conclusions, new lines of research and application are pointed out. [ABSTRACT FROM AUTHOR]

• An iterative optimization framework is proposed to control the effect of outliers. • An iterative algorithm is designed to solve the general L p -norm optimization problem. • The iterative algorithm converges to a local optimum by rigorous theoretical analysis. • We can adjust the parameters of the GEPSVM Lp to balance the accuracy and training time. The proximal support vector machine via generalized eigenvalues (GEPSVM) is a well-known pattern classification method. GEPSVM, however, is prone to outliers due to its use of the squared L 2 -norm distance criterion. A robust GEPSVM version is proposed to tackle this problem using L 1 -norm distance optimization technique (GEPSVM L1). As optimizing a GEPSVM L1 with L 1 -norm terms can be challenging, we have developed an iterative algorithm to address the L 1 -norm ratio problem associated with GEPSVM L1. Furthermore, an efficient iterative optimization framework has been developed to conveniently address related optimization problems. The research contribution of this paper lies in providing a theoretical analysis of the algorithm's convergence. Besides, we find that the L 1 -norm distance-based methods in real-world applications, especially for handling samples with outliers, sometimes provides an unsatisfactory recognition result. Thus, a generalized version of GEPSVM L1 is proposed. The L 1 -norm distance is replaced with a L p -norm distance in GEPSVM L1 (GEPSVM Lp). It is the robust counterpart of GEPSVM L1 and GEPSVM. It is worth noting that we fine-tune GEPSVM Lp 's parameters to strike a balance between training time and classification accuracy, an especially crucial step for larger datasets. Our experiments indicate that the proposed GEPSVM Lp is more efficient and robust than the competitors in numerous experimental settings. Overall, our work demonstrates the importance of developing robust pattern classification methods in the presence of outliers and provides a practical solution for handling such cases in real-world applications. [ABSTRACT FROM AUTHOR]