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In recent years, distributed optimization problem have a wide range of applications in various fields. This paper considers the prescribed-time distributed optimization problem with/without constraints. Firstly, we assume the state of each agent is constrained, and the prescribed-time distributed optimization algorithm with constraints is designed on the basis of gradient projection algorithm and consensus algorithm. Secondly, the constrained distributed optimization problem is transformed into the unconstrained distributed optimization problem, and according to the gradient descent algorithm and consensus algorithm, we also propose the prescribed-time distributed optimization algorithm without constraints. By designing the appropriate objective functions, we prove the multi-agent system can converge to the optimal solution within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. Finally, three simulation examples are provided to verify the validity of the designed algorithms. • For distributed optimization problem with convex constraints, we design an effective prescribed-time distributed algorithm which can guarantee the multi-agent system converge to the optimal solution. • The designed algorithm in this paper can achieve convergence within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. • In this paper, we not only consider the condition that the agents' states are constrained, but also ensure that the multi-agent system can reach the optimal solution within any prescribed-time, which is more practical. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nature-inspired meta-heuristic algorithm, named Snow Geese Algorithm. It is inspired by the migratory behavior of snow geese and emulates the distinctive "Herringbone" and "Straight Line" shaped flight patterns observed during their migration. The algorithm is structured into three main phases for benchmark testing. In the first phase, the Snow Geese Algorithm's numerical results are compared with those of several classical meta-heuristic algorithms using the same test functions and original data from these algorithms. In the second phase, in order to minimize potential variations during the comparison, all algorithms undergo evaluation on a standardized testing platform. In the third phase, this paper applies the Snow Geese Algorithm to solve four widely recognized engineering optimization problems: the tubular column design, piston lever optimization design, reinforced concrete beam design and car side impact design. These real-world engineering problems serve as test cases to assess Snow Geese Algorithm problem-solving capabilities. The primary objective of the Snow Geese Algorithm is to provide an alternative perspective for tackling complex optimization problems. Please note that the complete source code for the Snow Geese Algorithm is publicly available at https://github.com/stones3421/SGA-project. • Snow Geese Algorithm: A novel meta-heuristic approach inspired by snow geese flight behavior, tackling optimization problems. • Two-stage Strategy: Mimics snow geese exploration and exploitation patterns, setting it apart from traditional methods. • Performance Evaluation: Multiple algorithms are assessed on different problems. • Algorithm Features: Experimental results validate the effectiveness of the Snow Geese Algorithm. • Practical Applications: Snow Geese Algorithm shows potential in engineering optimization, aiding accurate decision-making. [ABSTRACT FROM AUTHOR]

One of the most challenging robotic manipulator designs is finding an appropriate balance between the shaking force and shaking moment because this reduces vibrations. Several approaches have been introduced in the last decades; nevertheless, some assumptions must be established to make such a balance. In this paper, a dynamic balancing approach is proposed. The main novelty is the no dependence on specific trajectories to be executed by the manipulator, which allows finding a design with a similar tradeoff in the balancing under robot configuration changes. Also, the proposal incorporates mass distribution and link shape in a single design procedure. The proposal is stated as a constrained nonlinear optimization problem and applied to a hybrid serial-parallel robotic manipulator. The use of different bio-inspired algorithms and one gradient one in the solution of the balancing problem reveals that differential evolution finds the most suitable design. Besides, comparative simulation results of the obtained design with other design approaches show that the obtained design presents the most suitable tradeoff between the shaking force and the shaking moment when the manipulator executes tasks with different operating velocities. [ABSTRACT FROM AUTHOR]

• Propose a topology optimization method for large deformation-induced frictionless contact structures. • Develop a novel nodal density interpolation for the node-to-segment contact model. • Develop the P-norm aggregated strain energy constraint for strength design. • Potentially applicable to the design of functionally demanding wearable devices and soft robots. This paper developed a strength constrained topology optimization method for hyperelastic structures with large deformation-induced frictionless contact. The Neo-Hookean hyperelastic constitutive equation is adopted as the material model that incorporates both material and geometric nonlinearity. The large deforming contact is described by the node-to-segment algorithm. The topology optimization model is developed under the SIMP framework. Given the SIMP-related fictitious domain, we develop a nodal variable-based interpolation scheme to build smooth correlation between the contact force and nodal density variables, thus enabling the smooth transition and variation of contact conditions. Moreover, to guarantee the structure strength, local strain energy constraints are formulated and aggregated by the P-norm function. The details of sensitivity analysis are explicitly explained. At last, several benchmark examples are conducted that validate the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a delayed distributed proportional control is developed for a class of multiple-input-multiple-output systems in order to reduce the effects of external disturbances and uncertain dynamics. The system under consideration is a three degrees-of-freedom planar robot, consisting of an active joint driving the first link and passive flexible joints for the second and third links. The control challenge for this system is to drive the flexible arm around the desired setpoint or trajectory by controlling the first link. Actually, by using a specific Lyapunov-Krasovskii storage functional associated with a constrained optimization problem, the robustness of the proposed proportional retarded control action is achieved. This analysis, together with the attractive ellipsoid concept allows to conclude with Ultimately-Uniformly-Bounded stability. The closed-loop control algorithm is tested in numerical simulation and a comparative study with some existing recent control schemes based on the performance error is presented. • The proposed control scheme belongs to a class of multivariable proportional time delay distributed controller. • After finite time the trajectories of a flexible manipulator stay within reduced size attractive ellipsoid. • Ultimate Uniformly Bounded Stability of the controlled system is demonstrated by using a Lyapunov-Krasovskii functional. • The proposed algorithm can also be adapted for a class of nonlinear uncertain/disturbed multivariable output systems. • A sufficient condition to guarantee the feasibility of specific Bilinear Matrix Inequality is presented. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved enhancement space-variant anisotropic PDE-constrained for image denoising, based on a learning optimization procedure. Since the tensor structure of the Weickert-type operators encodes three critical parameters: λ 1 , λ 2 and θ , which define the local direction geometry in the image and also controls smoothing intensity along image features. We have then adopted an automatic estimation of these parameters based on a PDE-constrained optimization with a learning step of the additional information about the clean image. For the numerical solution of the computed free-noise image, we introduce a non-smooth Alternating Direction Method of Multipliers (ADMM) algorithm. The numerous tests in the experiments part incense, visually and quantitatively, the incomes of this new spatial tensor for PDE smoothing over the other denoising approaches. • An improved enhancement space-variant anisotropic PDE-constrained for image denoising is introduced. • An automatic estimation of the diffusion-tensor parameters based on a bilevel optimization with a learning step is involved. • A non-smooth ADMM algorithm is also performed to compute the solution of the optimization-constrained problem. • The comparative results demonstrate the performance of the proposed algorithm against complex noise structures. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient bi-level energy management strategy (EMS) to optimize the operation cost of a grid-connected microgrid, considering the system operational constraints and uncertainties for renewable energy sources and load demand. The first level is optimal day-ahead scheduling based on two stages: the first stage is finding the optimal operating points of sources during the next day while the second one is controllable loads management. The second level of the proposed EMS is rescheduling and updating the set-points of sources in real-time according to the actual solar irradiance, wind speed, load, and grid tariff. In this paper, a novel real-time strategy is proposed to keep the economic operation during real-time under uncertainties. Also, a recent meta-heuristic algorithm called Honey Badger Algorithm (HBA) is used to solve the problem of day-ahead scheduling of batteries, which is a complex constrained non-linear optimization problem. Results obtained demonstrate that the HBA based bi-level EMS provides the real-time optimal economic operation of a grid tied microgrid under uncertainties in weather, utility tariff and load forecasts. • Bi-level energy management strategy for optimal operation of grid tied microgrids. • Load shifting approach that reshapes the load according to the target load profile. • A novel real-time scheduling strategy of grid tied microgrids under uncertainties. [ABSTRACT FROM AUTHOR]

Nature- and society-inspired metaheuristic algorithms have recently become the most promising technological model. To solve more complex optimization problems and complicated engineering applications, this paper proposes a new people duality psychological tendency and feedback mechanism-based Inherited Optimization Algorithm(IOA), which is inspired by people showing positive-negative duality cognitive tendency and adaptive feedback behavior when selecting information resources with different identity attributes. The IOA algorithm contains the symmetric two exploration phases. The exploitation phase adaptively regulates the dualistic psychological balance of people in inheriting the information resources with better existence value through a feedback regulation mechanism controlled by the profitability awareness to increase population diversity. This paper qualitatively and quantitatively evaluates the optimization performance of IOA on 84 benchmarks, including swarm convergence behavior, effectiveness, convergence, robustness, and significance. The scalability of the IOA is investigated using the CEC2017 suites. The algorithm performance in solving constrained optimization is verified on 8 engineering problems. All statistical results of the IOA are compared with the most promising 12 metaheuristics, which shows that the absolute computational efficiency of IOA on four types of functions is 95%, 96.67%, 80.95%, and 76.92%, respectively, the average rank (rank sum ratio) of IOA is 1.08 (1.19%) among the 13 algorithms, ranking first. The Wilcoxon signed rank test results on the CEC2017 suites show that IOA contains 1437 significance indicators out of 1440 comparisons, with the proportion of significant differences 99.79%, which suggests the proposed IOA maintains efficient search efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a formal verification guided approach for a principled design and implementation of robust and resilient learning-enabled systems. We focus on learning-enabled state estimation systems (LE-SESs), which have been widely used in robotics applications to determine the current state (e.g., location, speed, direction, etc.) of a complex system. The LE-SESs are networked systems, composed of a set of connected components including: Bayes filters for state estimation, and neural networks for processing sensory input. We study LE-SESs from the perspective of formal verification, which determines the satisfiabilty of a system model against the specified properties. Over LE-SESs, we investigate two key properties – robustness and resilience – and provide their formal definitions. To enable formal verification, we reduce the LE-SESs to a novel class of labelled transition systems, named {PO}2-LTS in the paper, and formally express the properties as constrained optimisation objectives. We prove that the verification problems are NP-complete. Based on {PO}2-LTS and the optimisation objectives, practical verification algorithms are developed to check the satisfiability of the properties on the LE-SESs. As a major case study, we interrogate a real-world dynamic tracking system which uses a single Kalman Filter (KF) – a special case of Bayes filter – to localise and track a ground vehicle. Its perception system, based on convolutional neural networks, processes a high-resolution Wide Area Motion Imagery (WAMI) data stream. Experimental results show that our algorithms can not only verify the properties of the WAMI tracking system but also provide representative examples, the latter of which inspired us to take an enhanced LE-SESs design where runtime monitors or joint-KFs are required. Experimental results confirm the improvement in the robustness of the enhanced design. [ABSTRACT FROM AUTHOR]

Avoiding singularities is a crucial task in robotics and path planning. This paper proposes a novel algorithm for detecting the closest singularity to a given pose for nine interpretations of the 3-RPR manipulator. The algorithm utilizes intrinsic metrics based on the framework's total elastic strain energy density, employing the physical concept of Green-Lagrange strain. The constrained optimization problem for detecting the closest singular configuration with respect to these metrics is solved globally using tools from numerical algebraic geometry implemented in the software package Bertini. The effectiveness of the proposed algorithm is demonstrated on a 3-RPR manipulator executing a one-parametric motion. Additionally, the obtained intrinsic singularity distances are compared with extrinsic metrics. Finally, the paper illustrates the advantage of employing a well-defined metric for identifying the closest singularities in comparison with the existing methods in the literature, highlighting its application in design optimization. [ABSTRACT FROM AUTHOR]

This paper uses the mirror descent algorithm with periodic dynamic quantization to solve constrained distributed optimization problems with limited communication channels. Due to the imperfect network environment, obtaining accurate information is impractical, and thus a communication scheme under quantization needs to be considered. A periodic dynamic quantizer with finite quantization levels is proposed in this paper to achieve exact optimization. Moreover, a time-varying control parameter in the mirror descent algorithm is designed to control the quantization error. After a comprehensive analysis, the proposed algorithm can obtain an optimal value, and the optimal convergence rate is O (1 / T 0. 25). [ABSTRACT FROM AUTHOR]

The railway world is undergoing major changes. The advent of new technologies allows us to rethink the train system and face new challenges, but one must not forget all the ecological constraints that are now accentuated by the increase in energy costs. This paper focuses on the optimization of the driver commands to limit the energy consumption of the trains under punctuality and security constraints. This problem falls within the framework of control optimization problems for nonlinear dynamic mechanical systems in the presence of constraints and uncertainties. A four-step approach is then proposed in this paper to solve this problem: (1) the introduction of simplified and fast-to-evaluate models to model the nonlinear dynamic behavior of the train and its energy consumption; (2) the identification in a Bayesian formalism of the parameters on which these models depend from on-track measurements on commercial trains; (3) the reformulation of the optimization problem so that it integrates the uncertainties related to an imperfect knowledge of these estimated parameters; (4) the resolution of the optimization problem using evolutionary algorithms. The main specificity of this work lies in the fact that not only the objective function to be minimized, here the energy consumed by the train, is impacted by the uncertainties, but also the admissibility constraints of the solution, here punctuality and operating safety. The integration of the uncertainties in the search for the control function is thus not trivial and requires several original adaptations in order to make the final optimization problem well posed. • The paper considers a particular constrained control optimization problem. • Uncertainties on the cost function and constraints make the problem difficult. • The control problem is first reformulated to make the constraints deterministic. • Evolutionary algorithms are then used to identify optimized control functions. • An application to the minimization of the train electrical consumption is presented. [ABSTRACT FROM AUTHOR]

This paper introduces an improved differential evolution algorithm based on cooperative multi-population (CMp-DE for short), which combines diverse population collaboration mechanisms and catalytic factors into an improved differential evolution framework. By harnessing various population collaboration mechanisms, the algorithm enhances the diversity of individuals within populations during initial iterations and reduces it during later iterations, thereby harmonizing the algorithm's exploratory and exploitative capabilities. Furthermore, a novel mutation operator is proposed that divides the iterative process into exploration and exploitation phases, thereby augmenting the algorithm's global exploration prowess. Lastly, a catalytic operator is introduced to generate new individuals near post-crossover individuals based on a specified rule, which enhances the algorithm's ability to escape local optima and increasing stability. The proposed CMp-DE is benchmarked against the CEC2017 benchmark test functions and compared against 13 algorithms, including five differential evolution algorithms and their variants, as well as eight state-of-the-art metaheuristic optimization algorithms. This evaluation assesses the CMp-DE's solution accuracy, convergence, stability, and scalability. Finally, the applicability of CMp-DE is validated by addressing six practical optimization problems. The experimental results show that CMp-DE surpasses other algorithms in terms of both convergence accuracy and robustness. Moreover, integrating a catalytic operator with other optimization algorithms notably boosts performance in convergence accuracy and stability. The inclusion of the catalytic operator has significantly enhanced the performance of algorithms compared to their performance before its addition. This underscores the potential of the catalytic operator in improving the performance of various algorithms, particularly in terms of convergence accuracy and robustness. [ABSTRACT FROM AUTHOR]

Modeling interdependencies in a production process is a vital aspect of process engineering. Being built on the fundamental understanding of the process and capturing underlying physical and chemical mechanisms, this task is traditionally carried out through first-principle modeling. However, due to various assumptions and parameter approximation, such approach sometimes struggles to accurately model process dynamics. This is partially due to the requirements for process stability in mission-critical scenarios, where it is not viable to conduct extensive experiments that would enable the development of comprehensive models, and hence, the process control is still partially dependent on the experience of the process operator. In this paper, a data-driven approach based on production data is introduced to address the challenge of discovering, understanding, and modeling interdependencies in a production process by constructing a constrained multi-objective optimization problem. The applicability of the proposed approach is demonstrated through a case study in the ironmaking industry, whereby a set of Machine Learning and causality-based methods is provided while highlighting the significance of transparency and use of domain knowledge in the development of data-driven models. Based on the real data from a Sintering plant, interdependencies are quantified between five key process parameters, which, when supplemented with other control parameters, result in a higher overall process output leading to reductions in operational costs and environmental impact. Such approach shows potential for applications in a broader context across other fields of science and engineering, where it can be used to improve the discovery of interdependencies in complex real-world industrial settings. • Modeling interdependencies in production is a vital aspect of process understanding. • First-principle models can struggle to accurately model complex process dynamics. • Constrained multi-objective optimization is used to address interdependency modeling. • Interdependencies are identified and quantified between five key parameters. • Higher output, reduced operational costs, and environmental impact are achieved. [ABSTRACT FROM AUTHOR]

This paper aims to estimate the three-phase voltage sensitivity matrix and the network topology of a low-voltage distribution network from smart meter data, which measures voltage magnitude, current magnitude, and power factor with a lead/lag indicator. The targeted networks are three-phase networks with single-phase loads. Understanding network sensitivity and topology is crucial for fault detection, unmetered load identification, and addressing Dynamic Operating Envelope problems. The problem is formulated as a constrained optimization problem to estimate both the three-phase voltage sensitivity and the low-voltage transformer voltage. The estimated voltage sensitivity is used to further identify the network topology, by implementing an enhanced Recursive-Grouping and Backtracking algorithm, as well as a candidate topology selection technique. The proposed method is tested on the 55-node European feeder and several synthetic networks. Compared to the state-of-the-art, the results show a four-fold improvement in the accuracy of voltage sensitivity estimation and substantially fewer mistakes in the topology estimates. The result underscores the efficacy of a three-phase network model and voltage angle approximation in enhancing estimation accuracy. • Estimating voltage sensitivity to current of low-voltage distribution network. • Estimation uses smart meter measurements only. • Transformer voltage is estimated simultaneously. • Approximating unknown phase angle and considering coupling effect on neutral line. • A fast Recursive-Grouping and Backtracking algorithm to find topology. [ABSTRACT FROM AUTHOR]

Connected and autonomous vehicle (CAV) platooning technologies have been extensively studied. Many CAV platoon control schemes, e.g., model predictive control schemes, are formulated as constrained optimization problems since they can effectively handle vehicle dynamics, physical and safety constraints, and desired performance. At each time, a control input is generated from a constrained optimization problem, whose constraint set is time-varying and depends on states and controls in the past times. A fundamental question is whether the underlying optimization problem is sequentially feasible, i.e., whether the constraint set is nonempty at each time along a platoon state solution, assuming that it starts from an arbitrary initial condition which satisfies the physical and safety distance constraints. In this paper, we present a comprehensive study of sequential feasibility and related constraint properties under various vehicle dynamics and safety distance constraints. In particular, we consider three types of linear or nonlinear vehicle dynamic models and a wide range of representative spacing policy based safety distance constraints as well as physical constraints. We show that the constant spacing based safety distance constraint does not yield sequential feasibility, whereas most of variable spacing policy based safety distance constraints, along with the physical constraints, ensure sequential feasibility under mild, easily verifiable conditions on constraint or vehicle dynamics parameters. Extensions to general and unified forms of safety distance constraints have been made. We also show that under additional assumptions, each non-polyhedral constraint set has nonempty interior if sequential feasibility holds. This constraint property plays an important role in robust sequential feasibility analysis and optimization algorithm development. • Comprehensive sequential feasibility study of CAV platoons under various vehicle dynamics and physical or safety constraints. • Three types of linear or nonlinear vehicle dynamics are considered. • Five representative spacing policy based safety distance constraints and their generalizations are considered. • Unified treatment of sequential feasibility leads to various mild, easily verifiable sequential feasibility conditions. • The non-empty interior property of non-polyhedral constraint sets is established. [ABSTRACT FROM AUTHOR]

This paper considers a distributed constrained optimization problem over a multi-agent network in the non-Euclidean sense. The gossip protocol is adopted to relieve the communication burden, which also adapts to the constantly changing topology of the network. Based on this idea, a gossip-based distributed stochastic mirror descent (GB-DSMD) algorithm is proposed to handle the problem under consideration. The performances of GB-DSMD algorithms with constant and diminishing step sizes are analyzed, respectively. When the step size is constant, the error bound between the optimal function value and the expected function value corresponding to the average iteration output of the algorithm is derived. While for the case of the diminishing step size, it is proved that the output of the algorithm uniformly approaches to the optimal value with probability 1. Finally, as a numerical example, the distributed logistic regression is reported to demonstrate the effectiveness of the GB-DSMD algorithm. • A Gossip-Based Distributed Stochastic Mirror Descent (GB-DSMD) algorithm is proposed. • In the proposed algorithm, appropriate step-size can be chosen by each agent based on local information. • The impact of two different step sizes on the performance of GB-DSMD algorithm is analyzed. [ABSTRACT FROM AUTHOR]

Striking a balance between diverse constraints and conflicting objectives is one of the most crucial issues in solving constrained multi-objective optimization problems (CMOPs). However, it remains challenging to existing methods, due to the reduced search space caused by the constraints. For this issue, this paper proposes a Pareto fronts relationship identification-based two-stage constrained evolutionary algorithm called RITEA, which balances objective optimization and constraint satisfaction by identifying and utilizing the relationship between the unconstrained Pareto front (UPF) and the constrained Pareto front (CPF). Specifically, the evolutionary process is divided into two collaborative stages: training stage and reinforcement stage. In the training stage, a relationship identification method is developed to estimate the relationship between UPF and CPF, which guides the population search direction. In the reinforcement stage, the corresponding evolutionary strategies are designed based on the identified relationship to enhance the accurate search on the CPF. Furthermore, a dynamic preference fitness function (termed DPF) is designed to adaptively maintain the balance of search preference between convergence and diversity. Compared to seven state-of-the-art algorithms on 36 benchmark CMOPs in three popular test suites, RITEA obtains 77.8% of the best IGD values and 66.7% of the best HV values. The experimental results show that RITEA exhibits highly competitively when dealing with CMOPs. [Display omitted] • RITEA, a two-stage constrained evolutionary algorithm for CMOPs. • Identify and utilize the relationship between UPF and CPF to balance optimization and constraint satisfaction. • Utilizes collaborative training stage and reinforcement stage to guide population search accurately. • Introduction of a dynamic preference fitness function to adaptively balance the search preferences between convergence and diversity. [ABSTRACT FROM AUTHOR]

The distribution of relaxation times (DRT) analysis based on Tikhonov regularization is widely used to analyze the battery electrochemical impedance spectroscopy (EIS), but the selection of the regularization parameter has always been a problem. This paper proposes an Optimization Pre-calculation DRT-tool of EIS (OPDEIS) which not only completes the full-frequency EIS fitting with the insertion capacitor and inductance but also obtains the optimal regularization parameter and the fitting kernel function width through a multi-objective optimization algorithm. In particular, it can resolve the DRT parameter selection for EIS above 0.1 Hz in lithium-ion batteries effectively. This optimizer is suitable for lithium-ion battery EIS data with different states of charge (SOCs) and frequency points. It provides a reference for selecting the regularization parameter and full width at half maximum (FHWM) automatically, enabling the DRT process based on Tikhonov regularization to perfectly avoid overfitting or underfitting. [Display omitted] • The accurate implementation of full-frequency DRT considering the inserted capacitor. • Optimize the regularization parameter and FWHM to avoid overfitting and underfitting. • Multiple objective constrained optimization automatically obtains perfect DRT results • A new DRT pre-processing tool for different frequency points and SOCs are available [ABSTRACT FROM AUTHOR]

When dealing with Constrained Multi-objective Optimization Problems (CMOPs) and struggling to enhance feasibility, convergence and diversity, the researchers of Constrained Multi-objective Optimization Evolutionary Algorithms (CMOEAs) gravitate toward feasibility or take precedence to preserve well-converged solutions ignoring diversity in the past. To compensate for the defects, the paper proposes CMOEA-MSWA to guide the search of infeasible regions by coordinated strategy of archive and weight vectors. Firstly, the archive carrying the information of population diversity updates the weight vectors. Secondly, the updated weight vectors perpetuate the diversity information to the search of infeasible regions. The circular effects between strategies promote the detection of infeasible solutions with good objectives and exhibit competitive performance in terms of spread and evenness. To testify the versatility the CMOEA-MSWA in enhancing diversity, the comprehensive performance is evaluated firstly and the diversity analysis of the CMOEA-MSWA is carried out on four benchmark suites with 34 test instances, where the number of objectives for some of test problems is scaled from three to five. In comparison with five state-of-the-art CMOEAs, the proposed algorithm yields highly competitive performance in diversity on different types of CMOPs. In addition, the effectiveness of collaboration between archive and weight vectors on handling infeasible solutions is also verified. [ABSTRACT FROM AUTHOR]

The demand response (DR) operation of chiller plants has been approached with model predictive control (MPC) strategies, based on day-ahead electricity price, knowledge of upcoming weather and load profiles. Since chiller plants feature significant nonlinearities across wide variations in ambient and load conditions, nonlinear MPC (NMPC) is the typical alternative to piecewise linear MPC design, however, suffers from high computational loads. In this paper, an NMPC based chiller plant DR operation is proposed based on data-driven Koopman bilinear form (KBF) dynamic model structure. A generalized tangential interpolation based Krylov-subspace model reduction procedure is applied to the KBF model to yield parsimonious model in the Koopman lifted subspace. Then, the McCormick relaxation is applied to convexify the bilinear terms for the constrained optimization problem formulation associated with the MPC design of the reduced-order KBF system. The KBF based convexified MPC design minimizes the overall energy cost that combines the time-of-use (TOU) and demand charges, with the cooling load regulated in real time. The effectiveness of the proposed control strategy is shown with a Modelica-based dynamic chiller plant model via a Functional Mockup Interface (FMI) based Modelica-Python co-simulation platform. • Computationally efficient Koopman bilinear MPC design for chiller demand response. • Data-driven Koopman bilinear model obtained for nonlinear dynamic plant modeling. • Tangential interpolation based Krylov-subspace reduction for Koopman bilinear model. • Convexification of bilinear terms and ∞ -norm cost in the constrained optimization. • Simulation validation with Modelica-Python co-simulation platform based on FMI. [ABSTRACT FROM AUTHOR]

There are some situations in reliability studies in which a middle part of the sample is missing. For example, in life-testing experiments, the experimenter may not enable to observe the middle lifetimes. This may be due to negligence, removal of test units (censoring) and or equipment failure and technical problems in the middle of the experiment. In this paper, the problem of reconstructing unobserved middle lifetimes from the exponential distribution is discussed and reconstruction regions (or sets) are obtained. First, it is shown how to find balanced reconstruction regions for the unobserved middle lifetimes. Then, a constrained minimization problem is used to find the reconstruction region with the smallest area. For illustrative and comparative purposes, a simulation study and two numerical examples are presented. • Exact reconstruction regions for unobserved middle lifetimes are proposed. • Constrained optimization problems are stated to determine the minimum-size reconstruction regions. • Efficient procedures based on minimizing Lagrangian functions are proposed. • Two numerical examples and a simulation study are presented for illustrative and comparative purposes. [ABSTRACT FROM AUTHOR]

This paper addresses a risk-constrained decentralized stochastic linear-quadratic optimal control problem with one remote controller and one local controller, where the risk constraint is posed on the cumulative state weighted variance in order to reduce the oscillation of system trajectory. In this model, local controller can only partially observe the system's state, and sends the estimate of state to remote controller through an unreliable channel, whereas the channel from remote controller to local controller is perfect; here, the unreliable channel means the Bernoulli channel, that is, when data is transmitted on this channel, there is a certain probability of failure. For the considered constrained optimization problem with Bernoulli channel, we first punish the risk constraint into cost function through Lagrange multiplier method, and the resulting augmented cost function will include a quadratic mean-field term of state. In the sequel, for any but fixed multiplier, explicit solutions to finite-horizon and infinite-horizon mean-field decentralized linear-quadratic problems are derived together with necessary and sufficient condition on the mean-square stability of optimal system. Then, approach to find the optimal Lagrange multiplier is presented based on bisection method. Finally, two numerical examples are given to show the efficiency of the obtained results. [ABSTRACT FROM AUTHOR]

This paper introduces the Human Evolutionary Optimization Algorithm (HEOA), a metaheuristic algorithm inspired by human evolution. HEOA divides the global search process into two distinct phases: human exploration and human development. Logistic Chaos Mapping is employed for initialization. In the human exploration phase, an initial global search is conducted, followed by the human development phase, in which the population is categorized into leaders, explorers, followers, and losers, each utilizing distinct search strategies. The convergence speed and search accuracy of HEOA are evaluated using 23 well-established test functions. Furthermore, the algorithm's applicability in engineering optimization is assessed with four engineering problems. A comparative analysis with ten other algorithms highlights HEOA's effectiveness, as evidenced by various performance metrics and statistical measures. Consistently, the results demonstrate that HEOA surpasses most current state-of-the-art algorithms in approximating optimal solutions for complex global optimization problems. The MATLAB code for HEOA is available at https://github.com/junbolian/HEOA.git. [ABSTRACT FROM AUTHOR]

The parameters of fractures, such as fracture geometries, placement, and conductivity, are critical for the production of horizontal wells in tight gas reservoirs. Current studies on fracture parameter optimization, especially those using the optimization algorithms, are often based on the assumptions that the fractures are uniformly placed with equal length and conductivity. However, non-uniform designs of fracture spacing have been proved to be effective in reducing fracture interference as well as increasing the well production, especially with strong rock heterogeneity. In addition, most of the current literature focuses on identifying optimal hydraulic fracture geometries without considering the fracture propagation process, which may oversimplify the evaluation of completion cost and result in fracture geometries that cannot be realized in practice. In this paper, an automatic optimization process, which could efficiently couple the fracture propagation, is proposed to optimize the fracture parameters. The genetic algorithm (GA) with penalty function method is used to optimize the constraint and non-linear problem. A modified PKN fracture propagation model is established to quickly predict the hydraulic fracture geometries. Fracture locations and fluid injection volume are sequentially optimized with consideration of heterogeneous rock properties. Model parameters are taken from typical tight gas horizontal wells in Jinqiu gas field in Sichuan Province, China. The results show the importance of permeability on both fracture length and production in tight gas well. The lower the permeability, the more significant the production increase after fracture location optimization. More fractures tend to be placed in area with small permeability and fewer fractures are required in highly permeable regions. With an increase in rock heterogeneity, the distribution of fractures becomes more uneven. Conversely, increasing the number of fractures can lead to a more even distribution of fractures. More fluid should be injected in low-permeability regions according to the optimization results. The optimization method is applied to a practical tight gas well in Sichuan Basin and the cumulative gas production can be increased by 6.5% to 8% when compared with the initial uniform designs. • A modified PKN model is proposed for quick prediction of fracture propagation, validated against numerical simulations. • GA is used to optimize fracture locations by considering rock heterogeneity's impact on fracture propagation and production. • Sequential optimization of fracture placement and injection volume is conducted with its reliability verified. [ABSTRACT FROM AUTHOR]

Stress topology optimization of continuum structures is an important topic for structural design and has been widely investigated. However, stress topology optimization itself is ill-conditioned and the resulting optimized designs highly depend on the used parameters, mesh size, and so on. In this paper, stress minimization and stress-constrained topology optimization problems are reformulated by introducing a global measure of an optimized design, the average solid stress. The floating projection topology optimization method is established with ε -relaxation and the p-norm function. Due to the modified problem formulations, the optimized design becomes insensitive to the selection of p in the p-norm function once p is large enough, such as p = 16 , to ensure the total elimination of stress concentration. Numerical results show the uniform stress distribution of optimized designs, demonstrating the effectiveness of the proposed method. Some findings on the elimination of stress concentration, mesh-independent optimized topology, equivalent smooth design, and impractical design are presented and discussed. [ABSTRACT FROM AUTHOR]

How to balance the satisfaction of constraints and the optimization of objective functions is one of the key issues to solve constrained multi-objective optimization problems (CMOPs), especially when the constraints are complex. Although many algorithms have been designed to handle this, most of them are still unable to effectively handle CMOPs with complex constraints. Based on the above issue, this paper proposes a framework for bi-directional search, which evolves two populations (P 1 and P 2). P 1 aims to search the constrained Pareto front (CPF) from the infeasible side of the objective space, and P 2 from the feasible side, aiming to achieve a more efficient and comprehensive bi-directional search for the CPF. To ensure the diversity, we adopt a preferred weight vector selection strategy to choose potential mating parents, which improves the search capability for the marginal CPF. Furthermore, to coordinate the interaction between the two populations, we propose an environmental selection strategy to select the offspring generated by P 1 and P 2 under the same weight vector with the better fitness to update the populations respectively, and the fitness is evaluated based on the different constraint relaxations of the two populations, to update them respectively. Extensive experiments indicate that our proposed algorithm has significantly better results or was at least competitive when compared to eight state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The interests in evolutionary constrained multiobjective optimization are rapidly increasing during the past two decades. However, most related studies are limited to small-scale problems, despite the fact that many practical problems contain large-scale decision variables. Although several large-scale constrained multi-objective evolutionary algorithms (CMOEAs) have been developed, they are still tested on benchmarks that are designed for small-scale problems without the features of large-scale problems. To promote the research on large-scale constrained multi-objective optimization (LSCMO), this paper proposes a new LSCMO benchmark based on the design principles of large-scale multi-objective optimization and constrained multi-objective optimization. In this benchmark, more realistic features are considered, such as mixed linkages between constraint variables and unconstrained variables, imbalanced contributions of variables to the objectives, varying number constraint functions. Besides, to better solve the proposed benchmark, a bidirectional sampling strategy is proposed, where a convergence direction sampling and a diversity direction sampling are used to accelerate the convergence and maintain diversity respectively. Furthermore, the proposed bidirectional sampling strategy is embedded into an existing CMOEA to improve the search ability of algorithm in the large-scale search space with constraints. In experiments, the proposed algorithm is compared with several latest peer algorithms, and the results verify that the designed benchmark functions can effectively test the performance of algorithms and the proposed algorithm can better tackle the new benchmark. Finally, the proposed algorithm is used to solve the network structure control-based personalized drug target recognition problems with more than 2000 decision variables, and results show its superiority. [ABSTRACT FROM AUTHOR]

Many metaheuristic methods have been proposed and have shown promising performance in solving multi-objective optimization problems (MOPs). As a representative example, the competitive swarm optimizer (CSO) exhibits excellent performance in solving large-scale MOPs. However, CSO performs poorly when used straightforwardly for constrained MOPs with complex constraints or objective spaces. In this paper, we propose a dual-swarm-based CSO framework that co-evolves two swarms with different orientations: (i) CSO is improved using genetic algorithms, namely CSO-GA to form a convergence-directed swarm without considering constraints to accelerate the convergence. (ii) CSO is further improved by differential evolution, namely CSO-DE to form a search-directed swarm by considering constraints to enhance diversity and improving the search for the constrained Pareto front. Moreover, a new constraint relaxation method is proposed that combines the particle swarm feasible rate and current evolutionary generation to dynamically adjust the factor for decreasing or increasing constraint relaxation. Extensive experiments with 37 benchmark instances and nineteen real-world constrained MOPs demonstrate that our proposed algorithm yields significantly better or at least more competitive results than ten state-of-the-art methods. • A dual-swarm-based CSO framework is proposed for CMOPs. • Two swarms co-evolve to form different orientations. • A new constraint relaxation method is presented. • Results demonstrate the superiority of our approach. [ABSTRACT FROM AUTHOR]

Most multiple unmanned aerial vehicle (UAV) path planning problems are often treated as constrained single-objective optimization problems. How to consider them as constrained multi-objective optimization problems (CMOPs) have seldom been explored in this field. To fill this gap, this paper firstly constructs multiple UAV path planning problem as a CMOP with two objectives and five constraints. Then a novel multi-objective evolutionary algorithm with a two-fold constraint-handling mechanism is proposed for multiple UAV path planning. To cope with constraints effectively, a constraint-handling technique based on a progressive weight vector strategy is proposed. Besides, a constraint repair technique that considers the flying environment is designed to further guide the algorithm to find feasible promising regions. Eight multiple UAV path planning test instances with different solving difficulties are constructed. Subsequently, they are used to validate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm is superior over four compared algorithms in terms of obtaining a set of better-distributed Pareto optimal solutions. [ABSTRACT FROM AUTHOR]

Despite the inherent stability of Quadratic Approximation based Jaya (JaQA) in solving constrained optimization problems, its effectiveness in exploring the search space is limited. Furthermore, the user-defined penalty parameter utilized to mitigate constraint violation poses another impediment to the efficiency of JaQA. In light of these drawbacks, this paper introduces a new meta-heuristic algorithm, namely Self-adaptive Multi-population Quadratic Approximation guided Jaya (SMP-JaQA). The three additional key features of the SMP-JaQA over JaQA are (i) the inclusion of a multi-population technique to ensure sufficient diversity in the search process (to address the exploration), (ii) a self-adaptation strategy to make it more user-friendly for complex problems in various domains (to address the exploration further), and (iii) a dynamic penalty factor (to automate the penalty parameter). These novel changes help strike a balance between exploration and exploitation too by identifying potential areas in the search space and reduces the likelihood of getting stuck in a local optima. The validity of SMP-JaQA is tested over 18 and 28 benchmark functions of varying dimensions from the CEC-2010 and CEC-2017 test suites, respectively. In order to justify the wide applicability of the proposed algorithm, a set of four real-life constrained problems (includes two mechanical design problems, an electronics design problem and an agricultural problem) are being solved through SMP-JaQA. A thorough analysis of computational outcomes, exploration–exploitation phases, and scalability are presented to shed light on the efficacy and stability levels of SMP-JaQA. The results are compared with those of other state-of-the-art meta-heuristics using Friedman's and Wilcoxon Signed-Rank tests. The outcomes indicate that SMP-JaQA produces reasonable solutions in terms of global optimality and solution quality. [ABSTRACT FROM AUTHOR]

Traditional differential evolution (DE) algorithms have functional limitations in effectively addressing increasingly intricate numerical optimization problems. The key to responding to this challenge is to strike a suitable balance between exploration and exploitation. Exploration is used to find the global optimal solution, and exploitation is used to improve the accuracy of the global optimal solution. Therefore, this study introduces a novel differential evolution algorithm with a stage stratification method and a dual balanced mutation strategy framework, named SbmDE. To enhance the balance between convergence and diversity, the population is stratified into exploration and exploitation layers based on the size of the fitness value and evolutionary period. In the exploitation layer, a hybridization mutation is applied as the first mutation. A novel dual improved mutation operation is proposed and applied to the exploration layer. For the first mutation, the hybridization mutation is used in the same manner as for the exploitation layer. For the secondary mutation, two improved mutation strategies are proposed to alleviate premature convergence at the early stage of evolution and to enhance the local neighborhood search at a later stage, named DE/ranking-to-rand/1 and DE/best-current-dev/1. Experiments were conducted on the CEC2017 benchmark suite, which contains 29 single-objective real-parameter numerical optimization problems, to evaluate the performance of the proposed algorithm. Compared with 11 state-of-the-art algorithms, the results demonstrate the superiority of the proposed algorithm, which does not increase the time complexity. Additionally, based on the four engineering design problems, the proposed algorithm is fully competent in solving practical constrained optimization problems. • A stage stratification method is proposed to divide population. • Dual mutation is proposed and applied to the exploration layer. • Proposed two improved mutation strategies applied to the secondary mutation. • SbmDE obtains better values than the other algorithms in the paper. • SbmDE is applied to four engineering design problems. [ABSTRACT FROM AUTHOR]

The main challenge in addressing constrained multi-objective optimization problems (CMOPs) lies in achieving a balance among convergence, diversity, and feasibility. To address this issue, this paper proposes a constrained multi-objective evolutionary algorithm with Pareto estimation via neural network named CMOEA-PeNN. In order to exploit and explore the decision space, the proposed algorithm employs a dual-population mechanism, which is trained with a self-organizing map (SOM). Firstly, the population distribution structure in decision space is mapped to objective space while preserving neighborhood information, and then the neuron weight is utilized to estimate the Pareto front (PF). Secondly, a novel approach is devised to preserve the feasibility of the population and enhance the estimation of the Pareto front by SOM. The achievement scalarizing function (ASF) is employed to choose promising solutions. This strategy could guide the population toward the optimal solution while exploring the small feasible regions. Finally, the performance of CMOEA-PeNN is compared with five state-of-the-art constrained multi-objective evolutionary algorithms (CMOEAs) on three widely used benchmark problems and a real-world problem. The experimental results show that CMOEA-PeNN could archive competitive performance in solving CMOPs. [ABSTRACT FROM AUTHOR]

We study the problem of tuning the parameters of a room temperature controller to minimize its energy consumption, subject to the constraint that the daily cumulative thermal discomfort of the occupants is below a given threshold. We formulate it as an online constrained black-box optimization problem where, on each day, we observe some relevant environmental context and adaptively select the controller parameters. In this paper, we propose to use a data-driven P rimal- D ual C ontextual B ayesian O ptimization (PDCBO) approach to solve this problem. In a simulation case study on a single room, we apply our algorithm to tune the parameters of a Proportional Integral (PI) heating controller and the pre-heating time. Our results show that PDCBO can save up to 4.7% energy consumption compared to other state-of-the-art Bayesian optimization-based methods while keeping the daily thermal discomfort below the given tolerable threshold on average. Additionally, PDCBO can automatically track time-varying tolerable thresholds while existing methods fail to do so. We then study an alternative constrained tuning problem where we aim to minimize the thermal discomfort with a given energy budget. With this formulation, PDCBO reduces the average discomfort by up to 63% compared to state-of-the-art safe optimization methods while keeping the average daily energy consumption below the required threshold. [Display omitted] • A primal–dual contextual Bayesian optimization framework to tune building controllers is proposed. • It asymptotically achieves the optimal performance while satisfying constraints on average. • It saves up to 4.7% energy compared to alternatives, satisfying thermal comfort constraints on average. • Alternatively, it can reduce the thermal discomfort by 63% within a given energy budget. [ABSTRACT FROM AUTHOR]

Box-constrained optimization problems in the real m × n matrix space have been widely applied in big data mining. However, efficient solution of them is still a challenge. In this paper, a new nonmonotone line search rule is first proposed by extending the well-known ones and inheriting their advantages. Then, by analyzing and exploiting properties of this rule, a new nonmonotone spectral projected gradient algorithm is developed to solve the box-constrained optimization problems in the matrix space. Global convergence of the developed algorithm is also established. Numerical tests are conducted on a series of randomly generated test problems and those in the set of benchmark test problems. Compared with other existing nonmonotone line search rules, our rule shows its advantages in terms of the significantly reduced number of function evaluations and significantly reduced number of iterations. To further validate applicability of this research, we apply the studied optimization problem and the developed algorithm to solve the problems of image clustering. Numerical results demonstrate that the proposed method can generate better clustering results and is more robust than the similar ones available in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a distributed constrained optimization problem for discrete-time multi-agent systems with time-varying directed graphs is studied and a distributed projection subgradient algorithm is proposed. Furthermore, we investigate the scenario involving time delays in agent interactions and use an augmented matrix method to deal with delays. Specifically, we introduce virtual nodes along with delay edges to effectively transform the system with delays into an equivalent augmented system. It is shown that for our considered systems the agents' estimates obtained by this algorithm converge to the same optimal solution. Finally, simulations examples are given to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Constrained multi-objective optimization problems (CMOPs) are commonly encountered in engineering practice. The key to effectively solving these problems lies in achieving a timely balance between convergence, diversity, and feasibility during iterations. Furthermore, the appropriate utilization of infeasible solutions is crucial for identifying potential feasible regions. In order to accomplish this comprehensive objective, we propose a novel dual-stage constrained multi-objective evolutionary algorithm (CMOEA) called NACMOEA in this paper. It can be characterized by the following features: 1) Introducing a novel niche-based individual selection and infeasible solution utilization strategy to enhance convergence, diversity, and feasibility. 2) Presenting a cooperative search strategy assisted by dual archives to approximate the constrained Pareto front (CPF) from both feasible and infeasible perspectives, thereby improving the efficiency of obtaining the complete CPF. 3) Designing a new stage switch method based on non-dominant coverage rate to ensure proper completion of search stage switching. Extensive experiments demonstrate that NACMOEA exhibits competitive comprehensive performance when compared with other advanced CMOEAs. • The search process of the proposed NACMOEA is divided into two stages, where a new dual archive collaboration strategy is developed iteratively. • A niche-based environmental selection and an infeasible individual utilization strategy are designed to better balance the performance. • A novel non-dominant coverage rate metric is developed to facilitate appropriate switching in the search stage. • The proposed NACMOEA performs well compared to other advanced algorithms on various complex CMOP test problems. [ABSTRACT FROM AUTHOR]

Space Surveillance Network (SSN) plays a crucial role in Space Domain Awareness (SDA) as it helps to maintain the catalog of Resident Space Objects (RSOs). Howe'ver, as the population of RSOs continues to grow, the contradiction between the increasing number of RSOs and limited sensor resources will be further apparent. How to effectively utilize existing sensor resources becomes a vital concern. This paper focuses on the Multi-sensor Collaborative Observation Scheduling (MCOS) in SSN. The workflow of SSN is summarized, and a Mathematical Programming Model (MPM) is established to maximize observation profit. Given the complexity of MPM, a modified Particle Swarm Optimization is proposed by combining Reverse learning, Neighbor adjustment, and Particle perturbation (RNP-PSO). In the MPM, multiple constraints such as observation time for each RSO, observation frequency, and sensor capability are considered, enabling a comprehensive description of the MCOS problem for the first time. The RNP-PSO incorporates a reverse learning algorithm to improve the quality of the initial population. Additionally, continuous mapping is added to the neighbor adjustment process to enhance the algorithm's search capabilities. Furthermore, a particle perturbation strategy is proposed to increase the diversity of solutions and avoid local optima. A series of experiments have been conducted to evaluate the performance of the RNP-PSO algorithm in solving the MCOS problem. The results demonstrate that compared to several other effective algorithms, the RNP-PSO algorithm significantly improves the Task Completion Rate (TCR) by approximately 17.01%, 19.1%, 13.53%, and 13.99%, while simultaneously reduces the Resource Consumption Rate (RCR) by about 55.48% and 36.38%. The experiments also include strategy and parameter sensitivity verification, which thoroughly validate the performance of the RNP-PSO algorithm in various aspects. [ABSTRACT FROM AUTHOR]

Deep learning is attracting increasing attention due to its excellent predictive power and its ability to be applied in traditional research areas. In this paper, we propose a multisource response prediction network architecture based on a long short-term memory (LSTM)-stacked autoencoder to predict key crashworthiness indicators and complete curve reconstruction. Taking an expansion tube as an example using an equivalent scaling research method, a scaled expandable tubular (SET) finite element model was established and verified using a quasistatic compression test and a full-size coupler and buffer system experiment. A design of experiments (DOE) approach was used to obtain a dataset for training the prediction network. Neural network hyperparameters are critical to network prediction accuracy, and after comparison, the multisource response prediction network architecture showed good computational efficiency and satisfactory prediction accuracy when appropriate hyperparameters were selected. Subsequently, multiobjective constraint optimization was performed using the nondominated sorting genetic algorithm-II (NSGA-II) based on a prediction network architecture, which greatly improved the energy-absorption structure optimization accuracy. The results are expected to provide a research methodology for solving complex engineering problems by establishing a new framework for deep learning algorithms combined with optimization methods. • A multisource response prediction network was proposed for crashworthiness prediction. • Datasets of crashworthiness indicators and time histories were efficiently constructed. • Neural network hyperparameters have an important effect on prediction. • Efficient prediction of crashworthiness indicators and curves was achieved. • Multiobjective optimization was performed using NSGA-II with the proposed network. [ABSTRACT FROM AUTHOR]

• An optimum-pursuing method is developed for constrained optimization and reliability-based design optimization problems. • An optimum-pursuing function is proposed from the viewpoint of the optimization theory. • The well-known U function is rigorously derived based on the proposed optimum-pursuing function. • Optimum-pursuing method outperforms alternative methods in terms of both accuracy and efficiency. This paper proposes a new active learning method named as optimum-pursuing method (OPM) from the viewpoint of optimization theory, which aims to provide an effective tool for solving constrained optimization and reliability-based design optimization (RBDO) problems with low computation cost. It uses the cheap Kriging metamodel to replace the expensive physical response. The novelty of the proposed OPM primarily lies in two aspects. First, the OPM utilizes the advantage of the optimization theory rather than sampling technology. By using the augmented Lagrangian approach, it comprehensively considers the objective, constraints, and their relations, thereby automatic identification of important region in the vicinity of the optimum. Second, the accordingly optimum-pursuing function consists of three parts: Kriging mean, Kriging standard deviation, and merit function. Also, the target reliability surface is further considered to enhance the local accuracy of the reliability analysis. The performance of OPM is tested for both deterministic optimization and problems, in which two mathematical and three real-world engineering examples are selected to showcase the feasibility and validity. The results demonstrate that OPM is promising for solving both deterministic optimization and RBDO problems by comparing with the well-known active learning methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved version of Twin SVM using a non-smooth optimization method. Twin SVM generally consists in determining two non-parallel planes by alternately solving two constrained optimization models. Solving this problem using the classical Lagrangian method has many limitations, notably: its only limited to handle Gaussian noise, generally exaggerates the influence of outliers and cannot handle unbalanced data, this due to the differentiability of the model. To circumvent these issues, we transform two-constraint optimization models using the penalty method into an unconstrained non-smooth optimization one. The non-smoothness nature of the problem has many advantages, but it requires special treatment, which is why we use the primal dual method to solve it, since it is the most appropriate and it is robust in terms of stability, convergence and speed (Lyaqini, Nachaoui and Hadri, 2022). To demonstrate the effectiveness of the proposed approach, several experiments were carried out on numerous UCI benchmarks, medical image and HandPD datasets. These experiments demonstrated the effectiveness and applicability of the proposed approach, with satisfactory results compared to the state of the art. [ABSTRACT FROM AUTHOR]

How to establish a simple and efficient nonlinear optimization problem form-finding model in noise environments is the main concern of this paper, thereby, a sequential quadratic programming form-finding (SQP) approach via a noise-tolerant zeroing neural network (NTZNN) is presented to solve the proposed problem. First, the rank of force density matrix is selected as optimization objective due to its crucial physical property. In addition, linear relations between the force density vectors and force density matrix are selected as the constraint conditions, which can ensure the tensegrity mechanism in a stable status. Furthermore, the simulations are shown that the presented SQP-NTZNN algorithm has superiority with anti-noise performance and computation efficiency, which by comparing the computational rate in the same noise environment with the novel form-finding algorithm. Eventually, the form-finding simulations under different initial conditions are formed to verify the importance of the constrained conditions, it can prove the high efficiency of the proposed nonlinear constrained optimization model which is transformed from the form-finding problem. [ABSTRACT FROM AUTHOR]

This paper focuses on designing surrogate models that have uncertainty quantification capabilities to effectively improve the thermal performance of rib-turbulated internal cooling channels. To construct the surrogate, we use the deep operator network (DeepONet) framework, a novel class of neural networks designed to approximate mappings between infinite-dimensional spaces using relatively small datasets. The proposed DeepONet takes an arbitrary rib geometry as input and outputs continuous detailed pressure and heat transfer distributions around the profiled ribs. The datasets needed to train and test the proposed DeepONet framework were obtained by simulating a 2D rib-roughened internal cooling channel. To accomplish this, we continuously modified the input rib geometry by adjusting the control points according to a simple random distribution with constraints, rather than following a predefined path or sampling method. The studied channel has a hydraulic diameter, D h , of 66.7 mm, and a length-to-hydraulic diameter ratio, L / D h , of 10. The ratio of rib center height to hydraulic diameter (e / D h), which was not changed during the rib profile update, was maintained at a constant value of 0.048. The ribs were placed in the channel with a pitch-to-height ratio (P / e) of 10. In addition, we provide the proposed surrogates with effective uncertainty quantification capabilities. This is achieved by converting the DeepONet framework into a Bayesian DeepONet (B-DeepONet). B-DeepONet samples from the posterior distribution of DeepONet parameters using the novel framework of stochastic gradient replica-exchange MCMC. Finally, we demonstrate the performance of the proposed DeepONet-based surrogate models with uncertainty quantification by incorporating them into a constrained, gradient-free optimization problem that enhances the thermal performance of the rib-turbulated internal cooling channel. • Develop a deep operator learning-based surrogate model. • Develop a Bayesian DeepONet model with uncertainty quantification capability. • Optimize internal cooling channel rib profiles using the surrogate model. [ABSTRACT FROM AUTHOR]

The fuzzy analytic hierarchy process (FAHP) is a widely implemented approach for determining the weights of criteria and alternatives from pairwise comparison matrices. FAHP methodology uses triangular fuzzy as a tool for the decision-maker to arrange judgments expressed as the pairwise comparison matrix elements. FAHP is then modeled into a nonlinear constrained optimization problem to increase the decision maker's satisfaction. However, in some cases, this model would yield multiple optimal solutions. Logarithmic fuzzy preference programming (LFPP) is offered to overcome this unfavorable situation. Nevertheless, LFPP includes a parameter in its model for which no specific method is available to determine. Meanwhile, the differences in parameter values will affect the solution. In the worst case, the weights obtained will be inaccurate. As a result, the decision taken based on the weights will be biased and unreliable. This issue is particularly important when it requires high accuracy. It includes the decisions on the portfolio investment, economic, human capital measurements, and shipping registry. Another crucial thing is that the use of the natural logarithm function in the LFPP method has the potential for an overflow effect at the computational stage when it is implemented to derive the weights. Therefore, in the application context, it is significant to improve the shortcomings of the LFPP method in terms of parameter involvement and the use of the logarithmic function. For this purpose, this paper proposes a non-parameter transcendental fuzzy preference programming (TFPP) model for deriving the optimal weights from fuzzy pairwise comparison matrices. The TFPP is obtained by investigating different functions' effects on fuzzy preference programming (FPP). The proposed method has a more general form. One of the specific forms of the TFPP is chosen in a computational implementation. The numerical results confirm that the TFPP optimization model is reliable in obtaining the optimum global weights. The comparison with the LFPP method also shows that the TFPP is more efficient. • Logarithmic fuzzy preference programming (LFPP) includes a parameter in its model. • The parameter incorporated in LFPP affects the optimality of the solution. • Some transcendental functions employed to replace the logarithmic function in LFPP. • Transcendental fuzzy preference programming performs better than the LFPP. [ABSTRACT FROM AUTHOR]

In this study, a novel meta-heuristic search (MHS) algorithm for constrained global optimization problems is proposed. Since many algorithms aim to achieve well-balanced exploitation–exploration stages with often unsatisfactory results, in the approach introduced in this paper, Attraction–Repulsion Optimization Algorithm (AROA), the balance associated with attraction–repulsion phenomena that occur in nature is mimicked. AROA introduces a search strategy in which a candidate solution is moved in the search space depending on the quality of solutions in its neighborhood, as well as the best candidate. The candidates are managed by local search operators based on modified Brownian motion, trigonometric functions, randomly selected solutions, and a form of memory. Consequently, AROA exhibits a satisfactory exploitation–exploration balance exhibited by highly competitive performance. The introduced algorithm is experimentally compared with the state-of-the-art meta-heuristics on the CEC 2014, 2017, and 2020 test suites. The obtained results reveal the advantages of AROA over related algorithms and its suitability in solving complex real-world problems. • A novel attraction–repulsion search scheme is introduced. • Proposed meta-heuristic is governed by balanced search operators. • Algorithm is validated on three demanding CEC benchmarks. [ABSTRACT FROM AUTHOR]