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To address the challenge of performance portability and facilitate the implementation of electronic structure solvers, we developed the basic matrix library (BML) and Parallel, Rapid O(N), and Graph-based Recursive Electronic Structure Solver (PROGRESS) library. The BML implements linear algebra operations necessary for electronic structure kernels using a unified user interface for various matrix formats (dense and sparse) and architectures (CPUs and GPUs). Focusing on density functional theory and tight-binding models, PROGRESS implements several solvers for computing the single-particle density matrix and relies on BML. In this paper, we describe the general strategies used for these implementations on various computer architectures, using OpenMP target functionalities on GPUs, in conjunction with third-party libraries to handle performance critical numerical kernels. We demonstrate the portability of this approach and its performance in benchmark problems. [ABSTRACT FROM AUTHOR]

In this research, we present an advanced numerical technique developed by adhering to the consistency condition, leveraging the characteristics of polynomial cubic spline approximations. Our method is specifically tailored for tackling nonlinear equations like the Kuramoto-Sivashinsky and Korteweg-de Vries equations. Unlike conventional finite difference methods, which encounter difficulties in addressing singular biharmonic problems directly, our approach overcomes this hurdle. By incorporating off-step points in the spatial dimension, our method facilitates the direct resolution of singular problems. We assess stability using von Neumann stability analysis on a linear model. Chaotic simulations of the nonlinear Kuramoto–Sivashinsky equation are conducted across various time stages. Single-soliton of the Korteweg-de Vries equation is solved numerically at different time levels. Our proposed technique exhibits high accuracy, demonstrating its efficacy compared to prior research. Additionally, we delve into solving diverse benchmark problems numerically, evaluating global relative and maximum absolute errors in comparison with previous studies. [ABSTRACT FROM AUTHOR]

Weighted-type finite-difference schemes are a class of widely used nonlinear schemes that can capture strong discontinuities accurately and efficiently. For the Euler equations without source terms, poor convergence of weighted-type schemes is a widely known difficulty in finding steady-state solutions with strong shock waves. The primary reason for this lies in the fact that classical weighted-type schemes produce spurious oscillations near strong discontinuities. Recently, a novel weighted-type scheme has been developed. The nonlinear weights of the new scheme are fourth-order accurate and do not reduce the accuracy at the high-order critical points, which is beneficial for steady-state convergence. In this paper, we compare the convergence performances of classical and new weighted-type schemes in detail. Several benchmark problems containing shock waves, contact discontinuities, and rarefaction waves were used to compare the convergence performance among different weighted-type schemes. The results show that the new weighted-type scheme basically eliminates slight post-shock oscillations, and the residual settles to machine zero. Compared to classical weighted-type schemes, the steady-state convergence performance of the new weighted-type scheme is significantly improved. [ABSTRACT FROM AUTHOR]

This paper presents in-depth exploration and verification of the OpenNode nodal diffusion code, a robust tool designed for multigroup neutron diffusion simulations under steady-state conditions. Leveraging the Nodal Expansion Method with a quartic polynomial and moments weighting method, OpenNode demonstrates exceptional accuracy in approximating nodal surface fluxes, further enhanced by the Quadratic Transverse Leakage approximation. The critical concept of commutativity between adjoint and forward solutions is thoroughly investigated, serving as a benchmark for the code's reliability in predicting system responses, determining single-point reactor kinetics parameters, and facilitating perturbation analyses. The paper meticulously details OpenNode's methodology for adjoint neutron flux computation, unraveling its rigorous approach through transposition operations and intricate mathematical transformations. Noteworthy features, including support for second and fourth polynomial orders; versatile computation modes; different mesh points; and seamless integration with Python, PyQt5, and Blender, underscore OpenNode's adaptability. Results from comprehensive analysis of the two-dimensional and three-dimensional International Atomic Energy Agency core benchmark problem showcase OpenNode's prowess. The code excels in reactor geometry visualizations, benchmark parameters, and neutronic analysis, with a particular emphasis on commutativity verification against various benchmarked codes. The precision of OpenNode is further demonstrated in power distribution analyses, revealing remarkable proximity to reference values and symmetrical power distribution patterns. [ABSTRACT FROM AUTHOR]

This study explores reduced-order modeling for analyzing time-dependent diffusion-deformation of hydrogels. The full-order model describing hydrogel transient behavior consists of a coupled system of partial differential equations in which the chemical potential and displacements are coupled. This system is formulated in a monolithic fashion and solved using the finite element method. We employ proper orthogonal decomposition as a model order reduction approach. The reduced-order model performance is tested through a benchmark problem on hydrogel swelling and a case study simulating co-axial printing. Then, we embed the reduced-order model into an optimization loop to efficiently identify the coupled problem's material parameters using full-field data. Finally, a study is conducted on the uncertainty propagation of the material parameter. [ABSTRACT FROM AUTHOR]

This paper proposes a family of weighted- θ high-order ADI schemes for sine-Gordon equations in high dimensions in combination with the discretization of a three-level linearized scheme in temporal direction and a classical compact difference scheme in spatial direction. The unique solvability, convergence and stability of the difference scheme in two dimensions are strictly proved with the convergence order four in space and order two in time under the L ∞ -norm. More remarkably, the present numerical scheme can be extended to cope with the three-dimensional case feasibly. Several benchmark problems in two and three dimensions verify theoretical results and the capacity in simulating the dynamic behaviour of ring solitons. Specially, several novel numerical isosurfaces in three dimensions are presented for the first time. [ABSTRACT FROM AUTHOR]

We suggest using a GMRES preconditioner to speed up the convergence of Krylov subspace methods for solving linear equations with a block structure. These types of equations commonly arise in unfitted finite element discretizations of the Darcy flow problem in a fracture network. We have thoroughly examined the spectrum of the preconditioned matrix to demonstrate its effectiveness. Additionally, we have conducted numerical experiments to highlight how well the preconditioners work with flexible-GMRES when solving linear equations from benchmark test problems. [ABSTRACT FROM AUTHOR]

Whale optimization algorithm (WOA) is a new bio‐meta‐heuristic algorithm presented to simulate the predatory humpback whales' behavior in the ocean. In previous studies, WOA has been observed to exhibit lower accuracy and slower convergence rates. In this paper, we propose an improved the WOA by innovatively incorporating an adaptive fitness‐distance balance strategy, namely AFWOA. AFWOA can continuously and efficiently identify the maximum potential candidate solutions from the population within the search process, thus improving the accuracy rate and convergence speed of the algorithm. Through various experiments in IEEE CEC2017 and an ill‐conditional problem, AFWOA is proven to be more competitive than the original WOA, several other state‐of‐the‐art WOA variants and other four classic meta‐heuristic algorithms. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Inspired by the gender transition behavior seen in hawkfish, this paper introduces the HawkFish optimization algorithm, a nature-inspired optimization technique modeled on the unique gender transition behavior of hawkfish. By leveraging this biological phenomenon, the proposed method addresses optimization problems through dual fitness functions, combining an original and inverse fitness function to drive search space exploration while avoiding local minima. The algorithm's performance is rigorously evaluated against benchmark problems, including the CEC/GECCO 2019 suite, and applied to real-world engineering challenges like welded beam and tension/compression spring design. The proposed method consistently outperforms existing algorithms in terms of convergence rate, accuracy, and solution quality. The results underscore the algorithm's efficiency in exploring unknown search spaces and solving complex optimization tasks, making it a promising tool for various domains requiring high precision and optimization efficiency. [ABSTRACT FROM AUTHOR]

The remarkable progress of deep learning in dermatological tasks has brought us closer to achieving diagnostic accuracies comparable to those of human experts. However, while large datasets play a crucial role in the development of reliable deep neural network models, the quality of data therein and their correct usage are of paramount importance. Several factors can impact data quality, such as the presence of duplicates, data leakage across train-test partitions, mislabeled images, and the absence of a well-defined test partition. In this paper, we conduct meticulous analyses of three popular dermatological image datasets: DermaMNIST, its source HAM10000, and Fitzpatrick17k, uncovering these data quality issues, measure the effects of these problems on the benchmark results, and propose corrections to the datasets. Besides ensuring the reproducibility of our analysis, by making our analysis pipeline and the accompanying code publicly available, we aim to encourage similar explorations and to facilitate the identification and addressing of potential data quality issues in other large datasets. [ABSTRACT FROM AUTHOR]

Multi-modal multi-objective optimization problems (MMOPs) are challenging due to multiple solutions sharing similar objective values. Existing algorithms for solving MMOPs typically evaluate the crowding in the decision space and objective space independently, leading to an imbalance in diversity between the two spaces. We introduce a mechanism that balances diversity in both the decision and objective spaces, aiming to enhance diversity while maintaining convergence in both spaces. We propose a multi-modal multi-objective evolutionary algorithm (MMEA) that selects qualified solutions based on Gaussian similarity. Gaussian similarity assesses the closeness of solution pairs and serves as the diversity fitness criterion for the algorithm. We conducted experiments on 28 benchmark problems and compared MMEA-GS with five state-of-the-art approaches. The results demonstrate that MMEA-GS effectively addresses most MMOPs, achieving higher diversity and convergence. [ABSTRACT FROM AUTHOR]

The Dual Mesh Control Domain Method (DMCDM), developed by Reddy (J.N. Reddy. "A dual mesh finite domain method for the numerical solution of differential equations." Int J Comput Methods Eng Sci,20(3):212–228, 2019), is an alternative to the classical weak‐form Galerkin finite element method. An advantage of DMCDM is that it combines the interpolation capabilities of the finite element method with the direct use of an integral form of the balance laws. Furthermore, it is easily extensible to mixed formulations, resulting in simpler than traditional finite element formulations. In this work, we extend DMCDM to the fully finite strain case with plasticity. We introduce a new discretization algorithm for finite strain problems, which includes a mean‐dilatation technique to solve the volumetric locking problem. Assessment is supported by five linear and five finite strain benchmark problems, one of them being 3D. Finite strain solutions were found to be stable, exempt from hourglassing, and also locking‐free. Results are found to be competitive with classical F‐bar and B‐bar elements, with a simpler formulation. [ABSTRACT FROM AUTHOR]

AbstractThis article examines the assumptions of rotation-based shear measures, such as those used in Timoshenko beam theory and rotation-based finite elements, in the large-deformation analysis of soft materials that experience significant changes in geometry. In such applications, the shear strain does not depend only on the rotation of the beam cross section, but it also depends strongly on the stretches of the longitudinal and transverse gradient vectors. The effect of the independent gradient stretches, which can be significant in case of soft materials, is not accounted for when using shear measures that employ rotations only. A shear-stretch function (SSF) and shear-rotation function (SRF) are introduced to quantify the effect of the independent gradient stretches on the shear strain. The effects of the stretch and rotation on the shear strain are compared using a large-deformation flexible-beam example that can serve as a benchmark problem in future investigations. [ABSTRACT FROM AUTHOR]

Workflow tasks in the cloud environment are the abstraction and decomposition of large‐scale and complex tasks in real‐world scenarios, so cloud workflow scheduling problems have important research significance. However, most of the existing cloud workflow scheduling schemes are aimed at a single workflow, and do not make reasonable use of the commonality or complementary knowledge between similar tasks. Moreover, most cloud workflow scheduling models mainly focus on a few objectives such as time or cost, which is not comprehensive enough. Therefore, this paper first proposes a multi‐objective cloud workflow scheduling model, which solves the maximum completion time, execution cost and energy consumption as three objectives during task execution. Secondly, to efficiently handle multiple similar cloud workflow scheduling tasks at the same time, this paper treats various cloud workflow scheduling issues as distinct tasks, establishes a multi‐task cloud workflow scheduling framework that aims for the same goal while accommodating workflows of differing scales, and a multi‐objective evolutionary multi‐task optimization algorithm based on elite selection (MOEMT‐ES) is designed to solve the above scheduling model. Finally, through algorithm comparison experiments on the CEC2017 evolutionary multi‐task optimization competition benchmark problem and multi‐workflow test problem, MOEMT‐ES shows superior competitiveness. [ABSTRACT FROM AUTHOR]

Optical Ising machines have emerged as a promising dynamical hardware solver for computational hard optimization problems. These Ising machines typically require an optical modulator to represent the analog spin variables of these problems. However, modern day optical modulators have a relatively low modulation resolution. We therefore investigate how the low bit-resolution of optical hardware influences the performance of this type of computing platform. Based on numerical simulations, we determine the minimum required bit-resolution of an optical Ising machine for different benchmark problems of different sizes. Our study shows that a limited bit-resolution of 8bit is sufficient for the optical modulator. We also observe that the use of a 1bit-resolution modulator significantly improves the performance of the Ising machine across all considered benchmark problems. Optical Ising machines are hardware systems designed to efficiently solve complex optimization problems by using optical modulators to encode the problem variables. The authors' simulations show that while an 8bit modulation resolution is sufficient, using just a 1bit resolution not only works but also enhances performance across several benchmark problems. [ABSTRACT FROM AUTHOR]

In this work, a higher-order compact discretization is developed on the nonuniform polar grid. The discretization carries theoretically optimal third-order convergence in space and second order in time on a (5, 5) stencil. Conceptualized using the unsteady convection-diffusion equation, it is further extended to flow problems governed by the Navier–Stokes equations as well as the Boussinesq equations. The scheme developed here combines the advantages of body-fitted mesh with grid clustering, thereby making it efficient to capture flow gradients on polar grids. The scheme carries a spatial convergence of order three with temporal order of convergence being almost two. Diverse flow problems are being investigated using the scheme. Apart from a verification study, we validate the scheme by time marching simulation for the benchmark problem of a driven polar cavity and the problem of natural convection in the horizontal concentric annulus. In the process, a one-sided approximation for the Neumann boundary condition for vorticity is also presented. Finally, the benchmark problem of forced convection around a circular cylinder is tackled. The results obtained in this study are analyzed and compared with the well-established numerical and experimental data wherever available in the literature. The newly developed scheme is found to generate accurate solutions in each case. [ABSTRACT FROM AUTHOR]

Early-stage design of complex systems is considered by many to be one of the most critical design phases because that is where many of the major decisions are made. The design process typically starts with low-fidelity tools, such as simplified models and reference data, but these prove insufficient for novel designs, necessitating the introduction of high-fidelity tools. This challenge can be tackled through the incorporation of multifidelity models. The application of multifidelity (MF) models in the context of design optimization problems represents a developing area of research. This study proposes incorporating compositional kernels into the autoregressive scheme (AR1) of multifidelity Gaussian processes, aiming to enhance the predictive accuracy and reduce uncertainty in design space estimation. The effectiveness of this method is assessed by applying it to five benchmark problems and a simplified design scenario of a cantilever beam. The results demonstrate significant improvement in the prediction accuracy and a reduction in the prediction uncertainty. Additionally, the article offers a critical reflection on scaling up the method and its applicability in early-stage design of complex engineering systems, providing insights into its practical implementation and potential benefits. [ABSTRACT FROM AUTHOR]

This paper proposes the capacity flexibility models of transit networks to describe the ability of urban transit networks to accommodate the changes in passenger demands. In order to keep the variations of the average passenger travel time in a reasonable way, the concept of coefficient of variation (CoV) is introduced to measure and limit the deviation from a baseline time of the average passenger travel time. The CoV could transform the capacity flexibility values to non-dimensional form in order to compare the capacity flexibility characteristics of different scale transit networks on an equal basis. The genetic algorithm with deep search (GA-DS) for this problem is proposed to find the approximating optimal solution of the capacity flexibility models more effectively. The algorithm has been tested with benchmark problems reported in the existing literature. The optimal solution of the flexibility models shows that the flexibility values are further improved by reducing the number of stops for transit routes under different transit operation conditions. At last, the influence factors of the capacity flexibility of transit systems are discussed to address reliable transit services. [ABSTRACT FROM AUTHOR]

ArborX is a performance portable geometric search library developed as part of the Exascale Computing Project (ECP). In this paper, we explore a collaboration between ArborX and a cosmological simulation code HACC. Large cosmological simulations on exascale platforms encounter a bottleneck due to the in-situ analysis requirements of halo finding, a problem of identifying dense clusters of dark matter (halos). This problem is solved by using a density-based DBSCAN clustering algorithm. With each MPI rank handling hundreds of millions of particles, it is imperative for the DBSCAN implementation to be efficient. In addition, the requirement to support exascale supercomputers from different vendors necessitates performance portability of the algorithm. We describe how this challenge problem guided ArborX development, and enhanced the performance and the scope of the library. We explore the improvements in the basic algorithms for the underlying search index to improve the performance, and describe several implementations of DBSCAN in ArborX. Further, we report the history of the changes in ArborX and their effect on the time to solve a representative benchmark problem, as well as demonstrate the real world impact on production end-to-end cosmology simulations. [ABSTRACT FROM AUTHOR]

A benchmark problem is formulated which is well suited for the validation of mesoscopic phase-field models for grain-boundary migration in polycrystals. First, an analytical steady-state solution of the sharp moving boundary problem is derived for a symmetric lamellar structure, which is valid for arbitrary bulk driving forces and triple junction angles. Characteristic quantities are identified to reduce the parameter space which in turn allows a systematic comparison of simulations and analytical results. Various multiphase-field (MPF) formulations are compared which approximate the sharp interface problem in terms of a diffuse regularization. An interfacial thickness convergence study reveals that the model error is largely dependent on the ratio of bulk to interfacial stabilizing force as well as the underlying model formulation. An additional grid convergence study highlights the efficiency of a more advanced discretization scheme. The results can be used to guide the selection of appropriate models and to estimate the interface thickness and spatial resolution required to achieve a given accuracy target. The post-processing framework consists of a fully automated determination of well-defined metrics from the phase field simulation data, eliminating human bias and facilitating reproducibility. The corresponding code is made openly available to assist the materials science and engineering community in validating MPF, multi-order parameter and similar model developments. We believe that this work provides a reliable benchmark procedure to better understand the potentials and limitations of current MPF models as well as alternative approaches. [ABSTRACT FROM AUTHOR]

In this paper a new procedure for modeling brittle fracture processes in concrete components is proposed in the framework of the so-called discrete crack approach and exploiting ergodic behaviour with respect to the refinement of the mesh. The complexity in controlling localized crack development in quasi-brittle materials like concrete usually necessitates very fine meshes that are associated with a very high computational cost. In this work, a method is proposed that exploits the ergodic behavior with respect to mesh refinement/density observed in simulations performed on randomly generated meshes. The proposed method is framed in the discrete crack concept, which avoids or minimizes mesh dependence. It considers the interaction between elastic solids or aggregates and nonlinear interfaces, where the fractures are described based on the relative displacements at the joints where energy dissipation occurs. To allow significant geometric flexibility in the modeling of elastic aggregates, Voronoi discretizations of virtual elements are used in combination with nonlinear interface elements. Numerical simulations of benchmark problems demonstrate that the proposed procedure accurately predicts failure behavior in brittle materials. Specifically, the peak load, crack path, and load–displacement relationship obtained from the averaged behavior of coarse-mesh simulations closely match those from very fine-mesh simulations. The results demonstrate that by taking advantage of both the well-known mesh flexibility of virtual element method and the observed ergodic behavior with respect to mesh density, the proposed procedure represents an efficient and accurate tool for modeling failure behavior in quasi-brittle materials such as concrete. [ABSTRACT FROM AUTHOR]

A smoothed naturally stabilized conforming nodal integration (S-NSNI) for the reproducing kernel particle method (RKPM) is proposed for non-linear explicit dynamics. The Taylor series expansion of the nodal strains in the solid variational formulation is employed, which introduces stabilization by enriching the energy of the originally underintegrated system. As a result, the higher-order gradients of meshfree shape functions are required, and are approximated by using the gradient smoothing technology. In conjunction with implicit gradients, this results in a formulation devoid of computationally intensive differentiation of meshfree shape functions. In addition, when conforming smoothing domains are employed, the formulation is variationally consistent and converges optimally while passing the patch test. The smoothed framework further alleviates a numerical locking in the original NSNI by avoiding direct differentiation that leads to inaccuracies in stabilization terms. To enhance the nontrivial computation of the required stress gradients or estimates there of, a novel stress re-interpolation methodology is introduced, which is favorable for the implementation of arbitrary constitutive laws such as those in commercial codes, which are often plentiful. The framework is developed for both Lagrangian and semi-Lagrangian RKPM and is applicable to both moderate and extreme deformations. The effectiveness of the proposed methodology is demonstrated by the implementation into the KC-FEMFRE and MEGA codes and applied to several benchmark problems that include elastic, nearly incompressible, and plastic materials, as well as geomaterial modeling using continuum damage mechanics coupled with plasticity. [ABSTRACT FROM AUTHOR]

Purpose: The rise of remote work increasingly requires organizations to coordinate a single large, consolidated talent pool into ad-hoc, short-term project teams on demand. This problem involves many simultaneous considerations including project revenues and rejection costs, conflicting projects and roles, worker assignment costs, worker utilization preferences and limits, worker reassignment costs, and arbitrary role start and end times. Moreover, plans must be continuously updated in response to changing circumstances. This paper addresses the problem of dynamic virtual team planning and coordination. Design/methodology/approach: We show this problem is NP-hard and provide a dynamic mixed integer linear programming (MILP) formulation for both optimal initial plan generation as well as continuous plan adjustment and re-optimization. We utilized a factorial experiment design to generate benchmark problems spanning a wide range of characteristics and conducted extensive computational experimentation using a common MILP solver. Findings: Exactly optimal solutions to large, realistically sized problems were consistently obtained in short amounts of time. All observed solution times were sufficient to support the operational decision-making requirements of real-world virtual team coordination, demonstrating the viability of this approach. Practical implications: The approach developed in this research can enable organizations to optimally coordinate virtual teams on a large scale and continually adjust plans in response to changing circumstances, all in an automated manner. Originality/value: This paper addresses a new and complex problem of increasing importance to organizations due to the rise in remote work. We provide a problem formulation and exact approach for optimally solving both the planning and re-planning aspects of this problem. [ABSTRACT FROM AUTHOR]

The advent and evolution of Industry 4.0 have been driven by technologies such as the Industrial Internet of Things, Big Data, and Cloud Computing. Within this framework, digital twins have gained significant popularity and are now employed across a wide range of industries and processes. A crucial step in developing a digital twin is deriving the system model, for which numerous methods are available. Among these, the Koopman operator and Dynamic Mode Decomposition with control have demonstrated their effectiveness and are widely recognized in the scientific community. This paper proposes a procedure for the automatic selection of Koopman observables by solving an optimization problem. The objective is to identify the minimal set of observables, belonging to a predefined dictionary, that minimize the error between actual process observations and predictions made by the estimated linear model—a key requirement for digital twin development. To tackle the optimization challenge, any algorithm available in the literature can be utilized. In this paper, the evolutive algorithms, including Genetic Algorithm and Differential Evolution Algorithm, are applied to evaluate the proposed approach in a benchmark problem. In both cases, the algorithms obtained the minimum set of observable functions from the dictionary used that achieve the lowest error obtained between the real process and the model, confirming the validity of the proposed method. [ABSTRACT FROM AUTHOR]

The Fusion Neutron Source (FNS) clean benchmark experiments on tungsten, vanadium, and beryllium assemblies from the SINBAD (Shielding Integral Benchmark Archive and Database) are analyzed to experimentally validate OpenMC (version 0.14.1-dev) fusion neutronics capabilities. The assemblies were irradiated with a 14-MeV deuterium-tritium neutron source. Neutron spectra, photon spectra, reaction rates, gamma heating rates (GHRs), and tritium production rates (TPRs) are compared to measured data in the experimental assemblies and MCNP-6.2 results. In the tungsten case, slight overestimations of the experimental data were observed in the neutron spectra, and the photon spectra agreed well with the experiments. Most of the GHRs agreed with the measured data within the range of experimental uncertainty in the tungsten and vanadium assemblies. In the vanadium assembly, the calculated neutron spectra underestimated the experiments in the low energy region while the photon spectra were well calculated when compared to experiments. The most noticeable discrepancies with experimental data in the gamma heating were observed at detector positions closest to the source. For the reaction rates, notable discrepancies with experimental data were seen at the front and rear of the assemblies. Compared to experiments, the OpenMC neutron spectra were well predicted in the beryllium assembly, whereas the calculated fission reaction rate and TPRs overestimated the experiments, an observation similar to that which has been reported by other authors. The average, overall calculation-to-experiment ratio (C/E) over nine TPR and seven GHR measurements were 1.03 ± 0.20 and 0.95 ± 0.14, respectively. In the case of verification, the OpenMC results of the benchmark calculations indicated comparable accuracy to MCNP-6.2. In general, the validation exercise showed that OpenMC can be used to analyze the fusion neutronics shielding benchmark problems. [ABSTRACT FROM AUTHOR]

Metaheuristics are commonly used in various fields, including real-life problem-solving and engineering applications. The present work introduces a novel metaheuristic algorithm named the Artificial Circulatory System Algorithm (ACSA). The control of the circulatory system inspires it and mimics the behavior of hormonal and neural regulators involved in this process. The work initially evaluates the effectiveness of the suggested approach on 16 two-dimensional test functions, identified as classical benchmark functions. The method was subsequently examined by application to 12 CEC 2022 benchmark problems of different complexities. Furthermore, the paper evaluates ACSA in comparison to 64 metaheuristic methods that are derived from different approaches, including evolutionary, human, physics, and swarm-based. Subsequently, a sequence of statistical tests was undertaken to examine the superiority of the suggested algorithm in comparison to the 7 most widely used algorithms in the existing literature. The results show that the ACSA strategy can quickly reach the global optimum, avoid getting trapped in local optima, and effectively maintain a balance between exploration and exploitation. ACSA outperformed 42 algorithms statistically, according to post-hoc tests. It also outperformed 9 algorithms quantitatively. The study concludes that ACSA offers competitive solutions in comparison to popüler methods. [ABSTRACT FROM AUTHOR]

This article presents for the very first time a layerwise bending analytical solution for cross-ply laminated composite plates with clamped boundary conditions at all edges. The displacement field is implemented within the framework of the Carrera unified formulation at a layer level by employing Legendre polynomials. The governing equations are obtained by using the principle of virtual displacements statement. This work utilizes the boundary-discontinuous double Fourier series to provide analytical solutions. The high accuracy of the proposed solution is demonstrated by comparing the results with three-dimensional finite-element model (FEM) solutions of multilayered and sandwich plates for various side-to-thickness ratios. In conclusion, the highly accurate proposed solution might be used as a benchmark problem for new analytical or FEMs. [ABSTRACT FROM AUTHOR]

The past few years of the COVID‐19 pandemic outbreak have shown that optimal management of medical specimens is a key aspect of healthcare logistics, both for addressing the delivery of perishable items such as vaccines, and for ensuring the timely analysis of swabs and samples. Accordingly, recent optimization literature described the problem of optimal collection and delivery of medical specimens, modeled as a multitrip vehicle routing problem with time windows and a completion time objective function. Aiming to achieve good‐quality solutions in short computational times, this work describes a hybrid approach, combining a matheuristic construction phase with an adaptive large neighborhood search (ALNS). Our matheuristic relies on a clustering algorithm to yield subsets of the medical specimens that are optimally served by single vehicles of the fleet. The solutions of the matheuristic phase serve as starting points for the ALNS intensification phase. Extensive experimentation on both new and established benchmark problem instances shows that our hybrid method is able to match the optimality of the state of the art on small instances and outperforms the existing exact method by one order of magnitude on larger problems. [ABSTRACT FROM AUTHOR]

Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? How should features with different units be aligned, and how should their relative importance be weighted? Here, we introduce the Differentiable Information Imbalance (DII), an automated method to rank information content between sets of features. Using distances in a ground truth feature space, DII identifies a low-dimensional subset of features that best preserves these relationships. Each feature is scaled by a weight, which is optimized by minimizing the DII through gradient descent. This allows simultaneously performing unit alignment and relative importance scaling, while preserving interpretability. DII can also produce sparse solutions and determine the optimal size of the reduced feature space. We demonstrate the usefulness of this approach on two benchmark molecular problems: (1) identifying collective variables that describe conformations of a biomolecule, and (2) selecting features for training a machine-learning force field. These results show the potential of DII in addressing feature selection challenges and optimizing dimensionality in various applications. The method is available in the Python library DADApy. Feature selection across diverse features in molecular systems is challenging. Here, the authors present Differentiable Information Imbalance (DII), a method to optimize feature weights, align units, and find the best feature set size, available in the Python library DADApy. [ABSTRACT FROM AUTHOR]

Predicting the mechanical response of materials subjected to transient heat processes is crucial in various engineering applications. This paper introduces a novel peridynamic (PD) coupled field model formulated within the framework of Non-Ordinary State-Based Peridynamics (NOSBPD). This model offers the capability to simulate the coupled thermo-mechanical behavior of materials by incorporating both thermal transport and mechanical deformation analyses. To verify the model's accuracy, a benchmark problem involving a steel plate undergoing transient thermal expansion is investigated. This model presents a valuable tool for various engineering applications involving coupled thermo-mechanical processes. [ABSTRACT FROM AUTHOR]

In this paper, topology optimization (TO) problems of structures subjected to design-dependent loads are formulated in the context of a special density-based TO approach wherein a Non Uniform Rational Basis Spline (NURBS) hyper-surface is used to represent the topological descriptor. Unlike classical density-based TO approaches, the NURBS-density-based method allows representing the pseudo-density field through a purely geometric computer-aided design compatible entity. In this context, the problem is formulated in the most general case of inhomogeneous Neumann-Dirichlet boundary conditions. Moreover, a thorough study of the penalty function of the design-dependent loads is carried out to investigate its effect on the optimized topologies and overcome the singularity effect related to the zones characterized by low values of the pseudo-density field. Finally, a wide campaign of sensitivity analyses is conducted to investigate the influence of the integer parameters of the NURBS entity, of the combination of design-depedent loads and inhomogeneous Neumann-Dirichlet boundary conditions, and of the concentrated load on the optimized topology. The effectiveness of the approach is tested on 2D and 3D benchmark problems. [ABSTRACT FROM AUTHOR]

The nonlinear geometrically exact (GeX) hybrid-mixed four-node solid-shell element under non-conservative loading, developed in the companion paper, is here extended to the stress analysis of functionally graded material (FGM) shells. The term GeX means that the middle surface is described by analytical functions, including spline functions, and, therefore, the coefficients of the first and second fundamental forms can be taken exactly at element nodes. The FGM solid-shell element formulation is based on the choice of N sampling surfaces (SaS) parallel to the middle surface and located at Chebyshev polynomial nodes within the shell as well as on the outer surfaces, to introduce the displacements of these surfaces as basic unknowns. The use of Lagrange polynomials of degree N–1 in the distributions of displacements, strains, stresses, and elastic coefficients through the thickness of the shell leads to an efficient 3N-parameter shell formulation. To develop the FGM four-node solid-shell element based on the hybrid-mixed method, in which strains and stresses are utilized as primary variables, the Hu-Washizu variational principle is applied. Due to the analytical integration used to evaluate the tangent stiffness matrix, the developed FGM solid-shell element subjected to displacement-dependent loads shows superior performance in the case of coarse meshes and allows the use of only one load step in most of the considered benchmark problems. It has been established that the difference between the second Piola-Kirchhoff and Cauchy stress tensors in non-conservative problems for FGM shells undergoing moderately large strains can be significant. [ABSTRACT FROM AUTHOR]

Real-time hybrid simulation (RTHS) is a widely applied test method in structural engineering, which is developed from pseudo-dynamic test. Much of the past work has been centered on one-dimensional RTHS using a single hydraulic actuator. When the complexity of the problem demands to increase the number of degrees of freedom to be enforced on the boundary conditions, more than one hydraulic actuator must be used. Multiple-actuator or multi-axial RTHS (maRTHS) requires that more than one hydraulic actuator exerts the required motion on experimental substructures demanding the implementation of multiple-input multiple-output (MIMO) control strategies. A new maRTHS benchmark control problem has been developed, focusing on a frame subjected to seismic load at the base, substantially transforming and intensifying the complexity of the problem. The time delay generated by the dynamic characteristics of the loading system and the transmission process as well as the high coupling between the hydraulic actuators and the nonlinear kinematics escalates the complexity of the actuator control tracking. A sliding mode adaptive delay compensation method suitable for maRTHS is proposed, which utilizes a MIMO sliding mode method to reduce the coupling effects of actuators and the adaptive compensation method to compensate the residual delay. The effectiveness of the method is verified by numerical simulating different working conditions in the Benchmark Problem Platform. [ABSTRACT FROM AUTHOR]

The Sparrow Search Algorithm (SSA) is recognized for its rapid convergence and precision in engineering optimization, yet it faces the challenge of premature convergence on complex problems. To address this, a multi-strategy improved sparrow search algorithm (MISSA) is proposed to enhance the optimization performance and applicability in this study. For the first time in the algorithm, the first definition of ellipses is integrated into SSA to balance its exploration and exploitation capabilities. A group co-evolutionary mechanism is introduced to promote population diversity and suppress premature convergence. Unlike most existing work, ablation experiments are utilized to evaluate the effective impact of these enhancement strategies on SSA. Statistical results based on the Wilcoxon signed-rank test and Friedman test show that the dynamic regulator based on the first definition of ellipses has the greatest impact on improving the performance of SSA. Numerical experiments based on the CEC2017 benchmark problems are used as an optimization case to compare MISSA with the classical metaheuristic algorithm and other state-of-the-art variants of SSA. The results demonstrate the outstanding performance and immense potential of MISSA in problem-solving. The applicability of the proposed algorithm is validated through six actual engineering optimization problems, showcasing strong competitiveness in global optimization. [ABSTRACT FROM AUTHOR]

In this work, a low‐order virtual element method (VEM) with adaptive meshing is applied for thermodynamic topology optimization with linear elastic material for the two‐dimensional case. VEM has various significant advantages compared to other numerical discretization techniques, for example the finite element method (FEM). One advantage is the flexibility to use arbitrary shaped elements including the possibility to add nodes on the fly during simulation, which opens a new variety of application fields. The latter mentioned feature is used in this publication to propose an adaptive mesh‐refinement strategy during the thermodynamic optimization procedure and investigate its feasibility. The thermodynamic topology optimization (TTO) includes a efficient gradient‐enhanced approach to regularization of the otherwise ill‐posed density based topology optimization approach and was already applied to various material models in combination with finite elements. However, the special numerical treatment leading to efficiency of the regularization approach within the TTO has to be modified to be applicable to VEM, which is presented in the publication. We show the performance of this framework by investigating several numerical results on benchmark problems. [ABSTRACT FROM AUTHOR]

This paper presents a model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows. A scenario of new vehicle routing is analyzed in which customers are asked to provide alternative time windows to offer flexibility to help route planners find more fuel-efficient routes ("green delivery"). Customers can rank their preferred time windows as first, second, and third. The optimization model aims to reduce tour costs, promote electromobility over fossil fuels, such as diesel, and meet customer preferences when possible and affordable. The study incorporates a multi-objective optimization model with three objectives, which are overall cost, use of fossil fuel, and customer satisfaction. For the new problem, a set of realistic benchmark problems is created and four mainstream solvers are applied for the Pareto front approximation: NSGA-II, NSGA-III, MOEA/D, and SMS-EMOA. These algorithms are compared in terms of their effectiveness in achieving the objectives of minimizing travel costs, promoting electromobility, and meeting customer preferences. The study uses five different problems of single-vehicle route planning. Two major findings are that the selection of the metaheuristic can make a big difference in terms of algorithm performance. The resulting 3-D Pareto fronts reveal the nature of this new class of problems: Interestingly, in the new model with flexible time windows, most users can still be delivered in their most preferred time windows with only small concessions to the other objectives. However, using only one time window per user can lead to an increasingly drastic cost and fossil fuel consumption. [ABSTRACT FROM AUTHOR]

A numerical optimization technique used to address nonlinear programming problems is the trust region (TR) method. TR uses a quadratic model, which may represent the function adequately, to create a neighbourhood around the current best solution as a trust region in each step, rather than searching for the original function's objective solution. This allows the method to determine the next local optimum. The TR technique has been utilized by numerous researchers to tackle multi‐objective optimization problems (MOOPs). But there is not any publication that discusses the issue of applying a chaotic search (CS) with the TR algorithm for solving multi‐objective (MO) problems. From this motivation, the main contribution of this study is to introduce trust‐region (TR) technique based on chaotic search (CS) for solving MOOPs. First, the reference point interactive approach is used to convert MOOP to a single objective optimization problem (SOOP). The search space is then randomly initialized with a set of initial points. Second, in order to supply locations on the Pareto boundary, the TR method solves the SOOP. Finally, all points on the Pareto frontier are obtained using CS. A range of MO benchmark problems have demonstrated the efficiency of the proposed algorithm (TR based CS) in generating Pareto optimum sets for MOOPs. Furthermore, a demonstration of the suggested algorithm's ability to resolve real‐world applications is provided through a practical implementation of the algorithm to improve an abrasive water‐jet machining process (AWJM). [ABSTRACT FROM AUTHOR]

An adaptive large neighborhood search (ALNS) lacks a clear measure for assessing the improvement degree in the new solution, which causes fuzziness and uncertainty in the operator score. To solve the aforementioned problem, the main innovation of this study is to propose an adaptive neutrosophic large neighborhood search (ANLNS). Specifically, the main work is as follows. Firstly, the number of times each operator scores are quantified by constructing NSs, thereby analyzing algorithm performance and preventing the idealization of scores. Secondly, a novel neutrosophic utility function and score function are proposed to assign an appropriate score for the operator within a reasonable interval. Finally, the effectiveness and robustness of the proposed ANLNS is validated by the capacitated vehicle routing problem benchmarks with three varying scales and comparative analyses. The compared results indicate that the proportion of best solutions for ANLNS are 50%, 100%, and 37.5%, which significantly highlight the robustness and reliability of the proposed algorithm when the degree of destruction is 0.3, 0.5, and 0.7, respectively. Meanwhile, the proposed ANLNS is efficient and flexible, providing a novel method for addressing other situation optimization problems. [ABSTRACT FROM AUTHOR]

Plan deordering removes unnecessary ordering constraints between actions in a plan, facilitating plan execution flexibility and several other tasks, such as plan reuse, modification, and decomposition. Block deordering is a variant of plan deordering that encapsulates coherent actions into blocks to eliminate further ordering constraints from a partial-order plan (POP) and is useful in many applications (e.g., generating macro-actions and improving the overall plan quality). The existing block deordering strategy is formulated in propositional encodings. Finite-domain state variable encodings (e.g., SAS+ representation), in contrast with propositional encodings, can capture the internal structure and the behavior of state variables of a planning instance through concise constructs such as causal graphs (CGs) and domain transition graphs (DTGs). This work redefines the semantics of block deordering terminologies and related plan deordering concepts in finite domain representation (FDR). Our proposed semantics also resolves some limitations of the existing block semantics and further enhance plan flexibility. In addition, this work exploits block deordering to eliminate redundant actions from a POP. A comparative analysis is also performed on block deordering with various deordering/reordering techniques using explanation-based order generalization (EOG) and MaxSAT. Our experiments on the benchmark problems from International Planning Competitions (IPC) show that our FDR formalism of block deordering significantly improves the plan execution flexibility while maintaining good coverage and execution time. [ABSTRACT FROM AUTHOR]

Genetic Programming (GP) often uses large training sets and requires all individuals to be evaluated on all training cases during selection. Random down-sampled lexicase selection evaluates individuals on only a random subset of the training cases, allowing for more individuals to be explored with the same number of program executions. However, sampling randomly can exclude important cases from the down-sample for a number of generations, while cases that measure the same behavior (synonymous cases) may be overused. In this work, we introduce Informed Down-Sampled Lexicase Selection. This method leverages population statistics to build down-samples that contain more distinct and therefore informative training cases. Through an empirical investigation across two different GP systems (PushGP and Grammar-Guided GP), we find that informed down-sampling significantly outperforms random down-sampling on a set of contemporary program synthesis benchmark problems. Through an analysis of the created down-samples, we find that important training cases are included in the down-sample consistently across independent evolutionary runs and systems. We hypothesize that this improvement can be attributed to the ability of Informed Down-Sampled Lexicase Selection to maintain more specialist individuals over the course of evolution, while still benefiting from reduced per-evaluation costs. [ABSTRACT FROM AUTHOR]

Territorial Differential Meta-Evolution (TDME) is an efficient, versatile, and reliable algorithm for seeking all the global or desirable local optima of a multivariable function. It employs a progressive niching mechanism to optimize even challenging, high-dimensional functions with multiple global optima and misleading local optima. This paper introduces TDME and uses standard and novel benchmark problems to quantify its advantages over HillVallEA, which is the best-performing algorithm on the standard benchmark suite that has been used by all major multimodal optimization competitions since 2013. TDME matches HillVallEA on that benchmark suite and categorically outperforms it on a more comprehensive suite that better reflects the potential diversity of optimization problems. TDME achieves that performance without any problem-specific parameter tuning. [ABSTRACT FROM AUTHOR]

The molten salt reactor (MSR) flowing-fuel simulation capability of the Griffin-Pronghorn-coupled multiphysics code system developed by Idaho National Laboratory (INL) was verified against the Center National de la Recherche Scientifique (CNRS) MSR benchmark problem. Griffin and Pronghorn, which are INL's neutronics and thermal-hydraulics codes built upon the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, have been recently extended to handle the flowing fuel of MSRs causing the drift of delayed neutron precursors (DNP). In the Griffin-Pronghorn code system, Griffin provides the fission rate density to Pronghorn, which simulates the generation, decay, and transport of DNPs along with the fluid, and the redistributed DNP densities are fed back to Griffin. The coupling and transfers are largely automatically managed at the framework level by the powerful MultiApp system of MOOSE. The verification results against the CNRS benchmark problem demonstrate that the Griffin-Pronghorn code system can accurately simulate the unique physics phenomena of MSRs in both steady-state and transient conditions. [ABSTRACT FROM AUTHOR]

This article presents a bi-level non-uniform cellular automata (CA) algorithm for the solution of sizing, topology and layout optimization of truss structures. The non-uniform CA was successfully used in a previous study to solve the weight optimization problem of truss structures for topology and sizing (El Bouzouiki, Sedaghati, and Stiharu 2021. Computers & Structures 242: 106394). In this article, an extended version of the non-uniform CA algorithm is proposed, based on the fully stressed design approach and the distribution of strain energy within the structure, to find the optimal position of the cell's (joint's) coordinates. The proposed non-uniform CA algorithm can solve the minimum weight optimization problem of truss structures subjected to both stress and displacement constraints. Several benchmark problems are presented to demonstrate the efficiency and accuracy of the proposed methodology. [ABSTRACT FROM AUTHOR]

In this manuscript, we study a benchmark tracking problem when prices evolve through a Binomial model with a random environment. The agent invests a given fund's capital into different assets in a predetermined market to replicate at each stage of time a financial index or benchmark. To measure the actual deviation between the agent's wealth and the current benchmark, we apply a deviation error expressed as a total sum of quadratic functions. We also assume the agent is obligated to change her/his investment between two markets when it is mandatory. This obligation happens when the fund's wealth falls down a predetermined bankruptcy barrier. The dynamic programming method is then used to get optimal investment strategies that minimize the deviation error as well as to characterize the minimum deviation. We also apply the so-called potential function to analyze the influence of the environment on the prices. Numerical simulations are provided to illustrate our results. [ABSTRACT FROM AUTHOR]

Determining whether two STRIPS planning instances are isomorphic is the simplest form of comparison between planning instances. It is also a particular case of the problem concerned with finding an isomorphism between a planning instance P$$ P $$ and a sub‐instance of another instance P′$$ {P}^{\prime } $$. One application of such a mapping is to efficiently produce a compiled form containing all solutions to P$$ P $$ from a compiled form containing all solutions to P′$$ {P}^{\prime } $$. We also introduce the notion of embedding from an instance P$$ P $$ to another instance P′$$ {P}^{\prime } $$, which allows us to deduce that P′$$ {P}^{\prime } $$ has no solution‐plan if P$$ P $$ is unsolvable. In this paper, we study the complexity of these problems. We show that the first is GI‐complete and can thus be solved, in theory, in quasi‐polynomial time. While we prove the remaining problems to be NP‐complete, we propose an algorithm to build an isomorphism when possible. We report extensive experimental trials on benchmark problems that demonstrate conclusively that applying constraint propagation in preprocessing can greatly improve the efficiency of a SAT solver. [ABSTRACT FROM AUTHOR]

The numerical solution of continuum damage mechanics (CDM) problems suffers from convergence-related challenges during the material softening stage, and consequently existing iterative solvers are subject to a trade-off between computational expense and solution accuracy. In this work, we present a novel unified arc-length (UAL) method, and we derive the formulation of the analytical tangent matrix and governing system of equations for both local and non-local gradient damage problems. Unlike existing versions of arc-length solvers that monolithically scale the external force vector, the proposed method treats the latter as an independent variable and determines the position of the system on the equilibrium path based on all the nodal variations of the external force vector. This approach renders the proposed solver substantially more efficient and robust than existing solvers used in CDM problems. We demonstrate the considerable advantages of the proposed algorithm through several benchmark 1D problems with sharp snap-backs and 2D examples under various boundary conditions and loading scenarios. The proposed UAL approach exhibits a superior ability of overcoming critical increments along the equilibrium path. Moreover, in the presented examples, the proposed UAL method is 1–2 orders of magnitude faster than force-controlled arc-length and monolithic Newton–Raphson solvers. [ABSTRACT FROM AUTHOR]

In this manuscript, we propose an efficient algorithm for solving a class of nonlinear operator equations. The algorithm is an improved version of previously established method. The algorithm's features are as follows: (i) the search direction is bounded and satisfies the sufficient descent condition; (ii) the global convergence is achieved when the operator is continuous and satisfies a condition weaker than pseudo-monotonicity. Moreover, by comparing it with previously established method the algorithm's efficiency was shown. The comparison was based on the iteration number required for each algorithm to solve a particular problem and the time taken. Some benchmark test problems, which included monotone and pseudo-monotone problems, were considered for the experiments. Lastly, the algorithm was utilized to solve the logistic regression (prediction) model. [ABSTRACT FROM AUTHOR]

In this manuscript, a novel approach to topology optimization is proposed which integrates considerations of uncertain load positions, thereby enhancing the reliability-based design within the context of structural engineering. Extending the conventional framework to encompass imperfect geometrically nonlinear analyses, this research discovers the intricate interplay between nonlinearity and uncertainty, shedding light on their combined effects on probabilistic analysis. A key innovation lies in treating load position as a stochastic variable, augmenting the existing parameters, such as volume fraction, material properties, and geometric imperfections, to capture the full spectrum of variability inherent in real-world conditions. To address these uncertainties, normal distributions are adopted for all relevant parameters, leveraging their computational efficacy, simplicity, and ease of implementation, which are particularly crucial in the context of complex optimization algorithms and extensive analyses. The proposed methodology undergoes rigorous validation against benchmark problems, ensuring its efficacy and reliability. Through a series of structural examples, including U-shaped plates, 3D L-shaped beams, and steel I-beams, the implications of considering imperfect geometrically nonlinear analyses within the framework of reliability-based topology optimization are explored, with a specific focus on the probabilistic aspect of load position uncertainty. The findings highlight the significant influence of probabilistic design methodologies on topology optimization, with the defined constraints serving as crucial conditions that govern the optimal topologies and their corresponding stress distributions. [ABSTRACT FROM AUTHOR]

In this article, we propose, analyze and demonstrate a dynamic momentum method to accelerate power and inverse power iterations with minimal computational overhead. The method can be applied to real diagonalizable matrices, is provably convergent with acceleration in the symmetric case, and does not require a priori spectral knowledge. We review and extend background results on previously developed static momentum accelerations for the power iteration through the connection between the momentum accelerated iteration and the standard power iteration applied to an augmented matrix. We show that the augmented matrix is defective for the optimal parameter choice. We then present our dynamic method which updates the momentum parameter at each iteration based on the Rayleigh quotient and two previous residuals. We present convergence and stability theory for the method by considering a power‐like method consisting of multiplying an initial vector by a sequence of augmented matrices. We demonstrate the developed method on a number of benchmark problems, and see that it outperforms both the power iteration and often the static momentum acceleration with optimal parameter choice. Finally, we present and demonstrate an explicit extension of the algorithm to inverse power iterations. [ABSTRACT FROM AUTHOR]

(1) Background: The mutual relationship between evolution and learning is a controversial argument among the artificial intelligence and neuro-evolution communities. After more than three decades, there is still no common agreement on the matter. (2) Methods: In this paper, the author investigates whether combining learning and evolution permits finding better solutions than those discovered by evolution alone. In further detail, the author presents a series of empirical studies that highlight some specific conditions determining the success of such combination. Results are obtained in five qualitatively different domains: (i) the 5-bit parity task, (ii) the double-pole balancing problem, (iii) the Rastrigin, Rosenbrock and Sphere optimization functions, (iv) a robot foraging task and (v) a social foraging problem. Moreover, the first three tasks represent benchmark problems in the field of evolutionary computation. (3) Results and discussion: The outcomes indicate that the effect of learning on evolution depends on the nature of the problem. Specifically, when the problem implies limited or absent agent–environment conditions, learning is beneficial for evolution, especially with the introduction of noise during the learning and selection processes. Conversely, when agents are embodied and actively interact with the environment, learning does not provide advantages, and the addition of noise is detrimental. Finally, the absence of stochasticity in the experienced conditions is paramount for the effectiveness of the combination. Furthermore, the length of the learning process must be fine-tuned based on the considered task. [ABSTRACT FROM AUTHOR]