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The energetically most favorable chemical ordering of bimetallic nanoparticles can be characterized by combining global optimization algorithms and surrogate energy models. The latter approximate the energy of nanoalloys relying on structural descriptors, training models, and data. Here, we systematically evaluate the performance of highly data-efficient topological descriptors [Kozlov et al., Chem. Sci. 6, 3868 (2015)] for predicting the energies of metal nanoalloys with different chemical orderings. We also introduce a new descriptor based on atomic coordination types, which results in a less data-efficient and interpretable approach, but improves the general accuracy and the quantification of orderings in the inner parts of nanoparticles. The capacity of both the original and new approaches in combination with a basin hopping algorithm is illustrated by generating convex hulls of PdZn nanoalloys and predicting the resulting active surface site distribution as a function of particle composition. Finally, we show how these approaches can be combined with machine-learning adsorption models in electrocatalysis studies for a fast evaluation of the reactivity landscape of targeted nanoalloys. [ABSTRACT FROM AUTHOR]

We present the results for energy landscapes of hexapeptides obtained using interfaces to the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) program. We have used basin-hopping global optimization and discrete path sampling to explore the landscapes of hexapeptide monomers, dimers, and oligomers containing 10, 100, and 200 monomers modeled using a residue-level coarse-grained potential, Mpipi, implemented in LAMMPS. We find that the dimers of peptides containing amino acid residues that are better at promoting phase separation, such as tyrosine and arginine, have melting peaks at higher temperature in their heat capacity compared to phenylalanine and lysine, respectively. This observation correlates with previous work on the same uncapped hexapeptide monomers modeled using atomistic potential. For oligomers, we compare the variation in monomer conformations with radial distance and observe trends for selected angles calculated for each monomer. The LAMMPS interfaces to the GMIN and OPTIM programs for landscape exploration offer new opportunities to investigate larger systems and provide access to the coarse-grained potentials implemented within LAMMPS. [ABSTRACT FROM AUTHOR]

Energy landscapes and the closely related cost function landscapes have been recognized in science, mathematics, and various other fields such as economics as being highly useful paradigms and tools for the description and analysis of the properties of many systems, ranging from glasses, proteins, and abstract global optimization problems to business models. A multitude of algorithms for the exploration and exploitation of such landscapes have been developed over the past five decades in the various fields of applications, where many re-inventions but also much cross-fertilization have occurred. Twenty-five years ago, trying to increase the fruitful interactions between workers in different fields led to the creation of workshops and small conferences dedicated to the study of energy landscapes in general instead of only focusing on specific applications. In this perspective, I will present some history of the development of energy landscape studies and try to provide an outlook on in what directions the field might evolve in the future and what larger challenges are going to lie ahead, both from a conceptual and a practical point of view, with the main focus on applications of energy landscapes in chemistry and physics. [ABSTRACT FROM AUTHOR]

We introduce an atomistic classifier based on a combination of spectral graph theory and a Voronoi tessellation method. This classifier allows for the discrimination between structures from different minima of a potential energy surface, making it a useful tool for sorting through large datasets of atomic systems. We incorporate the classifier as a filtering method in the Global Optimization with First-principles Energy Expressions (GOFEE) algorithm. Here, it is used to filter out structures from exploited regions of the potential energy landscape, whereby the risk of stagnation during the searches is lowered. We demonstrate the usefulness of the classifier by solving the global optimization problem of two-dimensional pyroxene, three-dimensional olivine, Au12, and Lennard-Jones LJ55 and LJ75 nanoparticles. [ABSTRACT FROM AUTHOR]

Pump–probe thermoreflectance has been commonly applied for characterizing the thermal properties of materials. Generally, a reliable and efficient non-linear fitting process is often implemented to extract unknown thermal parameters during the pump–probe thermoreflectance characterizations. However, when it comes to processing large amounts of data acquired from similar structural samples, non-linear fitting process appears to be very time-consuming and labor-intensive to search for the best fitting for every testing curve. Herein, we propose to apply deep learning (DL) approach to nanosecond transient thermoreflectance technique for high-throughput experimental data processing. We first investigated the effect of training set parameters (density and bounds) on the predictive performance of the DL model, providing a guidance to optimize the DL model. Then, the DL model is further verified in the measurement of the bulk sapphire, SiC, diamond samples, and GaN-based multilayer structures, demonstrating its capability of analyzing the results with high accuracy. Compared to the conventional non-linear fitting method (such as Global Optimization), the computation time of the new model is 1000 times lower. Such a data-driven DL model enables the faster inference and stronger fitting capabilities and is particularly efficient and effective in processing data acquired from wafer-level measurements with similar material structures. [ABSTRACT FROM AUTHOR]

The paper describes a smart model for palletising as main intra-logistics task for product-on-pallet distribution, and its development in Logistics 4.0 framework aligned to Industry 4.0. The model is developed in holonic paradigm as a 2-layer Holonic Logistics Execution System (HLES) in semiheterarchical topology. Scheduling of logistics activities and allocation to resources are optimised for global efficiency on compact time periods. The logistics model virtualises physical entities: resources, pallets and orders as holons implemented with digital twin software, and categories of task workloads: resource health monitoring, dispatching and tracking pallet orders, re-assigning jobs at resource failure, which grants reality-awareness and robustness. Global workload optimisation uses Constraint Programming as decision making technology with ILOG optimiser engine as situation-specific solver tool in SaaS cloud model, and delegate multi-agent system (MAS) technology for intelligence distribution. The optimised objective function is a combination of palletising cost weighted by robot speed limits and pallet storage cost in payable stocks. The main constructs are exemplified and validated on a real-life structure with multiple palletising resources in which programmable logic controllers (PLC) coordinate locally the parallel execution of order holons according to their globally optimised sequence. [ABSTRACT FROM AUTHOR]

A differential evolution (DE) global optimization method for all-electron orbital-free density functional theory (OF-DFT) is presented. This optimization method does not need information about function derivatives to find extreme solutions. Results for a series of known orbital-free energy functionals are presented. Ground state energies of atoms (H to Ar) are obtained by direct minimization of the energy functional without using either Lagrange multipliers or damping procedures for reaching convergence. Our results are in agreement with previous OF-DFT calculations obtained using the standard Newton–Raphson and trust region methods. Being a zero-order method, the DE method can be applied to optimization problems dealing with non-differentiable functionals or functionals with non-closed forms. [ABSTRACT FROM AUTHOR]

In this contribution, we employ computational tools from the energy landscape approach to test Gaussian Approximation Potentials (GAPs) for C60. In particular, we apply basin-hopping global optimization and explore the landscape starting from the low-lying minima using discrete path sampling. We exploit existing databases of minima and transition states harvested from previous work using tight-binding potentials. We explore the energy landscape for the full range of structures and pathways spanning from the buckminsterfullerene global minimum up to buckybowls. In the initial GAP model, the fullerene part of the landscape is reproduced quite well. However, there are extensive families of C1@C59 and C2@C58 structures that lie lower in energy. We succeeded in refining the potential to remove these artifacts by simply including two minima from the C2@C58 families found by global landscape exploration. We suggest that the energy landscape approach could be used systematically to test and improve machine learning interatomic potentials. [ABSTRACT FROM AUTHOR]

In this contribution, we employ computational tools from the energy landscape approach to test Gaussian Approximation Potentials (GAPs) for C60. In particular, we apply basin-hopping global optimization and explore the landscape starting from the low-lying minima using discrete path sampling. We exploit existing databases of minima and transition states harvested from previous work using tight-binding potentials. We explore the energy landscape for the full range of structures and pathways spanning from the buckminsterfullerene global minimum up to buckybowls. In the initial GAP model, the fullerene part of the landscape is reproduced quite well. However, there are extensive families of C1@C59 and C2@C58 structures that lie lower in energy. We succeeded in refining the potential to remove these artifacts by simply including two minima from the C2@C58 families found by global landscape exploration. We suggest that the energy landscape approach could be used systematically to test and improve machine learning interatomic potentials. [ABSTRACT FROM AUTHOR]

The significance of rigorous optimization techniques in antenna engineering has grown significantly in recent years. For many design tasks, parameter tuning must be conducted globally, presenting a challenge due to associated computational costs. The popular bio-inspired routines often necessitate thousands of merit function calls to converge, generating prohibitive expenses whenever the design process relies on electromagnetic (EM) simulation models. Surrogate-assisted methods offer acceleration, yet constructing reliable metamodels is hindered in higher-dimensional spaces and systems with highly nonlinear characteristics. This work suggests an innovative technique for global antenna optimization embedded within a machine-learning framework. It involves iteratively refined kriging surrogates and particle swarm optimization for generating infill points. The search process operates within a reduced-dimensionality region established through fast global sensitivity analysis. Domain confinement enables the creation of accurate behavioral models using limited training data, resulting in low CPU costs for optimization. Additional savings are realized by employing variable-resolution EM simulations, where low-fidelity models are utilized during the global search stage (including sensitivity analysis), and high-fidelity ones are reserved for final (gradient-based) tuning of antenna parameters. Comprehensive verification demonstrates the consistent performance of the proposed procedure, its superiority over benchmark techniques, and the relevance of the mechanisms embedded into the algorithm for enhancing search process reliability, design quality, and computational efficiency. [ABSTRACT FROM AUTHOR]

To address the path planning problem for unmanned combat aerial vehicle (UCAV) more effectively, a novel two-stage path planning model is proposed. The first stage involves a longitudinal search primarily aimed at predicting the initial path, while the second stage is a horizontal search designed to correct the initial path. Furthermore, to tackle the UCAV path planning issue more effectively, this paper designs an improved elephant clan optimization (IECO) algorithm based on the average sample learning strategy, opposition-based learning, and Lévy flight disturbance strategy. Subsequently, IECO is integrated with the two-stage model (TSIECO) to address the UCAV path planning problem. Additionally, numerical experiments across 15 test functions reveal that IECO outperforms other algorithms in terms of optimization capability and convergence speed. Finally, the UCAV path planning experimental results indicate that the two-stage model based on IECO, as proposed in this paper, has significant advantages over traditional path planning models based on other swarm intelligence algorithms. Specifically, in three different simulated environments, the TSIECO has been tested on a total of 9 maps with varying parameters, yielding paths that are optimal in terms of cost and stability. [ABSTRACT FROM AUTHOR]

In this study, we have managed to model the energy surface of the aminophenol derivative azomethine molecule mathematically depending on two torsion angles SC1(C9C10C12N14) and SC2(C2C1C6C11). For this purpose, firstly, discrete data obtained from Density Functional Theory calculations have been converted into continuous data with the help of the Fuzzy Inference System. Thus, it is possible to calculate energy values for untested data, which are very costly in terms of time to obtain with other methods/experiments. Then, the continuous and non-smooth surface obtained from the fuzzy inference system and representing the energy values of the molecule has been transformed into a differentiable surface with the help of Bezier surfaces. Thus, an objective function has been obtained in which global optimization methods based on the derivative (or gradient) operator could be used. [ABSTRACT FROM AUTHOR]

Optimization problems are common across various fields, and one effective solution is the swarm intelligence algorithm.It is essential for the algorithm to deliver high-quality solutions for problems with varying characteristics. However, most existing swarm intelligence rely on fixed and monotonic search strategies, which limits their ability to handle the diverse and complex situations encountered when solving real-world optimization problems with unknown fitness landscapes. To extend the applicability of swarm intelligence and thus offer users an efficient black-box optimizer for various applications, a novel self-learning mechanism is proposed and applied to the Salp Swarm Algorithm (SSA) to develop the self-learning salp swarm algorithm (SLSSA) in this paper. In SLSSA, four distinct search strategies, including a novel multiple food sources search strategy, are adopted to strengthen the search agents' abilities to conquer various difficulties in the search space. To improve the efficiency of the search process, the self-learning strategy dynamically determines the execution probability of each search strategy according to the quality of solutions it produced previously. Moreover, a parameter setting method is proposed in this paper, which eliminates the need for a trial-and-error approach and allows for straightforward configuration of the parameters that optimize the performance of SLSSA. In comparison with several highly regarded state-of-the-art peer algorithms, the performance of SLSSA in solving the CEC2014 benchmark functions was thoroughly examined. Subsequently, SLSSA was applied to train multi-layer perceptron classifiers and test on the UCI machine-learning datasets. The experimental results and analysis on benchmark functions and multi-layer perceptron classifier training problems demonstrate that SLSSA outperforms the competing algorithms in terms of solution accuracy, stability, and overall convergence speed. Moreover, computational time comparisons reveal that SLSSA achieves significant performance improvement with only a marginal increase in time cost compared to the original SSA. [ABSTRACT FROM AUTHOR]

A novel analytical method for evaluating the buckling stability of the pier–cap–pile system under scour effect is proposed, and furthermore, the concept of rapid assessment of bridge buckling stability based on natural frequency is innovatively introduced in this paper. First, the total potential energy function of the bridge pier–cap–pile system is established, and the closed-form solution of structural system critical buckling load under scouring is solved according to the principle of stationary potential energy. Second, the sensitivity analyses of pier height, pier width, bending stiffness reduction in pier, pile slenderness ratio, pile bending stiffness reduction, m-value of soil and structural gravity to the buckling load are carried out. On this basis, 60 sets of sensitive parameter sample combinations are extracted by the Latin Hypercube Sampling algorithm, and a proxy model relating the critical buckling load to scour depth and the sensitive parameters are then developed using the McQuarter global optimization algorithm. Finally, the regression relationship model between the critical buckling load and the natural frequency is formed by combining the proxy model and the existing relationship between the scour depth and the first-order natural frequency. The feasibility of the proposed assessment method is demonstrated by the finite element method using a bridge case study, which provides an insight and a new means of quantitatively evaluating the safety of bridges under scour effect. [ABSTRACT FROM AUTHOR]

ABSTRACT The inversion of subsurface geological structures is a crucial approach for gaining insights into the internal composition of the earth. In this paper, we propose a novel inversion method combining the nonsingular indirect boundary element method (IBEM) with the multistrategy particle swarm optimization (MSPSO) algorithm, tailored for accurately inverting 3D subsurface cavities. Leveraging the semi‐analytical nature of IBEM offers advantages such as dimensionality reduction, automatic fulfillment of radiation conditions at infinity, and high computational accuracy. Furthermore, to augment global optimization and local search capabilities, an MSPSO algorithm is introduced. Employing multiple optimization strategies enhances particle diversity, accelerates algorithm convergence, and mitigates the risk of local optima. Through the consideration of subsurface cavities with varying parameters, this method quickly identifies the approximate location of the cavity within a wide search range. The final results demonstrate that the proposed method can simultaneously and accurately invert the 3D spatial position, size, and orientation of the cavity. [ABSTRACT FROM AUTHOR]

We consider a simplicial branch and bound Global Optimization algorithm, where the search region is a simplex. Apart from using longest edge bisection, a simplicial partition set can be reduced due to monotonicity of the objective function. If there is a direction in which the objective function is monotone over a simplex, depending on whether the facets that may contain the minimum are at the border of the search region, we can remove the simplex completely, or reduce it to some of its border facets. Our research question deals with finding monotone directions and labeling facets of a simplex as border after longest edge bisection and reduction due to monotonicity. Experimental results are shown over a set of global optimization problems where the feasible set is defined as a simplex, and a global minimum point is located at a face of the simplicial feasible area. [ABSTRACT FROM AUTHOR]

The development of innovative materials, based on the modern technologies and processes, is the key factor to improve the energetic sustainability and reduce the environmental impact of electrical equipment. In particular, the modeling of magnetic hysteresis is crucial for the design and construction of electrical and electronic devices. In recent years, additive manufacturing techniques are playing a decisive role in the project and production of magnetic elements and circuits for applications in various engineering fields. To this aim, the use of the deep learning paradigm, integrated with the most common models of the magnetic hysteresis process, has become increasingly present in recent years. The intent of this paper is to provide the features of a wide range of deep learning tools to be applied to magnetic hysteresis context and beyond. The possibilities of building neural networks in hybrid form are innumerable, so it is not plausible to illustrate them in a single paper, but in the present context, several neural networks used in the scientific literature, integrated with various hysteretic mathematical models, including the well-known Preisach model, are compared. It is shown that this hybrid approach not only improves the modeling of hysteresis by significantly reducing computational time and efforts, but also offers new perspectives for the analysis and prediction of the behavior of magnetic materials, with significant implications for the production of advanced devices. [ABSTRACT FROM AUTHOR]

Recovering the shape of an object from a 3D point cloud is a challenging task due to the complex shapes and diverse requirements of different tasks. For buildings in urban scenes, maintaining geometric constraints such as parallelism, perpendicularity, symmetry, and coplanarity is important during the model fitting process. In this paper, we present a regularity-constrained point cloud reconstruction framework that comprises primitive initialization, constraint construction, and global optimization. Our approach first performs normal optimization and plane segmentation on the input point cloud. We then compute the global reference directions to set the target normal for each plane to construct the constraints. Finally, we obtain the model by globally optimizing the position and orientation of the planes while considering the constraints. Our experimental results demonstrate that our proposed algorithm not only strictly enforces geometric constraints but also closely fits the input point cloud. Furthermore, our framework outperforms state-of-the-art methods in terms of shape recovery and constraint maintenance, as demonstrated by comparative evaluations. [ABSTRACT FROM AUTHOR]

The rigid matching of two geometric clouds is vital in the computer vision and its intelligent applications, such as computational geometry, robotics, shape modelling, surface reconstruction and mapping, and many other fields. The variants of the iterative closest point algorithm were employed as the most noticeable matching algorithm. In traditional ICP algorithms applications for symmetrical geometry matching, the initial uncertainty and the multiple local minima of the distance function adversely affect the alignment process, which leads to weak performance, such as incorrect correspondence, narrow convergence region, and instability. In this study, the novel algorithm fused the ICP algorithm, particle filter and K-means clustering to correctly estimate the transformation ICP parameters. Further guide to initial values of parameters and their covariance obtained by k-means clustering. Then, a particle filter was implemented to estimate accurate values and perform global optimization. In the introduced PF-ICP algorithm, the alignment parameters: rotation angles, scale factor, and translation, were defined as particles elements optimized using a sequential importance resampling (SIR) particle filter. The proposed algorithm was implemented on a medical robot FPGA board and applied to "three symmetrical models" and "noisy and poor datasets." The calculated variances and estimated parameters were compared with four modified ICP methods. The results show a significantly increasing accuracy and convergence region with an acceptable speed for the practical conditions. [ABSTRACT FROM AUTHOR]

This paper employs the Sine Cosine Algorithm, a recent meta-heuristic method, to enhance the radiation properties of concentric elliptical antenna arrays in far-field applications, such as smart grid wireless communication and the Internet of Things. It focuses on minimizing the Sidelobe Level, a key parameter in antenna radiation, to mitigate unwanted signals and interference. The proposed method, known for its global optimization effectiveness, tackles the challenge of designing sparse multiple concentric elliptical arrays. By using the Sine Cosine Algorithm, the inter-element spacing and optimal determination of amplitude coefficients are achieved, resulting in a radiation pattern with reduced sidelobe level. To showcase the adaptability and superior performance of our proposed algorithm, we systematically evaluated its effectiveness across four distinct sets of concentric elliptical antenna arrays. These arrays comprise varying numbers of elements: (6, 12, 18 elements, 6, 12, 18, 24 elements, 6, 12, 18, 24, 30 elements and 6, 12, 18, 24, 30, 36 elements) with and without a central element. Using the sine cosine algorithm, we synthesize arrays and compare their sidelobe level and First Null Beam Width values with those from recent methods. Our findings consistently shows that the proposed algorithm consistently outperforms alternatives, yielding significantly lower sidelobe level values in all comparisons. [ABSTRACT FROM AUTHOR]

Moving vehicle-based sensors (MVSs) have been increasingly used for real-time sensing and anomaly detection in various applications such as the detection of wildfires and oil spills. In this article, we propose data-driven sampling strategies using MVSs to quickly identify abrupt changes in an area of interest in real time considering their pathwise movement constraints. To tackle challenges due to variability and partial observability of online observations, we integrate instruments of statistical process control and mathematical optimization to monitor the global status of the area of interest and adaptively adjust paths of MVSs to sample from suspicious locations based on real-time data. We provide theoretical investigations and conduct simulations to validate the superior performance of the proposed methods. In a numerical study based on real-world wildfire data, we illustrate that our proposed strategies are able to detect wildfires much earlier than benchmark methods and can significantly reduce wildfire-related costs. [ABSTRACT FROM AUTHOR]

One of the most striking examples of the close connections between global optimization processes and statistical physics is the simulated annealing method, inspired by the famous Monte Carlo algorithm devised by Metropolis et al. in the middle of last century. In this paper, we show how the tools of linear kinetic theory allow the description of this gradient-free algorithm from the perspective of statistical physics and how convergence to the global minimum can be related to classical entropy inequalities. This analysis highlights the strong link between linear Boltzmann equations and stochastic optimization methods governed by Markov processes. Thanks to this formalism, we can establish the connections between the simulated annealing process and the corresponding mean-field Langevin dynamics characterized by a stochastic gradient descent approach. Generalizations to other selection strategies in simulated annealing that avoid the acceptance–rejection dynamic are also provided. [ABSTRACT FROM AUTHOR]

The AUTO algorithm is presented to incrementally build models and solve supervised learning problems. AUTO uses traditional derivative-free optimisation, like genetic algorithms, to search the problem space of arbitrary functions. With reinforcement learning, AUTO learns actions to guide its search process and gradually improve performance. A comparative analysis is presented exploring supervised learning performance and interpretability of AUTO models. Results indicate AUTO outperforms genetic programming and rivals multilayer perceptrons. AUTO automatically builds models to closely match datasets without the limited search space of traditional supervised learning. With enough time and computational resources, AUTO can generate any function. [ABSTRACT FROM AUTHOR]

An improved grey wolf optimization (GWO) is proposed for direct L1-norm optimization in rank deficient GNSS networks to detect outliers since the standard GWO and the global optimization (GO) algorithms cannot deal with such problems. In contrast to the traditional method for L1-norm minimization which is solved by a cumbersome search of linear programming (LP) problem, in this algorithm, one only requires to input the target function. Furthermore, it has a greater ability to detect gross errors of observables with low reliability compared to traditional methods such as LP. Moreover, it does not consider the correct observables as blunders in contrast to some algorithms such as the iteratively reweighted least-squares (IRLS) method, which may consider them as outliers. The numerical results of real and simulated GNSS networks approve the efficiency of the new algorithm. [ABSTRACT FROM AUTHOR]

In recent research, metaheuristic strategies stand out as powerful tools for complex optimization, capturing widespread attention. This study proposes the Educational Competition Optimizer (ECO), an algorithm created for diverse optimization tasks. ECO draws inspiration from the competitive dynamics observed in real-world educational resource allocation scenarios, harnessing this principle to refine its search process. To further boost its efficiency, the algorithm divides the iterative process into three distinct phases: elementary, middle, and high school. Through this stepwise approach, ECO gradually narrows down the pool of potential solutions, mirroring the gradual competition witnessed within educational systems. This strategic approach ensures a smooth and resourceful transition between ECO's exploration and exploitation phases. The results indicate that ECO attains its peak optimization performance when configured with a population size of 40. Notably, the algorithm's optimization efficacy does not exhibit a strictly linear correlation with population size. To comprehensively evaluate ECO's effectiveness and convergence characteristics, we conducted a rigorous comparative analysis, comparing ECO against nine state-of-the-art metaheuristic algorithms. ECO's remarkable success in efficiently addressing complex optimization problems underscores its potential applicability across diverse real-world domains. The additional resources and open-source code for the proposed ECO can be accessed at https://aliasgharheidari.com/ECO.html and . [ABSTRACT FROM AUTHOR]

The Grey Wolf Optimizer (GWO) is one of the more successful swarm-based intelligent algorithms in recent years, but the shortcomings of the Grey Wolf Optimizer are revealed as the problems handled become progressively more complex. For this purpose, this paper presents a new variant of GWO and names its Hybrid Contact List Subpopulation Mixed Evolution Grey Wolf Optimizer (CSELGWO). In the paper first introduces the Contact List Mechanism (CLM) to obtain high quality local optimal information in the search space. This is followed by the Hybrid Contact List Subpopulation Generation (HCSG) mechanism, which utilizes the information in the Contact List to assist in the updating of the Subpopulation and interacts with the main population through Subpopulation Mixed Evolution (SME) to interact with the main population, thus significantly improving population diversity and convergence accuracy. In addition, the proposed Levy Flight with archives and Activation Mechanism (LFAA) can moving away from local optimality by reasonable judgment. We evaluated it using 66 test functions and showed excellent convergence speed, stability and accuracy. Additionally, when compared with the top-performing algorithm from the CEC2020 Real World Competition, CSELGWO demonstrates effective solutions to real-world problems. Finally, we compared LSHADE_cnEpSin with LSHADE_SPACMA. Although CSELGWO does not outperform these LSHADE variants in terms of convergence accuracy and standard deviation obtained, it shows excellent performance on certain types of functions, indicating excellent potential. [ABSTRACT FROM AUTHOR]

This paper introduces a novel hybrid optimization algorithm named bat-salp swarm algorithm (BASSA). BASSA integrates the local exploitation capability of bat algorithm (BA) and the global exploration capability of salp swarm algorithm (SSA). Firstly, by introducing the echolocation of BA, the follower updating strategy of SSA is improved. Secondly, the algorithm selects between BA and SSA based on specific conditions. Finally, individuals undergo random differential mutation to increase population diversity, thereby avoiding local optima. To verify the effectiveness of the algorithm, we carry out experiments BASSA on 23 benchmark functions with different dimensions and compare it with 7 optimization algorithms, including BA, SSA, and 7 enhanced versions of SSA. Simulation results indicate that BASSA outperforms standard BA, SSA, and other enhanced algorithms in terms of mean and standard deviation. This suggests a significant improvement in optimization performance, with higher solution accuracy and faster convergence speed. Additionally, through performance evaluation on three real engineering problems, the results indicate that BASSA possesses strong optimization capabilities. [ABSTRACT FROM AUTHOR]

In this paper, we adopt a fixed point approach towards a problem of finding fuzzy distance between two subsets of a fuzzy metric space. This is accomplished simultaneously by determining two different pairs of points which are obtained by finding an optimal approximate solution of a coupled fixed point equation for an appropriate coupled contractive operator. By its nature, the problem is a global optimization problem. The coupled contraction defined here has certain properties suitable for applications to the optimization problem considered in this paper. The main result is illustrated with an example. By applications of the main theorem, a new coupled fixed point result is derived in fuzzy metric space and a new coupled proximity point result is obtained in metric spaces. This work is a continuation of a newly emerged line of research on global optimization in the context of fuzzy metric spaces. [ABSTRACT FROM AUTHOR]

Intelligent transportation system integration is becoming increasingly dependent on robust and energy-efficient protocols for communication between vehicles and infrastructure (V2V and V2I) networks. This study offers an energy-efficient cluster-based routing protocol for interaction between V2V and V2I, with a focus on traffic forecasting. To improve communication efficiency in dynamic vehicle contexts, the protocol makes use of the capabilities of vehicle ad-hoc networks (VANETs). A novel approach is provided to optimize the routing process and meet the issues posed by variable traffic situations. The suggested method combines two advanced hybrid optimization algorithms: The Adaptive Binary Bird Swarm Optimization Algorithm (ABBSOA) and the Adaptive Golden Eagle Optimization Algorithm (AGEOA). The goal of hybridizing these algorithms is to capitalize on their complementary capabilities, resulting in a synergistic impact that improves the protocol's adaptability and efficiency. Through its feeding, guarding, and flying imitation behaviors, ABBSOA, inspired by the collective behaviors of birds, focuses on global optimization, outperforming local optima. The golden eagle hunting patterns served as inspiration for AGEOA, which introduces indirect and direct effects for improved convergence and solution quality. For efficient communication management, the protocol adopts a cluster-based design, with cluster heads (CHs) dynamically determined using the hybrid optimization algorithm. The ABBSOA-AGEOA-based strategy improves the CH selection process by optimizing job distribution among less burdened CHs in changing traffic circumstances. The simulation results indicate that the proposed cluster-based energy-efficient. The routing protocol performs better than current protocols with regard to traffic forecasting accuracy, packet delivery rate (PDR), packet loss rate (PLR), throughput, network lifetime (NLT), cluster lifetime, cluster build time, and energy consumption end-to-end latency. Comparative simulations demonstrate the suggested method's advantage over current VANET routing techniques like SFSR, ICMFO, and FA-OLSR, as shown by metrics using the proposed systems ABBSOA and AGEOA, which work well together to get through the complicated networks of vehicles. This makes the protocol a new way for V2V and V2I to communicate in smart transportation systems that will last for a long time. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms have become increasingly significant in solving complex optimization problems. To address the limitations of the original Aquila Optimizer (AO), such as insufficient local exploitation ability, low optimization precision, and slow convergence rate, an enhanced Aquila Optimizer (PSAO) for global optimization has been proposed. PSAO uses a better ergodic good point set to initialize the Aquila population and modifies the search method by employing the golden sine operator and the mechanism of self-learning and social learning based on particle swarm, followed by designing a nonlinear balance factor γ as the switching condition of the algorithm. The simulation experiments on benchmark functions and CEC2017 functions have verified that the PSAO has better global optimization ability and stronger robustness compared with other intelligent algorithms. Meanwhile, the contribution of each component that belongs to PSAO has been validated by ablation experiments for the CEC2022 test functions. To further illustrate the practical application potential of PSAO, PSAO is successfully applied to four typical engineering design problems, three of which reach the best fitness values. In addition, utilizing PSAO to solve the UAV trajectory planning problem by considering the objectives of trajectory length, altitude and corner of the flight process, the total flight cost is reduced by 60.22 %, 27.94 %, and 22.41 % compared with AO, PSO and Gold-SA, respectively, which proves its applicability and superiority in solving the real optimization problems. [ABSTRACT FROM AUTHOR]

This study introduces the Harmonic Mean Optimizer (HMO), a novel meta-heuristic algorithm designed to address complex global optimization problems. The primary objective of this research is to develop an optimization technique that effectively balances exploration and exploitation without requiring extensive parameter tuning, thereby simplifying the optimization process and enhancing its robustness across various problem domains. The HMO employs a unique dual-fitness index that leverages the harmonic mean to assess both the quality and diversity of candidate solutions dynamically. This approach ensures a balanced search process that avoids premature convergence and improves the ability to find global optima. The methodology involves extensive benchmarking of the HMO against a comprehensive set of test functions, including 23 traditional functions from the Congress on Evolutionary Computation (CEC) 2017 test suite. The performance of the HMO is compared with that of established meta-heuristic algorithms, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to validate its efficacy in terms of convergence speed and solution accuracy. Additionally, the HMO is applied to several real-world engineering problems to demonstrate its practical utility. The results show that the HMO consistently outperforms the benchmark algorithms, achieving faster convergence and higher accuracy in finding optimal solutions across a diverse range of test problems. In practical applications, the HMO effectively optimized complex engineering tasks, achieving significant improvements in both solution quality and computational efficiency. These findings highlight the potential of the HMO as a versatile and powerful tool for global optimization tasks. the HMO offers a significant advancement in optimization methodologies by providing a robust, parameter-free approach that effectively balances exploration and exploitation. This study underscores the HMO's applicability to a wide range of optimization challenges and sets the foundation for future research and development in enhancing and applying this innovative algorithm to more complex and diverse problem domains. [ABSTRACT FROM AUTHOR]

This article proposes a rectangular branch‐and‐bound algorithm for solving linear multiplication problems (LMP) globally. In order to obtain a reliable lower bound of the original problem, this article designs a novel linear relaxation programming problem (LRP) that has not been seen in the existing literature. Based on the basic framework of the rectangular branch and bound algorithm, this article proposes an algorithm that can obtain a global solution. According to the structure of linear relaxation programming, the article designs a region reduction technology to improve the efficiency of the algorithm. This article also provides convergence analysis to ensure the reliability of the algorithm. Finally, several numerical experiments are used to demonstrate the effectiveness and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

As one of the important components of a ship, the ship's integrated power system is an important safeguard for ships. In order to improve the service life of the ship's power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship's integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship's ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship. [ABSTRACT FROM AUTHOR]

Coatis optimization algorithm (COA) has recently emerged as an innovative meta-heuristic algorithm (MA) for global optimization, garnering considerable attention from scholars and researchers. In this paper, we introduce three techniques to enhance COA: (1) the cooperative mechanism, (2) the fitness-based division, and (3) the optional base vector strategy. Collectively, we refer to our improved method as cooperative COA (CCOA). In addition, we introduce the incorporation of the S-shaped Sigmoid transfer function and the V-shaped Tanh transfer function into CCOA, leading to the development of SCCOA and VCCOA. These adaptations effectively address the challenges posed by feature selection tasks and the 0/1 knapsack problem. To comprehensively evaluate the performance of the continuous version of CCOA, as well as the binary versions of SCCOA and VCCOA, we conducted two distinct categories of numerical experiments. Firstly, we compared CCOA with nine representative MAs, including the original COA, on CEC2020 benchmark functions and six engineering optimization problems. Secondly, SCCOA and VCCOA are compared with six famous binary MAs on 13 feature selection datasets and 18 standard 0/1 knapsack problems. Experimental and statistical results show the competitiveness of CCOA and its binary versions, and it is promising to extend CCOA to various real-world application scenarios. [ABSTRACT FROM AUTHOR]

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

We consider the optimization of pairwise objective functions, i.e., objective functions of the form H (x) = H (x 1 , … , x N) = ∑ 1 ≤ i < j ≤ N H ij (x i , x j) for x i in some continuous state spaces X i . Global optimization in this setting is generally confounded by the possible existence of spurious local minima and the impossibility of global search due to the curse of dimensionality. In this paper, we approach such problems via convex relaxation of the marginal polytope considered in graphical modeling, proceeding in a multiscale fashion which exploits the smoothness of the cost function. We show theoretically that, compared with existing methods, such an approach is advantageous even in simple settings for sensor network localization (SNL). We successfully apply our method to SNL problems, particularly difficult instances with high noise. We also validate performance on the optimization of the Lennard-Jones potential, which is plagued by the existence of many near-optimal configurations. We demonstrate that the proposed algorithm allows us to effectively explore these configurations. [ABSTRACT FROM AUTHOR]

The kinetic model of the synthesis of methyl-tert-butyl ethers by intermolecular dehydration of tert-butanol with methanol using HY zeolite with a hierarchical structure as a catalyst (HYmmm), as well as a catalyst system based on CuBr2 deposited on HYmmm, is considered. A kinetic model was constructed and a multistage scheme of chemical transformations was developed based on the experimental data. The kinetic model is based on the law of acting surfaces with allowance for adsorption and desorption processes on the catalyst surface. The inverse problem was solved in the form of a global optimization problem, which made it possible to determine the parameters of the kinetic model—the kinetic constants and activation energies. [ABSTRACT FROM AUTHOR]

This paper proposes an SOC estimation method for lithium battery, which combines the online parameter identification and an improved particle filter algorithm. Targeted at the particle degradation issue in particle filtering, grey wolf optimization is introduced to optimize particle distribution. Its strong global optimization ability ensures particle diversity, effectively suppresses particle degradation, and improves the filtering accuracy. The recursive least square method with forgetting factor is also introduced to update the model parameters in a real-time manner, which further improves the estimation accuracy of SOC alternately with the improved particle filter algorithm. Experimental results validate the proposed method, with an average estimation error less than ±0.15%. Compared with conventional extended Kalman filter and unscented Kalman filter algorithms, the proposed algorithm has higher estimation accuracy and stability for battery SOC estimation. [ABSTRACT FROM AUTHOR]

In this paper, we consider a polynomial problem with equilibrium constraints in which the constraint functions and the equilibrium constraints involve data uncertainties. Employing a robust optimization approach, we examine the uncertain equilibrium constrained polynomial optimization problem by establishing lower bound approximations and asymptotic convergences of bounded degree diagonally dominant sum-of-squares (DSOS), scaled diagonally dominant sum-of-squares (SDSOS) and sum-of-squares (SOS) polynomial relaxations for the robust equilibrium constrained polynomial optimization problem. We also provide numerical examples to illustrate how the optimal value of a robust equilibrium constrained problem can be calculated by solving associated relaxation problems. Furthermore, an application to electric vehicle charging scheduling problems under uncertain discharging supplies shows that for the lower relaxation degrees, the DSOS, SDSOS and SOS relaxations obtain reasonable charging costs and for the higher relaxation degrees, the SDSOS relaxation scheme has the best performance, making it desirable for practical applications. [ABSTRACT FROM AUTHOR]

Taking into account the whale optimization algorithm's tendency to get trapped in local optima easily and its slow convergence rate, this paper proposes a diverse strategies whale optimization algorithm (DSWOA) and uses it to optimize the parameters of GRU, thereby achieving better regression prediction effects. First, an innovative t-distribution perturbation is used to perturb the optimal whale to expand the optimization space of the optimal whale. Secondly, in the random search stage, we perform a Cauchy walk on the whale's position and then use reverse learning to enable the algorithm to effectively navigate away from the local optimum. Finally, we adopt a horizontal learning strategy for all whales and use two random whales to determine the current whale's position. Updated, the results suggest that DSWOA is highly effective in global optimization. By utilizing DSWOA, the parameters of GRU were fine-tuned. The experimental findings reveal that GRU produces promising outcomes on multiple datasets, making it a more effective tool for regression prediction tasks. [ABSTRACT FROM AUTHOR]

Titanium alanate Ti(AlH4)4 is a candidate material for high‐density hydrogen storage that thus far has been scarcely investigated. This study determines the low energy structures of Ti(AlH4)4 by adopting a global optimization algorithm combined with density functional theories, and systematically compares their stabilities, electronic structures, and bonding properties with Mg(AlH4)2 and Ca(AlH4)2. Compared to other compounds, Ti(AlH4)4 is less stable and the Ti−H bond is of covalent nature with strong polarities. The covalent Ti−H interaction can weaken the Al−H bonds. Because both the ionic and covalent interactions of the Al−H bonds are the weakest in Ti(AlH4)4, it is favorable for the desorption of hydrogen. [ABSTRACT FROM AUTHOR]

Cell tracking in microscopy images is fundamental to new biological and medical discoveries today. It facilitates the study of the properties of living cells over time. Due to the temporal nature of the cell tracking task, data association is the most difficult aspect of automated cell tracking. Due to the intricate nature of biological, imaging, and algorithmic factors that influence cell segmentation and tracking results, it is challenging to provide a simple and efficient method to determine the appropriate approach for a specific dataset. For example, mitosis, a crucial biological process, plays a key role in correcting trajectories. This research looked at all the challenges of the cell tracking task and the current solutions that have been proposed so far. In this paper, we carefully identified the sources of the tracking challenges and categorized them in a hierarchical diagram to explain the impact of challenges at different levels on the different cell tracking subtasks. Then, after identifying the solutions provided so far, we classified them into three levels: strategic, tactical, and technical. At the strategy level, tracking before detection and tracking by detection are two main approaches. The tactics can be based on cell distance, similarity, overlap, motions, probability, model evaluation, and deep-learning methods. The techniques identified in our analysis include contour evolution, nearest-neighbor linking, morphological-operator-based tracking, similarity-based label propagation, overlap-based label propagation, motion-prediction-based label propagation, graph-based multiple hypothesis tracking, probability-graph-based global optimization, probability-model-based global optimization, and recently developed deep-learning models. By merging cell tracking methods at different levels in one diagram, this classification will help to understand current solutions and provide new insights for cell tracking algorithms. Overall, in this study, we conducted a comprehensive investigation of the challenges of cell tracking and its corresponding solutions, offering a unique source of information. [ABSTRACT FROM AUTHOR]

Prestack amplitude versus offset (AVO) inversion is an essential tool to estimate elastic properties. Approximations of P-wave reflection coefficients based on the Zoeppritz equation are limited by the assumption of weak contrast and are inaccurate for far offset, and the existing nonlinear inversion method, like ant colony optimization (ACO), cannot converge to the global optimal solution within a limited time. Consequently, we propose a nonlinear AVO inversion method based on the exact Zoeppritz equation using hybrid quantum ant colony optimization (HQACO). The Zoeppritz equation is a forward modeling approach with high accuracy at far offset and is valid at the strong-contrast interface. HQACO, as a global optimization algorithm, not only has the potential of ACO to search global solutions but also exploits the power of quantum computing to speed up optimization procedures. The self-adaptive rotating strategy is proposed to improve the flexibility and efficiency of the conventional quantum rotating gate largely. Moreover, the quantum gate is introduced to enhance the global search ability. Numerical results show that the average evaluation number required for the global solution decreases largely and the solution converges to the global optimal solution closely using the proposed method. Synthetic applications verify that HQACO shows the most reliable exploring competence to find the global solution. The new inversion method is not only suitable for the strong-contrast interface with noise but also shows promising accuracy using the wide offset range. [ABSTRACT FROM AUTHOR]

In the pursuit of enhancing the efficiency of solar cells, accurate estimation of unspecified parameters in the solar photovoltaic (PV) cell model is imperative. An advanced salp swarm algorithm called the Super‐Evolutionary Nelder‐Mead Salp Swarm Algorithm (SENMSSA) is proposed to achieve this objective. The proposed SENMSSA addresses the limitations of SSA by incorporating a super‐evolutionary mechanism based on a Gaussian‐Cauchy mutation and a vertical and horizontal crossover mechanism. This mechanism enhances both the global optimization capabilities and the local search performance and convergence speed of the algorithm. It enables a secondary refinement of the global optimum, unlocking untapped potential in the solution space near the global optimum and elevating the algorithm's precision and exploitation capabilities to higher levels. The Nelder‐Mead simplex method is further introduced to enhance local search capabilities and convergence accuracy. The Nelder‐Mead simplex method is a versatile optimization algorithm that improves local search by iteratively adjusting a geometric shape (simplex) of points. It operates without needing derivatives, making it suitable for non‐smooth or complex objective functions. To assess the efficacy of SENMSSA, a comparative analysis is conducted against other available algorithms, namely SSA, IWOA, SCADE, LWOA, CBA, and RCBA, using the CEC2014 benchmark function set. Subsequently, the algorithm was employed to determine the unknown PV parameters under fixed conditions for three different diode models. Additionally, SENMSSA is utilized to estimate PV parameters for three commercially available PV models (ST40, SM55, KC200GT) under varying conditions. The experimental results indicate that the SENMSSA proposed in this study displays a remarkably competitive performance in all test cases compared to other algorithms. As such, we consider that the SENMSSA algorithm constitutes a reliable and efficient solution for the challenge of PV parameter estimation. [ABSTRACT FROM AUTHOR]

The Griewank test function for global unconstrained optimization has multiple local minima clustered around the global minimum at the origin. A new version of this test function is proposed that has a similar structure, but whose behavior at the local minima and maxima is non-smooth. This piecewise smooth version of the Griewank function represents an abs-factorable test case of objective functions for global non-smooth optimization as, for example, observed in the training of neural networks. [ABSTRACT FROM AUTHOR]

This work presents a new version of the salp swarm optimizer (SSA), called "mSSA," that uses complex mathematical expressions to dynamically manipulate the crucial control parameter ( c 1 ) during optimization. These expressions are carefully designed to modulate the shift in search strategy from exploratory to exploitative, improving the flexibility and speed of convergence of the algorithm. To evaluate the performance of the developed mSSA variants, a thorough examination is carried out on twenty-three benchmark test functions alongside their application to the complex task of software module classification. The process of classifying defective software modules involves developing a multilayer perceptron (MLP) classifier that is suited to the particular complexity and heterogeneity of the task. Selecting the best optimizer is made easier by systematically evaluating the different mSSA versions as MLP classifier trainers. Based on metrics like classification accuracy, convergence speed, and avoidance of local minima, a comparative analysis in opposition to six previously published metaheuristic optimizers shows that mSSA3, when combined with the developed MLP classifier, outperforms both other mSSA variations and state-of-the-art metaheuristic optimizers in terms of overall performance. The excellent classification accuracy, swift convergence, and ability to avoid local minima of mSSA3 highlight its superiority and establish it as a cutting-edge method in the application of metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

To endow the prey with intelligent movement behavior and improve the performance of Golden Jackal Optimization (GJO), a Q-learning Improved Gold Jackal Optimization (QIGJO) algorithm is proposed. This paper introduces five update mechanisms and proposes double-population Q-learning collaborative mechanism to select appropriate update mechanisms to improve GJO performance. Additionally, a new convergence factor is incorporated to enhance convergence capability of GJO. QIGJO demonstrates excellent performance across 23 benchmark functions, CEC2022, and three classical engineering design problems, indicating high convergence accuracy and significantly enhanced global exploration capability. The reliability optimization model of the hydraulic system for concrete pump trucks was established based on a Continuous-time Multi-dimensional T-S dynamic Fault Tree (CM-TSdFT), considering the two-dimensional factors of operating time and number of impacts. Utilizing QIGJO to optimize this model yielded excellent results, providing valuable methodological support for reliability optimization of hydraulic systems. [ABSTRACT FROM AUTHOR]

Driver distraction detection not only effectively prevents traffic accidents but also promotes the development of intelligent transportation systems. In recent years, thanks to the powerful feature learning capabilities of deep learning algorithms, driver distraction detection methods based on deep learning have increased significantly. However, for resource-constrained onboard devices, real-time lightweight models are crucial. Most existing methods tend to focus solely on lightweight model design, neglecting the loss in detection performance for small targets. To achieve a balance between detection accuracy and network lightweighting, this paper proposes a driver distraction detection method that combines enhancement and global saliency optimization. The method mainly consists of three modules: context fusion enhancement module (CFEM), channel optimization feedback module (COFM), and channel saliency distillation module (CSDM). In the CFEM module, one-dimensional convolution is used to capture information between distant pixels, and an injection mechanism is adopted to further integrate high-level semantic information with low-level detail information, enhancing feature fusion capabilities. The COFM module incorporates a feedback mechanism to consider the impact of inter-layer and intra-layer channel relationships on model compression performance, achieving joint pruning of global channels. The CSDM module guides the student network to learn the salient feature information from the teacher network, effectively balancing the model's real-time performance and accuracy. Experimental results show that this method outperforms the state-of-the-art methods in driver distraction detection tasks, demonstrating good performance and potential application prospects. [ABSTRACT FROM AUTHOR]

The Arithmetic Optimization Algorithm (AOA) is a novel metaheuristic inspired by mathematical arithmetic operators. Due to its simple structure and flexible parameter adjustment, the AOA has been applied to solve various engineering problems. However, the AOA still faces challenges such as poor exploitation ability and a tendency to fall into local optima, especially in complex, high-dimensional problems. In this paper, we propose a Hybrid Improved Arithmetic Optimization Algorithm (HIAOA) to address the issues of susceptibility to local optima in AOAs. First, grey wolf optimization is incorporated into the AOAs, where the group hunting behavior of GWO allows multiple individuals to perform local searches at the same time, enabling the solution to be more finely tuned and avoiding over-concentration in a particular region, which can improve the exploitation capability of the AOA. Second, at the end of each AOA run, the follower mechanism and the Cauchy mutation operation of the Sparrow Search Algorithm are selected with the same probability and perturbed to enhance the ability of the AOA to escape from the local optimum. The overall performance of the improved algorithm is assessed by selecting 23 benchmark functions and using the Wilcoxon rank-sum test. The results of the HIAOA are compared with other intelligent optimization algorithms. Furthermore, the HIAOA can also solve three engineering design problems successfully, demonstrating its competitiveness. According to the experimental results, the HIAOA has better test results than the comparator. [ABSTRACT FROM AUTHOR]