Efficient Optimization Algorithm Research Articles

Solving the vehicle routing problem with time windows using modified football game algorithm

The vehicle routing problem with time windows (VRPTW) is a famous problem in logistics and transportation that has many applications in real life. The objective of the problem is to find the minimum distance covered by a set of vehicles that set to start together from the warehouse and visit some prefixed customers within certain timeframes. In this paper, an efficient optimization algorithm, called modified football game algorithm (MFGA), is proposed to solve the VRPTW as an NP-hard problem. MFGA imitates the behavior of football players during a game to find the best positions to score a goal under the supervision of a team coach. In the presented algorithm, a new method is presented to create an initial solution for the VRPTW problem. In addition, a more efficient method has been designed to create a new location for the player, which makes the algorithm more efficient. The performance of the proposed MFGA is validated against Solomon’s VRPTW benchmark instances. Experimental results confirm that MFGA produces competitive results compared to several state-of-the-art algorithms in terms of various solution quality indicators. The proposed algorithm obtains 17 best-known solutions (BKSs) and improves 12 BKSs in the literature.

An efficient many objective optimization algorithm with few parameters

During the past two decades, numerous many-objective optimization evolutionary algorithms (MaOEAs) have been proposed to tackle the challenges traditional multi-objective evolutionary algorithms face, that is to deal with abundant non-dominated solutions and low selection pressure. Specifically, a series of sophisticated selection strategies have been adopted, some of which need additional pre-defined parameters or a significant amount of extra computation time, which retards their applications in real life. In this paper, we propose an efficient indicator-based MaOEA with few parameters, SDE+-MOEA, that uses an adaptive combination of commonly used selection methods to improve search efficiency based on SDE+. SDE+ addresses situations where SDE cannot distinguish individuals with the same SDE values. The adaptive selection method dynamically selects between one-time and iterative selection methods at different stages of evolution to improve the search efficiency. Furthermore, apart from the four parameters for generating offspring, our proposed SDE+-MOEA does not introduce additional parameters. We conduct experimental studies to compare SDE+-MOEA with 11 state-of-the-art algorithms, including 2REA, ISDE+, θ-DEA, LMPFE, CVEA3, SRA, MOEA/DD, IBEA, Two_Arch2, NSGA-III, and SPEA2SDE using four representative performance indicators (HV, SP, PD and GD) on MaF benchmark with 5, 8, 10, and 15 objectives. Experimental studies demonstrate that, compared to the algorithms, SDE+-MOEA achieves better HV and competitive convergence and uniformity performance, while requiring few parameters. It means that SDE+-MOEA can find a solution set with better convergence and uniformity to help decision-makers understand the solved problems. Furthermore, SDE+-MOEA loses little spreadability since a better convergence often leads to a worse spread.

An energy-saving hydrogen liquefaction process with efficient utilization of liquefied natural gas cold energy

Storage and transportation of hydrogen in liquid form offers considerable advantages, but the high energy consumption of hydrogen liquefaction processes needs to be addressed. In this study, a novel hydrogen liquefaction process integrated with a dual-pressure organic Rankine cycle (DORC) is proposed. It offers significant energy-saving by efficiently utilizing the cold energy of liquefied natural gas (LNG). The key parameters in the proposed process are investigated, and the results are combined with an improved genetic algorithm for efficient optimization of the process. The optimal exergy efficiency of the proposed process is 48.7%, and the minimal energy consumption is 6.29 kWh/kgLH2. Thanks to the power output (381.3 kW) of the DORC and the energy saved by the cryogenic compression of nitrogen (586.6 kW), energy consumption of the proposed process is reduced by approximately 3.1% compared to that of the process without a DORC. Moreover, an improved helium Brayton refrigeration cycle for hydrogen cryo-cooling is simpler, but less the refrigerant usage and energy consumption compared to the previously proposed cycles. The proposed process demonstrates excellent performance in terms of energy saving and efficiency improvement, providing a low-cost scheme to produce liquid hydrogen.

An adaptive modeling for bifacial solar module levelized cost and performance analysis for mining application

AbstractPower density and efficiency typically dominate design approaches for power electronics. However, cost optimality is in no way guaranteed by these strategies. A design framework that minimizes the (i) levelized cost of electricity (LCOE), (ii) collection of light, and (iii) irradiance of the generation system is proposed as a solution to this flaw. From an improvement of the swarm behavior optimization model to get a minimum LCOE of solar panel, we design to optimize height, tilt angle, azimuth angle, and some parameters to solve the objective function and LCOE improvement problem to obtain the optimal design problem. In adaptive salp swarm optimization (ASSO), this change's proposed model producer swarm behavior is regarded as an adaptive process that keeps the algorithm from prematurely converging during exploration. The proposed algorithm's performance was confirmed using benchmark test functions, and the results were compared with those of the salp swarm optimization (SSO) and other efficient optimization algorithms. LCOE condition as far as “land‐related cost” and “module‐related cost” demonstrates that the optimal design of bifacial farms is determined by the interaction of these parameters. This proposed model can be used to evaluate visibility on building surfaces that are suitable for mining applications like crushing. Experimentation results show Minimum LCOE AS 0.05 (€/Kw)minimum irradiance and collection light as 336.23(w/m2) and 83.02%n proposed framework model. The swarm optimization method is contrasted with the optimal parameters derived from a conventional solver.

Optimal process planning for hybrid additive–subtractive manufacturing using recursive volume decomposition with decision criteria

This paper explores the concept of hybrid manufacturing (HM), which combines two or more manufacturing processes to improve efficiency and productivity. HM can be categorized based on the combination of additive, subtractive, transformative, and assistive processes, and this study specifically focuses on widely-adopted hybrid additive–subtractive manufacturing. The potential of HM to improve productivity and manufacturability is investigated from a process planning perspective. To enable fully automated and optimized process planning, a new set of decision criteria for handling a newly devised recursive volume decomposition of 3D CAD models is introduced along with a cost and time model for optimization. An optimization scheme is developed based on these requirements, and an efficient optimization algorithm using a genetic algorithm is proposed. The experimental results demonstrate that the integration of additive and subtractive processes in HM can overcome the limitations of conventional manufacturing. The optimal solutions reduce 69% and 63% of manufacturing cost and time on average for four test cases, and statistical significance was observed for the decision criteria most of the time.

Stochastic Localization Methods for Convex Discrete Optimization via Simulation

Solving Convex Discrete Optimization via Simulation via Stochastic Localization Algorithms Many decision-making problems in operations research and management science require the optimization of large-scale complex stochastic systems. For a number of applications, the objective function exhibits convexity in the discrete decision variables or the problem can be transformed into a convex one. In “Stochastic Localization Methods for Convex Discrete Optimization via Simulation,” Zhang, Zheng, and Lavaei propose provably efficient simulation-optimization algorithms for general large-scale convex discrete optimization via simulation problems. By utilizing the convex structure and the idea of localization and cutting-plane methods, the developed stochastic localization algorithms demonstrate a polynomial dependence on the dimension and scale of the decision space. In addition, the simulation cost is upper bounded by a value that is independent of the objective function. The stochastic localization methods also exhibit a superior numerical performance compared with existing algorithms.

An efficient hybrid optimization algorithm for detecting heart disease using adaptive stacked residual convolutional neural networks

Heart disease remains a leading global cause of mortality. Cardiovascular disease is the most common vascular disorder contributing to heart-related issues. Critical factors impacting cardiovascular health include blood pressure, glucose control, and cholesterol management. The escalating mortality rates associated with heart disease underscore the urgency for precise and secure diagnoses to facilitate timely intervention and prevention. Incorrect diagnoses of cardiovascular disease can have devastating consequences, highlighting the critical need for accurate assessments. In this study, we introduce an intelligent system for heart disease prediction based on the integration of the ACLS-RCNN and ICSOA techniques. The workflow begins with the collection and preprocessing of patient information. Data preprocessing focuses on addressing missing values, employing IQR-RS scaling, and handling imbalanced data using the ROS technique. Next, features are extracted from the pre-processed data utilizing kernel-based linear discriminate analysis. To reduce the dimensionality of the dataset, hybrid optimization (CI-AO + GI-SMO) is employed to select informative attributes. Disease prognoses are then generated through the application of the ACLS-RCNN model, with further enhancement achieved by the Improved Cuttlefish-Swarm Optimisation Algorithm (ICSOA). Through extensive Python-based simulations, we evaluate the recommended system's performance in terms of accuracy, precision, recall, and f1-score, comparing it against established methodologies. Our software demonstrates remarkable accuracy in clinical data prediction and diagnosis, offering a promising tool for enhancing heart disease prognosis and early detection.

An Efficient Self‐supporting Infill Structure for Computational Fabrication

AbstractEfficiently optimizing the internal structure of 3D printing models is a critical focus in the field of industrial manufacturing, particularly when designing self‐supporting structures that offer high stiffness and lightweight characteristics. To tackle this challenge, this research introduces a novel approach featuring a self‐supporting polyhedral structure and an efficient optimization algorithm. Specifically, the internal space of the model is filled with a combination of self‐supporting octahedrons and tetrahedrons, strategically arranged to maximize structural integrity. Our algorithm optimizes the wall thickness of the polyhedron elements to satisfy specific stiffness requirements, while ensuring efficient alignment of the filled structures in finite element calculations. Our approach results in a considerable decrease in optimization time. The optimization process is stable, converges rapidly, and consistently delivers effective results. Through a series of experiments, we have demonstrated the effectiveness and efficiency of our method in achieving the desired design objectives.

Discriminative Geometric-Structure-Based Deep Hashing for Large-Scale Image Retrieval.

Deep hashing reaps the benefits of deep learning and hashing technology, and has become the mainstream of large-scale image retrieval. It generally encodes image into hash code with feature similarity preserving, that is, geometric-structure preservation, and achieves promising retrieval results. In this article, we find that existing geometric-structure preservation manner inadequately ensures feature discrimination, while improving feature discrimination of hash code essentially determines hash learning retrieval performance. This fact principally spurs us to propose a discriminative geometric-structure-based deep hashing method (DGDH), which investigates three novel loss terms based on class centers to induce the so-called discriminative geometrical structure. In detail, the margin-aware center loss assembles samples in the same class to the corresponding class centers for intraclass compactness, then a linear classifier based on class center serves to boost interclass separability, and the radius loss further puts different class centers on a hypersphere to tentatively reduce quantization errors. An efficient alternate optimization algorithm with guaranteed desirable convergence is proposed to optimize DGDH. We theoretically analyze the robustness and generalization of the proposed method. The experiments on five popular benchmark datasets demonstrate superior image retrieval performance of the proposed DGDH over several state of the arts.

ECCA: Efficient Correntropy-Based Clustering Algorithm With Orthogonal Concept Factorization.

One of the hottest topics in unsupervised learning is how to efficiently and effectively cluster large amounts of unlabeled data. To address this issue, we propose an orthogonal conceptual factorization (OCF) model to increase clustering effectiveness by restricting the degree of freedom of matrix factorization. In addition, for the OCF model, a fast optimization algorithm containing only a few low-dimensional matrix operations is given to improve clustering efficiency, as opposed to the traditional CF optimization algorithm, which involves dense matrix multiplications. To further improve the clustering efficiency while suppressing the influence of the noises and outliers distributed in real-world data, an efficient correntropy-based clustering algorithm (ECCA) is proposed in this article. Compared with OCF, an anchor graph is constructed and then OCF is performed on the anchor graph instead of directly performing OCF on the original data, which can not only further improve the clustering efficiency but also inherit the advantages of the high performance of spectral clustering. In particular, the introduction of the anchor graph makes ECCA less sensitive to changes in data dimensions and still maintains high efficiency at higher data dimensions. Meanwhile, for various complex noises and outliers in real-world data, correntropy is introduced into ECCA to measure the similarity between the matrix before and after decomposition, which can greatly improve the clustering effectiveness and robustness. Subsequently, a novel and efficient half-quadratic optimization algorithm was proposed to quickly optimize the ECCA model. Finally, extensive experiments on different real-world datasets and noisy datasets show that ECCA can archive promising effectiveness and robustness while achieving tens to thousands of times the efficiency compared with other state-of-the-art baselines.

A novel efficient multi-objective optimization algorithm for expensive building simulation models

A novel efficient multi-objective optimization algorithm for expensive building simulation models

Efficient buckling and post-buckling analysis of porous FG sandwich beams by new RPIM-HOCM mesh-free approach

The main purpose of this paper is to analyze buckling and post-buckling behavior of porous functionally graded (FG) beams and sandwich beams using a new efficient optimal mesh-free high order algorithm. The proposed solver combines mesh-free approach based on radial point interpolation method with high order continuation method (RPIM-HOCM). It is assumed that the FG beam and FG layers of sandwich beam are made from a mixture of ceramic and metal such that bottom surface of the plate is metal-rich, whereas top surface is ceramic-rich. So, constitutive equation as well as material properties are considered to change smoothly and gradually in the thickness direction via a mathematical relationship distribution. Non-linear strong form of the beam buckling problem which is based on the first-order shear deformation theory (FSDT) is derived using the virtual displacements theorem. The proposed coupling (RPIM-HOCM) is presented and validated firstly by comparing obtained numerical results with those computed using a reference algorithm. Then, RPIM-HOCM solver is used to investigate the effect of power-law index, porosity distribution, span-to-height ratio, boundary conditions and skin-core–skin thickness ratio on critical buckling load and post-buckling behavior.

Numerical algorithms and simulations of boundary dynamic control for optimal mixing in unsteady Stokes flows

This work develops an efficient and accurate optimization algorithm to study the optimal mixing problem driven by boundary control of unsteady Stokes flows, based on the theoretical foundation laid by Hu and Wu in a series of work. The scalar being mixed is purely advected by the flow and the control is a force exerted tangentially on the domain boundary through the Navier slip conditions. The control design has potential applications in many industrial processes such as rotating wall driven mixing, mircomixers with acoustic waves, and artificial cilia mixing.The numerical algorithms have high complexity, high accuracy demand, and high computing expense, due to the multiscale nature of the mixing problem and the optimization requirements. A crucial problem is the computation of the Gâteaux derivative of the cost functional. To this end, a hybrid approach based on variational formula and finite difference is built with high accuracy and efficiency to treat various types of control input functions. We have experimented with various optimization algorithms including the steepest descent algorithm, the conjugate gradient method and two line search options (backtracking and exact line search). We are able to identify and implement the best combinations.The numerical simulations show that the mixing efficacy is limited when only one single type of control is applied, but can be enhanced when more diverse control types and more time segmentation are utilized. The mix-norm in the optimal mixings decays exponentially. The numerical study in this work demonstrates that boundary control alone could be an effective strategy for mixing in incompressible flows.

Simultaneous location and vehicle fleet sizing of relief goods distribution centers and vehicle routing for post-disaster logistics

The occurrence of a sudden-onset disaster such as a major earthquake brings about a great deal of uncertainty in, for example, the severity of damage to the road network and other infrastructure across the disaster region, the traffic flow conditions in the post-disaster network, and the demand for relief goods at relief centers. Despite these uncertainties, crucial decisions must still be made both before and rapidly after the occurrence of the disaster such that the logistical operation of relief goods distribution can have the greatest possible chance of success. In this research, we propose a two-stage stochastic programming model in which the first stage optimizes the location of relief goods distribution centers as well as the number of vehicles allocated to the logistical operation, while the second stage determines the best vehicle and inventory routing decisions for the logistics operation in the critical first time window after the random factors associated with the disaster are revealed. We collaborate with the National Science and Technology Center for Disaster Reduction (NCDR) in Taiwan to effectively model post-disaster road network conditions and the speed of vehicle traffic as a function of earthquake parameters (e.g., magnitude, time of strike). An efficient simulation optimization algorithm with feedback is proposed to tackle the two-stage stochastic programming model. We carry out an empirical study that consists of a set of experiments based on real data from NCDR to investigate the impact of critical factors in the proposed model under a variety of different conditions. Managerial insights are derived for decision makers pertinent to both the preparation and response phases of a sudden-onset disaster.

Efficient Bayesian automatic calibration of a functional-structural wheat model using an adaptive design and a metamodelling approach.

Functional-structural plant models are increasingly being used by plant scientists to address a wide variety of questions. However, the calibration of these complex models is often challenging, mainly because of their high computational cost, and, as a result, error propagation is usually ignored. Here we applied an automatic method to the calibration of WALTer: a functional-structural wheat model that simulates the plasticity of tillering in response to competition for light. We used a Bayesian calibration method to jointly estimate the values of five parameters and quantify their uncertainty by fitting the model outputs to tillering dynamics data. We made recourse to Gaussian process metamodels in order to alleviate the computational cost of WALTer. These metamodels are built from an adaptive design that consists of successive runs of WALTer chosen by an efficient global optimization algorithm specifically adapted to this particular calibration task. The method presented here performed well on both synthetic and experimental data. It is an efficient approach for the calibration of WALTer and should be of interest for the calibration of other functional-structural plant models.

Improved shape-from-focus reconstruction for high dynamic range freedom surface

The 3D reconstruction of freedom surface has been a major challenge for computer vision as well as metrology. Complex surface structures such as large undulations and deep grooves, along with surface reflection properties like high dynamic range (HDR) pose many difficulties for 3D reconstruction. Shape from focus (SFF) is widely studied for its low cost, ease of deployment, and effectively avoiding occlusion. However, its inherent disadvantages, such as inaccuracy and sensitivity to noise, cannot be ignored. This article demonstrates an improved depth map optimization method to solve these problems. Firstly, a more comprehensive and reliable depth recovery evaluation method is proposed. Then an efficient optimization algorithm is applied to the filtered depth maps. Comparable experiments confirm our method's effective suppression of noise and the reliable compensation of missing signals when measuring freedom surfaces.

Learning correlation information for multi-label feature selection

In many real-world multi-label applications, the content of multi-label data is usually characterized by high dimensional features, which contains complex correlation information, i.e., label correlations and redundant features. To alleviate the problem, we present a novel scheme, called learning correlation information for multi-label feature selection (LCIFS) method, by jointly digging up label correlations and controlling feature redundancy. To be specific, the regression model via manifold framework is presented to fit the relationship between feature space and label distribution, during which adaptive spectral graph is leveraged to learn more precise structural correlations of labels simultaneously. Besides, we utilize the relevance of features to constrain the redundancy of the generated feature subset, and a general ℓ2,p-norm regularized model is employed to fulfill more robust feature selection. The proposed method is transformed into an explicit optimization function, which is conquered by an efficient iterative optimization algorithm. Finally, we conduct comprehensive experiments on twelve realistic multi-label datasets, including text domain, image domain, and audio domain. The statistic results demonstrate the effectiveness and superiority of the proposed method among nine competition methods.

Physics-supervised deep learning–based optimization (PSDLO) with accuracy and efficiency

Identifying efficient and accurate optimization algorithms is a long-desired goal for the scientific community. At present, a combination of evolutionary and deep-learning methods is widely used for optimization. In this paper, we demonstrate three cases involving different physics and conclude that no matter how accurate a deep-learning model is for a single, specific problem, a simple combination of evolutionary and deep-learning methods cannot achieve the desired optimization because of the intrinsic nature of the evolutionary method. We begin by using a physics-supervised deep-learning optimization algorithm (PSDLO) to supervise the results from the deep-learning model. We then intervene in the evolutionary process to eventually achieve simultaneous accuracy and efficiency. PSDLO is successfully demonstrated using both sufficient and insufficient datasets. PSDLO offers a perspective for solving optimization problems and can tackle complex science and engineering problems having many features. This approach to optimization algorithms holds tremendous potential for application in real-world engineering domains.

Multi-objective Bayesian alloy design using multi-task Gaussian processes

In design applications, correlations among material properties (such as the tendency for stronger materials to be less ductile) are often neglected. This approach is echoed in multi-objective optimization techniques which treat each performance characteristic as an independent objective, aiming to optimize scalar functions and find optimal Pareto fronts. However, this overlooks the statistical relationships between performance characteristics inherent in a material system. To address this, we propose the use of Bayesian optimization, a highly efficient black-box optimization algorithm known for constructing Gaussian processes (GPs) – uncorrelated surrogates - to model objective functions. Rather than evaluating multiple GPs for each objective function separately, we argue for a shift towards jointly modeling these objective functions, considering their statistical correlations. This integrated approach utilizes naturally occurring relationships among material properties, providing additional information to enhance the performance of the design framework. This requires the replacement of multiple independent GPs with a single multi-task GP, employing a correlation matrix to construct a multi-task kernel function, wherein each task corresponds to a single objective function. We anticipate this refined methodology will better leverage material correlations, improving design optimization results.

Multi-view semi-supervised learning with adaptive graph fusion

Multi-view Semi-supervised Learning (MSL) is effective in using limited labels and considerable label-free data to improve learning performance. It has been successfully applied to a lot of real scenarios. In this study, we propose a model, termed MSL with Adaptive Graph Fusion (MSLAGF), which provides a novel solution for MSL. Unlike most existing methods propagating label information through the linear combination of pre-built fixed view-based similarity graphs, MSLAGF merges view-based graph construction, graph fusion, and label propagation. It adaptively learns view-specific graphs and automatically assigns weight coefficients to them. A multi-view fusion optimal graph is cleverly learned depending not only on the raw feature space but also on the dynamically predicted label space. Moreover, we present an efficient optimization algorithm to solve the formulated model. The view-specific graphs, the weight coefficients, the optimal graph, and the predicted labels are mutually negotiated and optimized in the optimization procedure. Extensive experimental results on six benchmark datasets validate the superiority.