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Multi-population multi-strategy differential evolution algorithm with dynamic population size adjustment

Soft Computing 2024年第17-18期28卷 9949-9963页

作者： Xue, Caiwen Liu, Tong Deng, Libao Gu, Wei Zhang, Baowu College of Metrology Measurement and Instrument China Jiliang University Hangzhou310018 China School of Information and Electrical Engineering Harbin Institute of Technology Shandong Weihai150006 China

Differential Evolution (DE) is a global optimization process that uses population search to find the best solution. It offers characteristics such as fast convergence time, simple and understood algorithm, few parameters, and good stability. To improve its presentation, we propose a differential evolution algorithm based on subpopulation adaptive scale and multi-adjustment strategy (ASMSDE). The algorithm separates the population into three sub-populations based on fitness scores, and different operating tactics are used depending on their characteristics. The superior population uses Gaussian disturbance, while the inferior population uses Levy flights. The intermediate population is responsible for maintaining the population's overall variety. The sizes of the three sub-populations are adaptively changed in response to evolutionary results to account for changes in individual differences over time. With the number of iterations increases and the disparities between individuals reduce, adopt a single population model instead of multi-population model in the later stage of evolution. The ASMSDE algorithm's performance is evaluated by comparing it to other sophisticated algorithms that use benchmark function optimizations. Experimental results show that the ASMSDE algorithm outperforms the comparison algorithms in the majority of circumstances, demonstrating its effectiveness and capacity to manage local optimum situations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： optimization algorithms

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Extrapolated Proportional-Integral Projected Gradient Method for Conic optimization

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IEEE CONTROL SYSTEMS LETTERS 2022年 7卷 73-78页

作者： Yu, Yue Elango, Purnanand Acikmese, Behcet Topcu, Ufuk Univ Texas Austin Oden Inst Computat Sci & Engn Austin TX 78712 USA Univ Washington Dept Aeronaut & Astronaut Seattle WA 98195 USA

Conic optimization is the minimization of a convex quadratic function subject to conic constraints. We introduce a novel first-order method for conic optimization, named extrapolated proportional-integral projected gradient method (xPIPG), that automatically detects infeasibility. The iterates of xPIPG either asymptotically satisfy a set of primal-dual optimality conditions, or generate a proof of primal or dual infeasibility. We demonstrate the application of xPIPG using benchmark problems in model predictive control. xPIPG outperforms many state-of-the-art conic optimization solvers, especially when solving large-scale problems.

关键词： optimization Gradient methods Symmetric matrices Extrapolation Mathematical models Convergence Minimization optimization algorithms infeasibility detection

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Employing the hybrid RBF prediction algorithm to predict the failure energy properties of concrete

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第3期7卷 1499-1514页

作者： Chi, Yi Hunan Commun Polytech Changsha 410132 Hunan Peoples R China

Recently, fracture energy has received considerable attention owing to its importance in crack propagation in concrete structures. However, measuring fracture energy of concrete require experimental tests, causes cost and time. The alternative ways could be effective such as machine learning methods. So, this study concentrated on the developing prediction models to estimate the preliminary (G(f)) and total (G(F)) fracture energy of concrete. This research integrates the radial foundation function nervous network (RBF) with the equilibrium optimizer (EO) and the salp swarm optimization algorithm (SSOA), abbreviated EORB and SSRB, respectively, to get a more in-depth understanding of G(f) and G(F). The 264 research recordings were used to build and examine theories from previous research. optimization procedures were used to determine the best propagation value and concealed substrate neuron count. Estimates demonstrate that both the improved EORB and SSRB were able to operate very well throughout the whole estimating procedure of G(f), with coefficient of determination (R-2) values of 0.9578 and 0.9907 and 0.8851, and 0.9617 for the training and assessment data subsets, respectively. Regarding G(F), R-2 values of 0.9119 and 0.8763, and 0.8586 and 0.8097 for the training and assessment data subsets were obtained. The comparison with the literature depicted a significant improvement in the efficacy of the EORB simulation, with R-2 going up from 0.8281 to 0.9119 related to G(f) and from 0.9025 to 0.9907 related to G(F). Given the logic and ease of model processing, the EORB analysis seems to be highly reliable for computing G(f) and G(F), even though the SSRB technique has distinctive features for simulating.

关键词： Fracture energy of concrete Artificial intelligence Radial foundation function optimization algorithms

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On the Dual Gradient Descent Method for the Resource Allocation Problem in Multiagent Systems

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Journal of Applied and Industrial Mathematics 2024年第2期18卷 316-332页

作者： Rokhlin, D.B. Institute of Mathematics Mechanics and Computer Sciences and Regional Scientific and Educational Mathematical Center of the Southern Federal University Rostov-on-Don344090 Russia

Abstract: We consider a sequence of block-separable convex programming problems describing theresource allocation in multiagent systems. We construct several iterative algorithms for setting theresource prices. Under various assumptions about the utility functions and resource constraints,we obtain estimates for the average deviation (regret) of the objective function from the optimalvalue and the constraint residuals. Here the average is understood as the expectation forindependent identically distributed data and as the time average in the online optimizationproblem. The analysis of the problem is carried out by online optimization methods and dualitytheory. The algorithms considered use the information concerning the difference between the totaldemand and supply that is generated by agents’ reactions to prices and corresponds to theconstraint residuals. © Pleiades Publishing, Ltd. 2024.

关键词： optimization algorithms

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optimization of prestressed concrete bridge girder section using a modified harmony search algorithm

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STRUCTURES 2022年 46卷 625-636页

作者： Jahjouh, Mahmoud Erhan, Semih Univ Bahrain Civil Engn Dept Sakhir Bahrain

Precast I girders provide rapid construction and structural simplicity, making them an increasingly popular choice for bridge construction. The current standard sections adopted by the American Association of State Highway and Transportation Officials (AASHTO) are not SI friendly and may be improved utilizing increasingly efficient metaheuristic optimization algorithms. Thus, this research study aims to obtain the optimum shape of Precast I (PCI) bridge girders commonly used in bridge construction due to various advantages. The optimization problem is solved using a modified metaheuristic optimization algorithm considering a number of constraints that are stipulated by the AASHTO LRFD code requirements. To show the capabilities of the utilized modified optimization algorithm, a comparison is performed with its baseline counterpart. The structural design optimization of bridge superstructures is performed with the objective of minimizing the total cost of the resulting superstructure. Consequently, the most economical shape of the PCI girders is obtained. The results of this analysis are then compared AASHTO and the California Department of Transportation (Caltrans) PCI girders. The new I girder sections exhibits 13-17% and 5-16% better structural efficiency factor (rho) as well as 14-19% and 4-21% structural efficiency ratio (alpha) than Caltrans and AASHTO girders, respectively. Thus, new sections for PCI girders are developed having various heights to facilitate more economical design of bridges having various geometric properties such as span length, girder spacing and slab thickness.

关键词： Bridge engineering Bridge Girder optimization Harmony search optimization algorithms

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Nonconvex Distributed optimization via Lasalle and Singular Perturbations

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IEEE CONTROL SYSTEMS LETTERS 2022年 7卷 301-306页

作者： Carnevale, Guido Notarstefano, Giuseppe Alma Mater Studiorum Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy

In this letter we address nonconvex distributed consensus optimization, a popular framework for distributed big-data analytics and learning. We consider the Gradient Tracking algorithm and, by resorting to an elegant system theoretical analysis, we show that agent estimates asymptotically reach consensus to a stationary point. We take advantage of suitable coordinates to write the Gradient Tracking as the interconnection of a fast dynamics and a slow one. To use a singular perturbation analysis, we separately study two auxiliary subsystems called boundary layer and reduced systems, respectively. We provide a Lyapunov function for the boundary layer system and use Lasalle-based arguments to show that trajectories of the reduced system converge to the set of stationary points. Finally, a customized version of a Lasalle's Invariance Principle for singularly perturbed systems is proved to show the convergence properties of the Gradient Tracking.

关键词： Radio frequency Convergence Linear programming Perturbation methods Heuristic algorithms Lyapunov methods Control theory Distributed control control of networks optimization optimization algorithms

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A General Framework for Approximate and Delayed Gradient Descent for Decomposable Cost Functions

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IEEE CONTROL SYSTEMS LETTERS 2024年 8卷 1229-1234页

作者： Zheng, Xinran Javidi, Tara Touri, Behrouz Univ Calif San Diego Elect & Comp Engn Dept La Jolla CA 92037 USA

We propose and analyze a generalized framework for distributed, delayed, and approximate stochastic gradient descent. Our framework considers n local agents who utilize their local data and computation to collectively assist a central server tasked with optimizing a global cost function composed of local cost functions accessible to the local agents. This framework is very general, subsuming a great variety of algorithms in federated learning and distributed optimization. In particular, this framework allows each local agent to approximate and share a stochastic (possibly biased) and delayed estimate of its local function gradient. Focusing on strongly convex functions with sufficient degree of smoothness, we characterize the mean square error in terms of the varying step-size, the approximation error (bias), and the delay in computing the gradients. This characterization, together with a careful design of step size process, establishes an optimal convergence rate that aligns with centralized stochastic gradient descent (SGD).

关键词： Convergence Servers optimization Delays Cost function Task analysis Smoothing methods optimization algorithms delay systems machine learning

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A variable sampling interval EWMA control chart for urinary tract infections data

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2024年第12期94卷 2727-2753页

作者： Bai, Yajie Chiang, Jyun-You Wang, Shuai Mou, Zhengcheng Southwestern Univ Finance & Econ Sch Stat Chengdu Peoples R China Hainan Univ Sch Econ 58 Renmin Ave Haikou 570228 Hainan Peoples R China

Control chart is an important method in Statistical Process Control, which is used to study how a process changes over time. The exponential distribution is one of the commonly used models for fitting data, especially in areas such as component life, aviation accidents, or health-care processes. In order to further improve the sensitivity of the control chart, we propose an exponentially weighted moving average control chart with a variable sampling interval scheme, denoted as VSIEWMA-T control chart, for monitoring exponentially distributed quality characteristics (e.g. Urinary Tract Infections data). In this paper, the average time to signal property of the proposed control chart is investigated using the Markov chain method. In addition, parameter optimization algorithms for known or unknown shift levels are provided. Subsequently, numerical analysis shows that the proposed control chart outperforms other competitive control charts. Finally, an example of urinary tract infections data is provided for illustration.

关键词： Exponentially weighted moving average chart variable sampling interval exponential distribution Markov chain method optimization algorithms

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A metaheuristic approach to compare different combined economic emission dispatch methods involving load shifting policy

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年 1-23页

作者： Misra, Srikant Panigrahi, P. K. Ghosh, Saradindu Dey, Bishwajit GIET Univ Dept EEE Gunupur India NIT Dept EE Durgapur India Univ Johannesburg Dept Elect & Elect Engn Technol Johannesburg South Africa

Distributed generators (DGs), which can be traditional fossil fuel generators or renewable energy sources (RES), must be appropriately planned in order to reduce a power network's overall generating cost. Renewable energy sources (RES) should be prioritized because they provide a clean and sustainable energy supply and are abundant in nature. Demand side management (DSM) optimizes the scheduling of flexible loads to reduce peak demand and improve the load factor, while keeping daily demand unchanged. The test system in this research employs a dependable and effective hybrid optimization tool to plan the DGs of a dynamic system in a way that matches low active power production costs with low pollutant emissions. The fitness functions used in the test system were non-linear due to the presence of the valve point effect (VPE). The costs and emissions were evaluated for various fitness functions which included involvement of wind, DSM, and different types of combined economic emission dispatch (CEED) methods. The test system's peak demand was cut by 12% and the load factor was raised from 0.7528 to 0.85 when DSM technique was used. The generation cost has been reduced from $1,014,996 to $1,012,182 using CSAJAYA algorithm which was further reduced to $1,007,441 after incorporating DSM. Likewise, the CEEDppf was also observed to be reduced to $1,231,435 and $1,216,885 with and without DSM compared to $1,232,001 from reported literature. Numerical results show that both the cost and emission were reduced significantly using the proposed CSAJAYA compared to a long-sighted list of algorithms published in literature.

关键词： Valve point effect Dynamic economic emission dispatch Sustainable development goals Demand side management optimization algorithms

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Neuro-fuzzy system development to estimate the compressive strength of improved high-performance concrete

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 395-409页

作者： Niu, Zhiqiang Yuan, Yan Sun, Jianglan Zhengzhou Univ Sci & Technol Fac Civil & Architectural Engn Zhengzhou 450064 Peoples R China

High-performance concrete (HPC) outperforms regular concrete due to incorporating additional components that go beyond the typical ingredients used in standard concrete. Various artificial analytical methods were employed to assess the compressive strength (CS) of high-performance concrete containing fly ash (FA) and blast furnace slag (BFS). The primary objective of this study was to present a practical approach for a comprehensive evaluation of machine learning algorithms in predicting the CS of HPC. The study focuses on utilizing the adaptive neuro-fuzzy inference system (ANFIS) to develop models for predicting HPC characteristics. To enhance the performance of ANFIS methods, the study incorporates the arithmetic optimization algorithm (AOA) and equilibrium optimizer (EO) (abbreviated as ANAO and ANEO, respectively). Notably, this research introduces novelty through the application of the AOA and EO, the evaluation of HPC with additional components, the comparison with prior literature, and the utilization of a large dataset with multiple input variables. The results indicate that the combined ANAO and ANEO systems demonstrated strong estimation capabilities, with R-2 values of 0.9941 and 0.9975 for the training and testing components of ANAO, and 0.9878 and 0.9929 for ANEO, respectively. The results comparison of this study presented the comprehensiveness and reliability of the created ANFIS model optimized with AOA for predicting the HPC's CS improved with FA and BFS, which could be applicable for practical usages.

关键词： High-performance concrete Compressive strength Prediction Adaptive neuro-fuzzy inference system optimization algorithms

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