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Cellular gradient algorithm for solving complex mechanical optimization design problems

INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2024年 282卷

作者： Wang, Rugui Li, Xinpeng Huang, Haibo Fan, Zhipeng Huang, Fuqiang Zhao, Ningjuan Guangxi Univ Sch Mech Engn Nanning 530004 Peoples R China

In mechanical optimization design problems, there are often some non-continuous or non-differentiable objective functions. For these non-continuous and non-differentiable optimization objectives, it is often difficult for existing optimal design algorithms to find the desired optimal solutions. In this paper, we incorporate the idea of gradient descent into cellular automata and propose a Cellular Gradient (CG) method. First, we have given the basic rules and algorithmic framework of CG and designed three kinds of growth and extinction rules respectively. Then, the three evolutionary rules for cellular within a single cycle are analyzed separately for form and ordering. The best expressions for the cellular jealous neighbor rule and the solitary regeneration rule are given, and the most appropriate order in which the rules are run is selected. Finally, the solution results of the cellular gradient algorithm and other classical optimization design algorithms are compared with a multi-objective multiparameter mechanical optimization design problem as an example. The computational results show that the cellular gradient algorithm has an advantage over other algorithms in solving global and dynamic mechanical optimal design problems. The novelty of CG is to provide a new way of thinking for solving optimization problems with global discontinuities.

关键词： Mechanical optimization optimization algorithm Discrete integrable problem Cellular automaton Gradient descent

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Efficient Surrogate Model Assisted Estimation of Distribution algorithm for Expensive optimization

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IEEE ACCESS 2024年 12卷 78248-78260页

作者： Shang, Jin Li, Guiying Hao, Hao Zhang, Yufang Wuxi Inst Technol Inst Control Technol Wuxi 214121 Peoples R China Heilongjiang Univ Sch Mech & Elect Engn Harbin 150080 Heilongjiang Peoples R China Shanghai Jiao Tong Univ Inst Nat Sci Shanghai 200240 Peoples R China

In recent years, several surrogate assisted evolutionary algorithms (SAEAs) have been proposed to solve expensive optimization problems. These problems lack explicit expressions and are characterized by high invocation costs. SAEAs leverage surrogate models to accelerate convergence towards the optimal region and reduce the number of function evaluations. While Gaussian Processes (GPs) are widely used due to their robustness and capability of providing uncertainty estimates, their applicability becomes limited in scenarios involving a large number of samples or high-dimensional spaces. This is due to their cubic time complexity in relation to the number of samples, which results in prohibitive computational demands for large-scale problems. To address the challenge, this work presents an efficient surrogate model-assisted estimation of the distribution algorithm (ESAEDA). This method employs a random forest as a surrogate model and combines it with a GP-hedge acquisition strategy to ensure the efficiency and accuracy of model-assisted selection. An improved EDA model called the variable-width histogram model with some unevaluated solutions is used to generate new solutions. To demonstrate the benefits of the proposed method, we compared ESAEDA with several state-of-the-art surrogate-assisted evaluation algorithms and the Bayesian optimization method. Experimental results demonstrate the superiority of the proposed algorithm over these comparison algorithms for two well-known test suites.

关键词： Computational modeling optimization Computational efficiency Closed box Time complexity Heuristic algorithms Gaussian processes Artificial intelligence Random forests optimization algorithm random forest

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Task allocation algorithm and optimization model on edge collaboration

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JOURNAL OF SYSTEMS ARCHITECTURE 2020年 110卷 101778-101778页

作者： Deng, Xiaoheng Li, Jun Liu, Enlu Zhang, Honggang Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Univ Massachusetts Engn Dept Boston MA 02125 USA

This paper investigates a mobile edge computing environment for video analysis tasks where edge nodes provide their computation capacities to process the computation intensive tasks submitted by end users. First, we introduce a Cloudlet Assisted Cooperative Task Assignment (CACTA) system that organizes edge nodes that are geographically close to an end user into a cluster to collaboratively work on the user's tasks. It is challenging for the system to find an optimal strategy that assigns workload to edge nodes to meet the user's optimization goal. To address the challenge, this paper proposes multiple algorithms for different situations. Firstly, considering the situation that historical data cannot be obtained, a multi-round allocation algorithm based on EMA prediction is proposed, and the experimental results prove the efficiency and necessity of multiple rounds of transmission. To address the second case of obtaining historical data, this paper introduces a prediction-based dynamic task assignment algorithm that assigns workload to edge nodes in each time slot based on the prediction of their capacities/costs and an empirical optimal allocation strategy which is learned from an offline optimal solution from historical data. Experimental results demonstrate that our proposed algorithm achieves significantly higher performance than several other algorithms, and especially its performance is very close to that of an offline optimal solution. Finally, we propose an online task assignment algorithm based on Q-learning, which uses the model-free Q-learning algorithm to actively learn the allocation strategy of the system, and the experimental results verify the superiority and effectiveness of this algorithm.

关键词： Edge computing Task assignment Prediction Q-Learning optimization algorithm

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Nonlinear Feature Selection for Multi-target Regression Problems

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SN Computer Science 2025年第5期6卷 1-15页

作者： Kanouté, Mamadou Grall-Maës, Edith Beauseroy, Pierre Computer Science and Digital Society Laboratory (LIST3N) University of Technology of Troyes Troyes France

When designing an inference model, feature selection methods can be used to select a subset of relevant variables from the original set of variables to increase the performance and reduce the complexity of the model. In this work, a new feature selection method exploiting nonlinear relationships between variables is introduced in the context of multi-target regression problems. The proposed approach is based on a neural network with one hidden layer in which a regularization term is introduced to enforce variable selection. Experiments on synthetic and real-world datasets show the effectiveness of the proposed approach. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mathematical computation Multi-output regression Neural network optimization algorithm Supervised nonlinear feature selection

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Enhance chaotic gravitational search algorithm (CGSA) by balance adjustment mechanism and sine randomness function for continuous optimization problems

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 537卷 122621-000页

作者： Jiang, Jianhua Yang, Xi Meng, Xianqiu Li, Keqin Jilin Univ Finance & Econ Dept Data Sci Changchun 130117 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

The gravitational search algorithm (GSA) is a population-based meta-heuristic optimization algorithm which finds the optimal solution by the law of gravity and attraction between objects. However, as the number of iterations increases, the increase of the quality of the agents makes GSA fall into the local optimal solution more easily, which greatly reduces the exploration capability of the algorithm. Although the chaotic gravitational search algorithm (CGSA) uses chaotic maps for improving diversity to solve this problem, it still has problems with the balance of exploration and exploitation. This paper proposes the balance adjustment based chaotic gravitational search algorithm (BA-CGSA), which introduces the sine randomness function and the balance mechanism to solve the above problem. 30 benchmark functions of IEEE CEC 2014 are adopted to evaluate the performance of the proposed algorithm in terms of exploration and exploitation. Meanwhile, a real engineering design problem is used to illustrate the ability of the algorithm to solve practical application problems. The experimental results demonstrate its good performance in continuous optimization problems. (C) 2019 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Gravitational search algorithm Sine cosine algorithm Balance adjustment mechanism

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Thermal resistance optimization of ultra-thin vapor chamber based on data-driven model and metaheuristic algorithm

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2024年 153卷

作者： Ye, Guimin Sheng, Yuxuan Zou, Yaping Zhang, Yang Tong, Wentao Yu, Xiao Jian, Qifei South China Univ Technol Sch Mech & Automot Engn Guangzhou 510641 Guangdong Peoples R China Guangzhou Inst Sci & Technol Guangzhou 510540 Peoples R China Guizhou Yong Hong Aviat Machinery Co LTD Guiyang 550000 Guizhou Peoples R China Aviat Ind Corp China Shenyang Aeroengine Res Inst Shenyang 110015 Peoples R China

The ultra-thin vapor chamber(UTVC) is extensively utilized across various fields due to its excellent heat dissipation performance and good temperature uniformity. The data-driven modeling approach is well suited to predict the thermal resistance of the UTVC due to its great flexibility and accuracy. In this paper, a novel approach is proposed to optimize the UTVC thermal resistance, which combines the radial basis function neural network(RBFNN) model with an improved adaptive differential fish swarm evolution algorithm(ADFEA). The mean square error of the RBFNN model was 0.00016 on the training set and 0.00027 on the test sets, which indicates that the model is able to accurately predict the thermal resistance of the UTVC. The data are obtained from experiments on a mesh wick UTVC with dimensions of 124 x 14 x 1 mm. A novel optimization algorithm, ADFEA, has been designed to enhance optimization capability and convergence accuracy. This algorithm combines differential algorithm and artificial fish swarm algorithm, incorporating a parameter adaptation mechanism. The optimal operating parameters of the UTVC are obtained by ADFEA optimization and the accuracy of the optimized results is verified by experiment. The proposed optimization method provides new insights for the design and optimization of UTVC.

关键词： Ultra-thin vapor chamber Thermal resistance Operating parameters Data -driven model optimization algorithm

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Hybrid Evolutionary algorithm for the optimization of Interplanetary Trajectories

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JOURNAL OF SPACECRAFT AND ROCKETS 2009年第2期46卷 365-372页

作者： Sentinella, Matteo Rosa Casalino, Lorenzo Politecn Torino Dipartimento Energet I-10129 Turin Italy

A hybrid evolutionary algorithm is applied to the design of interplanetary trajectories with multiple impulses and gravity assists. The optimization procedure runs three different optimizers based on genetic algorithms, differential evolution, and particle swarm optimization "in parallel";the algorithms, which can also he employed separately, are used synergistically here by letting the best individuals, found by each-algorithm, migrate to the others at prescribed intervals. A comparison with the results presented In recent literature, which state that differential evolution is well suited to deal with this kind of problem, is carried out. The performance of the hybrid optimizer is comparable to that of differential evolution in terms of computational time and function evaluations when problems with a reduced number of variables are considered. The hybrid optimizers may instead exhibit better performance when more complex problems are dealt with. The results also show that the algorithm performance is remarkably improved by introducing a "mass mutation' operator to avoid premature converge nee to suboptimal solutions and by means of a particular choice of the variables to describe the trajectory.

关键词： Particle Swarm optimization Interplanetary Trajectories Saturn Genetic algorithm Cassini Mission Orbital Period Lambert's Problem optimization algorithm Deep Space Maneuver Artificial Neural Network

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Artificial Intelligence in Geopolymer Concrete Mix Design: A Comprehensive Review of Techniques and Applications

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Iranian Journal of Science and Technology - Transactions of Civil Engineering 2025年 1-33页

作者： Mushthofa, Malik Thedy, John Teguh, Mochamad Purwanto Pratama, Adjie Gemilang Han, Ay Lie Department of Civil Engineering Islamic University of Indonesia Yogyakarta 55584 Indonesia Department of Bioenvironmental Systems Engineering National Taiwan University Taipei 10617 Taiwan Department of Civil Engineering Diponegoro University Semarang 50275 Indonesia

This systematic review explores the application of Artificial Intelligence (AI) in optimizing the mix design of fly ash-based geopolymer concrete (FABGC). Analyzing studies published between 2014 and 2025, it examines key methodologies, including machine learning models, optimization algorithms, and multi-criteria decision-making approaches. Critical aspects such as data preprocessing, AI model selection, hyperparameter tuning, explainable AI (XAI), and optimization strategies are synthesized to provide a comprehensive perspective on AI-driven FABGC research. The review identifies Deep Residual Networks (ResNet) and Extreme Gradient Boosting (XGB) as the most accurate models for predicting FABGC strength, consistently outperforming others due to their lower error metrics. Backpropagation Neural Networks (BPNN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) also demonstrate competitive performance, while Random Forest (RF) and Decision Tree (DT) models excel in computational efficiency with shorter training times. Despite being the most widely implemented, Artificial Neural Networks (ANN) rarely achieve the highest predictive accuracy. Traditional regression methods, though straightforward, lag behind in performance. These findings underscore the need for standardized datasets, enhanced collaboration, and innovative AI-driven approaches to improve FABGC mix design optimization. Addressing these challenges will facilitate more reliable and efficient AI applications in sustainable concrete technology. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Artificial intelligence Fly ash Geopolymer concrete Mix design optimization algorithm

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Artificial meerkat algorithm: a new metaheuristic algorithm for solving optimization problems

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PHYSICA SCRIPTA 2024年第12期99卷 125280-125280页

作者： Wang, Xiaowei Huangshan Univ Sch Tourism Huangshan Anhui Peoples R China

In this study, a novel artificial meerkat optimization algorithm (AMA) is proposed to simulate the cooperative behaviors of meerkat populations. The AMA algorithm is designed with two sub-populations, multiple search strategies, a multi-stage elimination mechanism, and a combination of information sharing and greedy selection strategies. Drawing inspiration from the intra-population learning behavior, the algorithm introduces two search mechanisms: single-source learning and multi-source learning. Additionally, inspired by the sentinel behavior of meerkat populations, a search strategy is proposed that combines Gaussian and L & eacute;vy variations. Furthermore, inspired by the inter-population aggression behavior of meerkat populations, the AMA algorithm iteratively applies these four search strategies, retaining the most suitable strategy while eliminating others to enhance its applicability across complex optimization problems. Experimental results comparing the AMA algorithm with seven state-of-the-art algorithms on 53 test functions demonstrate that the AMA algorithm outperforms others on 71.7% of the test functions. Moreover, experiments on challenging engineering optimization problems confirm the superior performance of the AMA algorithm over alternative algorithms.

关键词： artificial meerkat algorithm mechanism multiple search strategies multi-stage gaussian variation optimization algorithm engineering

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Supply-Demand-Based optimization: A Novel Economics-Inspired algorithm for Global optimization

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IEEE ACCESS 2019年 7卷 73182-73206页

作者： Zhao, Weiguo Wang, Liying Zhang, Zhenxing Hebei Univ Engn Sch Water Conservancy & Hydropower Handan 056021 Peoples R China Univ Illinois Prairie Res Inst Illinois State Water Survey Champaign IL 61820 USA

A novel metaheuristic optimization algorithm, named supply-demand-based optimization (SDO), is presented in this paper. SDO is a swarm-based optimizer motivated by the supply-demand mechanism in economics. This algorithm mimics both the demand relation of consumers and supply relation of producers. The proposed algorithm is compared with other state-of-the-art counterparts on 29 benchmark test functions and six engineering optimization problems. The results on the unconstrained test functions prove that SDO is able to provide very promising results in terms of exploration, exploitation, local optima avoidance, and convergence rate. The results on the constrained engineering problems suggest that SDO is considerately competitive in terms of computational expense, convergence rate, and solution accuracy. The codes are available at https://***/matlabcentral/fileexchange/71764-supply-demand-based-optimization.

关键词： Supply-demand-based optimization global optimization engineering design constrained problems optimization algorithm particle swarm optimization swarm intelligence

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