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population-based optimization in structural engineering: a review

ARTIFICIAL INTELLIGENCE REVIEW 2022年第1期55卷 345-452页

作者： Kashani, Ali R. Camp, Charles V. Rostamian, Mehdi Azizi, Koorosh Gandomi, Amir H. Univ Memphis Dept Civil Engn Memphis TN 38152 USA Univ Technol Sydney Fac Engn & IT Ultimo NSW 2007 Australia

Structural engineering is focused on the safe and efficient design of infrastructure. Projects can range in size and complexity, many requiring massive amounts of materials and expensive construction and operational costs. Therefore, one of the primary objectives for structural engineers is a cost-effective design. Incorporating optimality criteria into the design procedure introduces additional complexities that result in problems that are nonlinear, nonconvex, and have a discontinuous solution space. population-based optimization algorithms (known as metaheuristics) have been found to be very efficient approaches to these problems. Many researchers have developed and applied state-of-art metaheuristics to automate and optimize the design of real-world civil engineering problems. While there is a large body of published papers in this area, there are few comprehensive reviews that list, summarize, and categorize metaheuristic optimization in structural engineering. This paper provides an extensive survey of a wide range of metaheuristic techniques to structural engineering optimization problems. Also, information is provided on available structural engineering benchmark problems, the formulation of different objective functions, and the handling of various types of constraints. The performance of different optimization techniques is compared for many benchmark problems.

关键词： Engineering optimization Civil engineering population-based optimization Global optimization Metaheuristic algorithms Structural optimization

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Infrared and visible image fusion using fuzzy logic and population-based optimization

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APPLIED SOFT COMPUTING 2012年第3期12卷 1041-1054页

作者： Saeedi, Jamal Faez, Karim Amirkabir Univ Technol Dept Elect Engn Tehran Polytech Tehran Iran

This paper presents a new wavelet-based algorithm for the fusion of spatially registered infrared and visible images. Wavelet-based image fusion is the most common fusion method, which fuses the information from the source images in the wavelet transform domain according to some fusion rules. We specifically propose new fusion rules for fusion of low and high frequency wavelet coefficients of the source images in the second step of the wavelet-based image fusion algorithm. First, the source images are decomposed using dual-tree discrete wavelet transform (DT-DWT). Then, a fuzzy-based approach is used to fuse high frequency wavelet coefficients of the IR and visible images. Particularly, fuzzy logic is used to integrate the outputs of three different fusion rules (weighted averaging, selection using pixel-based decision map (PDM), and selection using region-based decision map (RDM)), based on a dissimilarity measure of the source images. The objective is to utilize the advantages of previous pixel-and region-based methods in a single scheme. The PDM is obtained based on local activity measurement in the DT-DWT domain of the source images. A new segmentation-based algorithm is also proposed to generate the RDM using the PDM. In addition, a new optimization-based approach using population-based optimization is proposed for the low frequency fusion rule instead of simple averaging. After fusing low and high frequency wavelet coefficients of the source images, the final fused image is obtained using the inverse DT-DWT. This new method provides improved subjective and objectives results as compared to previous image fusion methods. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Infrared and visible images Dual-tree discrete wavelet transform Fuzzy logic population-based optimization

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Sentiment Analysis and population-based optimization for Estimating Human Lifespan using Daily Activities 8

Sentiment Analysis and Population-based Optimization for Est...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Yesujyothi, Y. Bhanurangarao, M. Chandra, Meduri Raghu Department of Information Technology Bhimavaram India

ISBN: (纸本)9798350376425

This research introduces a novel system that utilizes sentiment analysis and population-based optimization to estimate human longevity based on daily activities. By analyzing emotional content and physical activities from two key life stages, youth and adulthood, the system provides a comprehensive assessment of overall health. Sentiment analysis is employed to quantify emotional well-being, a significant predictor of mental power. Physical activities are evaluated to assess physical strength, contributing to a holistic understanding of health. population-based optimization is then applied to extend these findings across various demographic groups, enhancing prediction accuracy. This multidisciplinary approach offers a unique perspective on how daily activities influence lifespan. By combining psychological insights with quantitative analytics, the system provides a more effective method for predicting life expectancy. © 2024 IEEE.

关键词： Emotions estimate Human lifespan population-based optimization Sentimental analysis

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Hybridized random forest with population-based optimization for predicting shear properties of rock fractures

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JOURNAL OF COMPUTATIONAL SCIENCE 2023年 72卷

作者： Zhou, Jian Wang, Zhenyu Li, Chuanqi Wei, Wei Wang, Shiming Armaghani, Danial Jahed Peng, Kang Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Grenoble Alpes Univ Lab 3SR CNRS UMR 5521 F-38000 Grenoble France Norin Min Ltd Beijing 100053 Peoples R China Hunan Univ Sci & Technol Sch Civil Engn Xiangtan 411201 Hunan Peoples R China Univ Technol Sydney Sch Civil & Environm Engn Ultimo NSW 2007 Australia

Rock mass fractures play an essential role in controlling the shear behavior of natural rocks. The clear understanding of the fracture's shear parameters is of great significance in maintaining underground structure stability. A novel approach combining the random forest (RF) model and two population-based optimization algorithms respectively (the Sine Cosine Algorithm (SCA) and the whale optimization algorithm (WOA)) was proposed to predict the shear strength, peak shear displacement, and dilation angles of rock fractures. 84 direct shear data was divided into training set and test set for training and testing the proposed models. Coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE), three indexes were utilized to evaluate the model prediction accuracy. The prediction performance indicated that SCA-RF and WOA-RF models achieved high accuracy in predicting the shear strength and dilation angles of rock fractures. These two models also perform satisfactorily in predicting the peak shear displacement by optimizing the dataset division and fitness function. Comparing with Barton-Bandis and Asadollahi-Tonon empirical models, the proposed models have significant advantages in estimating the shear properties of rock fractures. It has important implications for the development of accurate and reliable models for predicting the shear behavior of natural rocks, that some data processing techniques were used based on the characteristics of the direct shear dataset.

关键词： population-based optimization Rock fractures Random forest Shear properties

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PyPop7: a pure-Python library for population-based black-box optimization

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 14251-14278页

作者： Qiqi Duan Guochen Zhou Chang Shao Zhuowei Wang Mingyang Feng Yuwei Huang Yajing Tan Yijun Yang Qi Zhao Yuhui Shi Harbin Institute of Technology Harbin China and Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Shenzhen China University of Technology Sydney Sydney Australia Space and Astronomy CSIRO Marshfield Australia University of Birmingham Birmingham UK Tencent Inc. Shenzhen China

In this paper, we present an open-source pure-Python library called PyPop7 for black-box optimization (BBO). As population-based methods (e.g., evolutionary algorithms, swarm intelligence, and pattern search) become increasingly popular for BBO, the design goal of PyPop7 is to provide a unified API and elegant implementations for them, particularly in challenging high-dimensional scenarios. Since these population-based methods easily suffer from the notorious curse of dimensionality flowing to random sampling as one of core operations for most of them, recently various improvements and enhancements have been proposed to alleviate this issue more or less mainly via exploiting possible problem structures: such as, decomposition of search distribution or space, low-memory approximation, low-rank metric learning, variance reduction, ensemble of random subspaces, model self-adaptation, and fitness smoothing. These novel sampling strategies could better exploit different problem structures in high-dimensional search space and therefore they often result in faster rates of convergence and/or better qualities of solution for large-scale BBO. Now PyPop7 has covered many of these important advances on a set of well-established BBO algorithm families and also provided an open-access interface to adding the latest or missed black-box optimizers for further functionality extensions. Its well-designed source code (under GPL-3.0 license) and full-edged online documents (under CC-BY 4.0 license) have been freely available at https://***/Evolutionary-Intelligence/pypop and https://***, respectively.

关键词： black-box optimization evolutionary computation large-scale optimization open-source software population-based optimization swarm intelligence

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A distance determination wolf pack algorithm for solving high-dimensional complex functions and its application

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Journal of Supercomputing 2025年第8期81卷 1-52页

作者： Lai, Yuanda Wu, Husheng Peng, Qiang Hong, Chen Cheng, Shi College of Equipment Support and Management Engineering University of PAP A1 Wujing Rd Xi’an 710086 China Foundation Department Engineering University of PAP A1 Wujing Rd Xi’an 710086 China School of Computer Science Shaanxi Normal University Xi’an 710119 China

The wolf pack algorithm (WPA), a swarm intelligence method inspired by wolf hunting behaviors, faces limitations in convergence accuracy, computational efficiency, and local optima avoidance. This paper proposes a Distance Determination Wolf Pack Algorithm (DDWPA) to address these challenges. Key innovations include: (1) An initial wandering position recording mechanism to prevent premature local convergence;(2) a distance judgment factor integrated into the summoning phase for accelerated population evolution;and (3) a head wolf relative positioning strategy and distance-updated regeneration operator to enhance global search capability. The algorithm's convergence is theoretically verified through Markov process analysis. Extensive evaluations on CEC2022 and CEC2020 benchmark functions demonstrate DDWPA's superiority over existing swarm intelligence algorithms in terms of solution precision and convergence speed. Four engineering case studies further validate its effectiveness in handling high-dimensional real-world optimization problems. Results consistently show that DDWPA achieves superior global convergence and computational efficiency while maintaining robustness against local optima traps. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Distance determination factor High-dimensional function optimization population-based optimization Wander threshold Wolf pack algorithm

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A Multi-Objective JAYA-based Workflow Scheduling in Fog-Cloud Computing Environment

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Procedia Computer Science 2025年 259卷 230-239页

作者： Saiyam Varshney Gur Mauj Saran Srivastava Department of Physics and Computer Science Dayalbagh Educational Institute Agra India

The rapid growth of cloud-fog computing has introduced significant challenges in improving system performance and resource efficiency for dependent tasks. The inherent complexity and variability of tasks, coupled with fluctuating resource availability, make it difficult to identify an optimal scheduling algorithm. This study addresses these challenges by proposing a novel multi-objective optimization workflow scheduling approach based on the JAYA algorithm. The proposed method effectively tackles the complexities of scheduling within a distributed computing framework by integrating both fog and cloud resources. The approach aims to simultaneously minimize key performance metrics such as makespan, energy consumption, and cost, thereby enhancing overall system performance through optimized task allocation across heterogeneous resources. The Simulations conducted using the FogWorkflowSim toolkit compare the proposed approach with established algorithms, including Ant Colony optimization (ACO), and Particle Swarm optimization (PSO). The results demonstrate the superiority of the JAYA algorithm in achieving enhanced time efficiency, energy savings, and cost reduction. These findings highlight its significant contribution to advancing cloud-fog resource management by providing a robust framework for multi-objective optimization in workflow scheduling scenarios.

关键词： Cloud computing Fog computing IoT Scientific Workflows population-based optimization Makespan Energy consumption Cost

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Optimizing Rotary Cement Kiln modelling: A comparative analysis of metaheuristics in a real-world application

RESULTS IN ENGINEERING

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RESULTS IN ENGINEERING 2025年 25卷

作者： Castan-Lascorz, Miguel Angel Alcaide-Moreno, Antonio Arroyo, Jorge CIRCE Technol Ctr Parque Empresarial DinamizaAve Ranillas3D 1a Pla Zaragoza 50018 Spain

This study introduces a real-world metaheuristic application designed to minimize the simulation errors of a novel one-dimensional (1D) numerical model of a rotary cement kiln, calibrated by Computational Fluid Dynamics (CFD) results. This model aims to significantly reduce simulation time, facilitating the analysis of multiple scenarios. A major challenge is the high proportion of invalid or infeasible solutions typical in industrial environments, compounded by the time-sensitive nature of real-world applications. Another notable difficulty lies in selecting among the plethora of metaheuristic methods, which may exhibit significant variations in search efficiency. To address these challenges, this work evaluates the performance of five state-of-the-art metaheuristics and examines the impact of two penalty methods, death and static, on solution quality, constrained by a limited number of model evaluations due to time constraints. Furthermore, an adjustment to the Marine Predators Algorithm (MPA) is proposed, targeted at enhancing its exploitation phase in alignment with the established stopping criteria. The algorithm design and selection process adhere to recommendations from literature review articles, employing robust statistical techniques, including Bayesian analysis and Monte Carlo sampling to support the replicability of analysis. Results indicate that the adapted MPA is expected to achieve similar error reduction to the other methods but requires 40 % fewer evaluations, thus positioning as the most suitable for the industrial application. The findings are anticipated to provide practical insights for researchers and developers by presenting a case study grounded in best practices to support more effective algorithm design and informed decision-making in real-world optimization challenges.

关键词： Metaheuristics Penalty method population-based optimization Bayesian statistics Constraint optimization Numerical modelling

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A Linear State-Space Analysis of the Migration Model in an Island Biogeography System

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2011年第2期41卷 331-337页

作者： Sinha, Abhishek Das, Swagatam Panigrahi, B. K. Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 W Bengal India Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

Biogeography deals with the study of the distribution of biodiversity over space and time and has been well studied by naturists and biologists for over the last five decades. Recently, the theory of biogeography has been applied to solve difficult engineering optimization problems in the form of a nature-inspired metaheuristic, known as biogeography-based optimization (BBO) algorithm. In this correspondence paper, we present an in-depth analysis of the linear time-invariant (LTI) system model of immigration and emigration of organisms in an island biogeography system that forms the basis of BBO. We find the bound of the eigenvalues of the general LTI system matrix using the Perron-Frobenius theorem from linear algebra. based on the bounds of the eigenvalues, we further investigate four important properties of the LTI biogeography system, including the system reasonability with probability distribution vectors, stability, convergence, and nature of the equilibrium state. Our analysis gives a better insight into the dynamics of migration in actual biogeography systems and also helps in the understanding of the search mechanism of BBO on multimodal fitness landscapes.

关键词： Biogeography-based optimization (BBO) convergence equilibrium theory of island biogeography island biogeography linear time-invariant (LTI) system population-based optimization stability

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Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2016年 26卷 8-22页

作者： Abedinpourshotorban, Hosein Shamsuddin, Siti Mariyam Beheshti, Zahra Jawawi, Dayang N. A. Univ Teknol Malaysia UTM Big Data Ctr Skudai 81310 Johor Malaysia Univ Teknol Malaysia Dept Software Engn Skudai 81310 Johor Malaysia

This paper presents a physics-inspired metaheuristic optimization algorithm, known as Electromagnetic Field optimization (EFO). The proposed algorithm is inspired by the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, a possible solution is an electromagnetic particle made of electromagnets, and the number of electromagnets is determined by the number of variables of the optimization problem. EFO is a population based algorithm in which the population is divided into three fields (positive, negative, and neutral);attraction-repulsion forces among electromagnets of these three fields lead particles toward global minima. The golden ratio determines the ratio between attraction and repulsion forces to help particles converge quickly and effectively. The experimental results on 30 high dimensional CEC 2014 benchmarks reflect the superiority of EFO in terms of accuracy and convergence speed over other state-of-the-art optimization algorithms. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Global optimization Metaheuristics population-based optimization Golden ratio Evolutionary algorithms

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