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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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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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In Search of Excellence: SHOA as a Competitive Shrike optimization Algorithm for Multimodal Problems

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IEEE ACCESS 2024年 12卷 98407-98425页

作者： Abdulkarim, Hanan K. Rashid, Tarik A. Salahaddin Univ Erbil Engn Coll Software Engn Dept Erbil 44002 Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil 00964 Iraq

This paper proposes the Shrike optimization Algorithm (SHOA) as a swarm intelligence optimization algorithm. Many creatures, who live in groups and survive for the next generation, randomly search for food;they follow the best one in the swarm, a phenomenon known as swarm intelligence. While swarm-based algorithms mimic the behaviors of creatures, they struggle to find optimal solutions in multi-modal problem competitions. The swarming behaviors of shrike birds in nature serve as the main inspiration for the proposed algorithm. The shrike birds migrate from their territory in order to survive. However, the SHOA replicates the survival strategies of shrike birds to facilitate their living, adaptation, and breeding. Two parts of optimization exploration and exploitation are designed by modeling shrike breeding and searching for foods to feed nestlings until they get ready to fly and live independently. This paper is a mathematical model for the SHOA to perform optimization. The SHOA benchmarked 19 well-known mathematical test functions, 10 from CEC-2019 and 12 from CEC-2022's most recent test functions, for a total of 41 competitive mathematical test functions and four real-world engineering problems with different conditions, both constrained and unconstrained. The statistical results obtained from the Wilcoxon ranking sum and Fridman test show that SHOA has a significant statistical superiority in handling the test benchmarks compared to competitor algorithms in multi-modal problems. The results for engineering optimization problems show the SHOA outperforms other nature-inspired algorithms in many cases.

关键词： optimization methods Classification algorithms Birds Statistics Social factors Search problems Clustering algorithms Particle swarm optimization Shrike optimization constrained optimization swarm intelligence multi-modal meta-heuristic population-based optimization engineering problem

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Discrete Student Psychology optimization Algorithm for the Word Sense Disambiguation Problem

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第3期49卷 3487-3502页

作者： Haouassi, Hichem Bekhouche, Abdelaali Rahab, Hichem Mahdaoui, Rafik Chouhal, Ouahiba Univ Abbas Laghrour Dept Math & Comp Sci ICOSI Lab Khenchela Algeria

Word Sense Disambiguation (WSD) is a key step for many natural language processing tasks such as information search, automatic translation, and sentiment analysis. WSD is the process that identifies appropriate senses of ambiguous words in the text. With the increasing number of words to be disambiguated in large amount of text data, WSD becomes very challenging and that is why an exhaustive search for the best set of senses may be unpractical. Recently, several metaheuristic approaches have been proposed for different complex optimization problems and have achieved good results. Therefore, in order to improve the WSD process, in this paper, the WSD problem is modeled as a combinatorial optimization problem, and the Discrete Student Psychology-based optimization (DSPBO) metaheuristic is proposed and used to selecting appropriate senses. A DSPBO-based WSD is proposed to disambiguate more ambiguous words together in function to their contexts in the target text, and a Lesk-based fitness function is used to guide the DSPBO metaheuristic to optimize the general semantic similarity of selected senses. The proposed approach is evaluated and compared to several recent WSD approaches on the well-known corpuses SensEval-2, SensEval-3, SemEval-2007, SemEval-13, and SemEval-15. The comparison is made in terms of F-Measure, precision, and recall. Experiments show a significant improvement both over existing knowledge lexicon-based approaches and metaheuristic-based approaches, with a higher F-measure of 84.21%, 83.33%, 87.5%, 77.58%, and 81.08% on SensEval-2, SensEval-3, SemEval-2007, SemEval-13, and SemEval-15, respectively.

关键词： Student psychology optimization Discrete student psychology optimization Word sense disambiguation Natural language processing Combinatorial optimization population-based optimization

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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 Engn Univ PAP Coll Equipment Support & Management A1 Wujing Rd Xian 710086 Peoples R China Engn Univ PAP Fdn Dept A1 Wujing Rd Xian 710086 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R 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.

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

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An efficient classification rule generation for coronary artery disease diagnosis using a novel discrete equilibrium optimizer algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期43卷 2315-2331页

作者： Haouassi, Hichem Mahdaoui, Rafik Chouhal, Ouahiba Bekhouche, Abdelaali Univ Abbas Laghrour Dept Math & Comp Sci ICOSI Lab Khenchela Algeria

Many machine learning-based methods have been widely applied to Coronary Artery Disease (CAD) and are achieving high accuracy. However, they are black-box methods that are unable to explain the reasons behind the diagnosis. The trade-off between accuracy and interpretability of diagnosis models is important, especially for human disease. This work aims to propose an approach for generating rule-based models for CAD diagnosis. The classification rule generation is modeled as combinatorial optimization problem and it can be solved by means of metaheuristic algorithms. Swarm intelligence algorithms like Equilibrium Optimizer Algorithm (EOA) have demonstrated great performance in solving different optimization problems. Our present study comes up with a Novel Discrete Equilibrium Optimizer Algorithm (NDEOA) for the classification rule generation from training CAD dataset. The proposed NDEOA is a discrete version of EOA, which use a discrete encoding of a particle for representing a classification rule;new discrete operators are also defined for the particle's position update equation to adapt real operators to discrete space. To evaluate the proposed approach, the real world Z-Alizadeh Sani dataset has been employed. The proposed approach generate a diagnosis model composed of 17 rules, among them, five rules for the class "Normal" and 12 rules for the class "CAD". In comparison to nine black-box and eight white-box state-of-the-art approaches, the results show that the generated diagnosis model by the proposed approach is more accurate and more interpretable than all white-box models and are competitive to the black-box models. It achieved an overall accuracy, sensitivity and specificity of 93.54%, 80% and 100% respectively;which show that, the proposed approach can be successfully utilized to generate efficient rule-based CAD diagnosis models.

关键词： Coronary artery disease medical diagnosis machine learning rule-based diagnosis rule discovery population-based optimization discrete equilibrium optimization algorithm

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Archery Algorithm:A Novel Stochastic optimization Algorithm for Solving optimization Problems

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Computers, Materials & Continua 2022年第7期72卷 399-416页

作者： Fatemeh Ahmadi Zeidabadi Mohammad Dehghani Pavel Trojovsky Štěpán Hubálovsky Victor Leiva Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Mathematics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic School of Industrial Engineering Pontificia Universidad Católica de ValparaísoValparaíso2362807Chile Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

Finding a suitable solution to an optimization problem designed in science is a major ***,these must be addressed utilizing proper *** on a random search space,optimization algorithms can find acceptable solutions to *** Algorithm(AA)is a new stochastic approach for addressing optimization problems that is discussed in this *** fundamental idea of developing the suggested AA is to imitate the archer’s shooting behavior toward the target *** proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the *** AA is mathematically described,and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective ***,the proposed algorithm’s performance is compared *** approaches,including teaching-learning based optimization,marine predators algorithm,genetic algorithm,grey wolf optimization,particle swarm optimization,whale optimization algorithm,gravitational search algorithm,and tunicate swarm *** to the simulation findings,the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios,and it can give adequate quasi-optimal solutions to these *** analysis and comparison of competing algorithms’performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.

关键词： Archer meta-heuristic algorithm population-based optimization stochastic programming swarm intelligence population-based algorithm Wilcoxon statistical test

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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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