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meta-heuristic algorithms for influence maximization: a survey

EVOLVING SYSTEMS 2025年第1期16卷 1-28页

作者： Fan, Chencheng Wang, Zhixiao Zhang, Jian Zhao, Jiayu Meng, Xianfeng China Univ Min & Technol Dept Comp Sci Xuzhou 221116 Jiangsu Peoples R China Minist Educ Mine Digitizat Engn Res Ctr Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Phys Educ Xuzhou 221116 Jiangsu Peoples R China

Influence maximization (IM) is a key problem in social network analysis, which has attracted attention of many scholars due to the wide range of applications, the variety of IM algorithms have been proposed from different perspectives. In this paper, we review IM algorithms from the perspective of meta-heuristic optimization, proposed a two-layer structure taxonomy to organize almost all the meta-heuristic IM algorithms. The initial layer, predicated upon the delineation of problem construction models, stratifies IM algorithms into two categories: single-objective and multi-objective IM algorithms. Subsequently, the secondary layer discerns between evolution-based and population intelligence-based IM algorithms, delineating them according to the underlying conceptual frameworks, a detailed exposition and analysis ensue. Subsequent scrutiny involves an exhaustive evaluation of the merits and demerits inherent in each IM algorithm, juxtaposing considerations such as time complexity and experimental validation methodologies. Furthermore, we distill myriad strategies aimed at enhancing accuracy and mitigating time complexity across the four phases of the algorithmic process. Finally, based on the above analysis, the challenges and future directions of IM problems are outlined from the perspective of algorithms, applications and models.

关键词： Influence maximization meta-heuristic algorithms Multi-objective optimization Complex networks Genetic algorithms

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A review of feature selection methods based on meta-heuristic algorithms

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2025年第1期37卷 1-51页

作者： Sadeghian, Zohre Akbari, Ebrahim Nematzadeh, Hossein Motameni, Homayun Islamic Azad Univ Dept Comp Engn Sari Branch Sari Iran

Feature selection is a real-world problem that finds a minimal feature subset from an original feature set. A good feature selection method, in addition to selecting the most relevant features with less redundancy, can also reduce computational costs and increase classification performance. One of the feature selection approaches is using meta-heuristic algorithms. This work provides a summary of some meta-heuristic feature selection methods proposed from 2018 to 2022 that were designed and implemented on a wide range of different data for solving feature selection problem. Evaluation criteria, fitness functions and classifiers used and the time complexity of each method are also depicted. The results of the study showed that some meta-heuristic algorithms alone cannot perfectly solve the feature selection problem on all types of datasets with an acceptable speed. In other words, depending on dataset, a special meta-heuristic algorithm should be used. The results of this study and the identified research gaps can be used by researchers in this field.

关键词： Data dimension reduction classification feature selection optimisation algorithm meta-heuristic algorithms

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A performance comparison of eight meta-heuristic algorithms for the optimal design of large-scale truss structures

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2025年第3期47卷 1-37页

作者： Bodalal, Rafiq Libyan Int Univ Sch Engn & Technol Benghazi Libya

In recent years, meta-heuristic (MH) algorithms have emerged as powerful optimization tools, enabling efficient solutions to complex truss optimization tasks. In this study, a performance assessment of eight newly developed MH algorithms is presented for the optimal design of large-scale truss structures. The algorithms selected for testing include the Manta-ray Foraging Optimization (MRFO), Artificial Gorilla Troops Optimizer (GTO), Equilibrium Optimizer (EO), Henry Gas Solubility Optimizer (HGSO), Aquila Optimizer (AO), Heap-based Optimizer (HBO), Snake Optimizer (SO), and Artificial Hummingbird Algorithm (AHA). Collectively, the eight techniques cover recent advances in nature-inspired MH approaches and use diverse search mechanisms for their optimization procedure. To effectively compare the performance of the eight techniques, five large-scale truss benchmarks (including the 4666-bar truss tower) were employed as test beds. For statistical significance, the Friedman ranking test was used to quantitatively compare the performance of the eight techniques. The results of the comparison show HBO as the best-performing method by consistently providing the lightest truss designs with the least computational effort. Quantitatively, HBO produced structures that were (on average) 21% lighter than the other seven techniques. In contrast, both AO and HGSO suffered from poor results and slow convergence speeds. HGSO in particular emerged as the worst-performing method and was prone to falling into local optima. In light of this, recommendations for improving the optimization performance for the eight techniques were made within the article.

关键词： meta-heuristic algorithms Truss optimization Algorithm comparison Large-scale structures

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Population-level center-based sampling for meta-heuristic algorithms

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 92卷

作者： Khosrowshahli, Rasa Rahnamayan, Shahryar Ibrahim, Amin Bidgoli, Azam Asilian Makrehchi, Masoud Ontario Tech Univ Fac Engn & Appl Sci Oshawa ON Canada Brock Univ Dept Engn St Catharines ON Canada Ontario Tech Univ Fac Business & Informat Technol Oshawa ON Canada Wilfrid Laurier Univ Fac Sci Waterloo ON Canada

In recent years, the challenge of enhancing the efficiency and effectiveness of meta-heuristic algorithms has gained significant attention. Center-based sampling has shown promise in addressing this challenge, yet its application often requires customization for specific algorithms, limiting its generalizability. This study identifies a gap in the literature regarding the operation-independent application of center-based sampling. To address this, we propose a novel center-based sampling strategy at the population level, which can be seamlessly integrated into any population-based optimization algorithm. Our approach employs a collaborative multi-parent method to generate multiple center-based solutions, thereby increasing diversity and exploiting the solution space more effectively. We introduce two specific strategies: cluster-driven center-based sampling for single-objective optimization and ranking-driven center-based sampling for multi-objective optimization. The performance of these strategies is evaluated using the benchmark functions for the CEC-2017 competition on 5 single- and 6 many-objective evolutionary algorithms, demonstrating 40% similar to 100% statistical fitness improvement ratio over parent meta-heuristic algorithms, respectively. These findings highlight the potential of population-level center-based sampling to enhance the performance of meta-heuristic algorithms.

关键词： Center-based sampling Population-based algorithms Single-objective optimization Multi-objective optimization meta-heuristic algorithms

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Nature-Inspired meta-heuristic algorithms for Resource Allocation in the Internet of Things

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第5期38卷

作者： Amirghafouri, Fatemeh Neghabi, Ali Akbar Shakeri, Hassan Sola, Yasser Elmi Islamic Azad Univ Dept Comp Engn Sabzevar Branch Sabzevar Iran Islamic Azad Univ Dept Comp Engn Mashhad Branch Mashhad Iran

The Internet of Things (IoT) is a paradigm-shifting concept that helps realize an acquisition, processing, and analytical global network, digitizing tangible entities to enhance efficiency and safety in various smart cities, healthcare, and Industry 4.0 domains. However, whereas IoT scales, with several heterogeneous devices and diverse, varied capabilities with service demands, cloud resource management in IoT usually faces the challenge of intricate complexity in efficiently allocating resources despite the demand for varied quality of service (QoS). Hence, this paper systematically reviews nature-inspired metaheuristic algorithm applications in IoT resource allocation for solving NP-hard problems. We summarize recent advances in metaheuristic methods, including comparisons against traditional methods. We also discuss practical feasibility and scaling issues in real-world IoT scenarios. Further, we have highlighted a few gaps in the current literature and provided recommendations on specific topics for future research, thereby indicating how to develop scalable, efficient resource allocation solutions to meet IoT's ever-evolving demands.

关键词： Internet of Things (IoT) meta-heuristic algorithms resource allocation resource management review

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A Comparative Analysis of meta-heuristic algorithms for Finite Element Optimization 4th

A Comparative Analysis of Meta-heuristic Algorithms for Fini...

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4th International Conference of the Italian Association of Design Methods and Tools for Industrial Engineering

作者： Cirello, Antonino Ingrassia, Tommaso Marannano, Giuseppe Ricotta, Vito Univ Palermo Dept Engn Viale Sci I-90128 Palermo Italy

ISBN: (纸本)9783031765964;9783031765971

In the field of structural engineering, the optimization process is crucial for ensuring both the efficiency and safety of designed structures. The persistent search for solutions that maximize structural performance while minimizing costs presents a fascinating and complex challenge. In this context, this paper aims to analyze and compare three different Nature-inspired optimization algorithms that are derived from theory of biological evolution, swarm intelligence, and chemical and physical processes. Specifically, the study aims to provide a comprehensive overview of the performance and characteristics of Genetic algorithms, the Firefly Algorithm, and the Group Search Optimizer Algorithm in the context of structural optimization within the ANSYS APDL environment. The results obtained from each meta-heuristic algorithm will be discussed and compared in terms of their convergence, efficiency, and capability to find optimal and/or approximate solutions.

关键词： Structural optimization meta-heuristic algorithms finite element method ANSYS

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Applying meta-heuristic dynamic algorithms to maximize impact and discover significant nodes in social networks

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SOCIAL NETWORK ANALYSIS AND MINING 2025年第1期15卷

作者： Motevalli, Hooman Khezri, Edris Hassanzadeh, Hiwa Boukani, Jamal Azizbeygi Trik, Mohammad Vaezi, Anvar Liu, Lili Sharif Univ Technol Chem Engn Chem & Petr Engn Dept Tehran Iran Islamic Azad Univ Dept Comp Boukan Branch Boukan Iran Raparin Univ Dept Comp Sci Rania Iraq Urmia Univ Dept Comp Engn Orumiyeh Iran Urmia Univ Dept Comp Sci & Engn Orumiyeh Iran

Social media platforms have transformed global communication, shaping domains such as marketing, public health, and politics by enabling mass information dissemination. Identifying influential individuals in these networks termed the influence maximization problem is critical for optimizing information diffusion strategies like product marketing and disease prevention. Traditional algorithms often struggle with the dynamic and scalable nature of modern social networks, rendering them inadequate for real-world applications. To address these challenges, this study introduces the Advanced Dynamic Generalized Vulture Algorithm (ADGVA), a meta-heuristic approach designed to handle network dynamism effectively. ADGVA combines dynamic exploration-exploitation mechanisms, hierarchical analysis, and predictive modeling to identify high-influence nodes with enhanced accuracy while reducing computational complexity. Unlike traditional methods, it dynamically adapts to evolving network structures through an iterative seed set adjustment mechanism, maintaining high performance even in large-scale networks. The algorithm's efficiency and robustness are validated through experiments on real-world datasets, demonstrating superior scalability, precision, and influence spread compared to state-of-the-art approaches. The study also highlights potential limitations, including sensitivity to parameter settings and computational cost in highly dynamic environments, laying the groundwork for future research. By bridging computational feasibility with practical applicability, ADGVA sets a new benchmark for influence maximization, offering transformative solutions for the analysis and optimization of dynamic social networks.

关键词： Advanced dynamic generalized vulture algorithm Network dynamics Diffusion model meta-heuristic algorithms Social networks Influence maximization

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A deep learning-based particle contribution evaluation mechanism for meta-heuristic optimization algorithms

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APPLIED SOFT COMPUTING 2025年 176卷

作者： Su, Fang Liu, Ying Chen, Liquan Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China

meta-heuristic algorithms have been a popular research field nowadays. However, they are prone to falling into local optima, especially when applied to the Problem with Weak Influence of Global Optimal Solutions (PWIGOS), where the global optimal solution has a very small influence area in the search space. In this paper, based on the analysis of the influence of PWIGOS on meta-heuristic optimization algorithms, a novel Particle Contribution Evaluation Mechanism (PCEM) is proposed. Different from the current mechanisms in this field, PCEM is innovative in that it uses deep learning models to infer whether a particle is a high contribution particle within the influence region of the global optimum according to the feature information. This provides meta-heuristics with this additional critical information from outside the optimization process to guide the correct evolution of particle population. Additionally, a dynamic threshold setting method and a particle evolution adjustment method are designed, and three different types of classic and representative meta-heuristic algorithms, differential evolution (DE), Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are selected as application examples of PCEM. Experiments are conducted on 27 benchmark functions, CEC2017 benchmark suite and four real-word problems. According to the statistical results, PCEM not only excels in particle contribution assessment but also significantly enhances algorithm performance, especially when addressing challenging PWIGOS.

关键词： meta-heuristic algorithms PWIGOS Particle contribution evaluation Deep learning

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meta-heuristic algorithms for Hydrologic Frequency Analysis

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WATER RESOURCES MANAGEMENT 2011年第7期25卷 1855-1879页

作者： Hassanzadeh, Yousef Abdi, Amin Talatahari, Siamak Singh, Vijay P. Univ Tabriz Fac Civil Engn Tabriz Iran Texas A&M Univ Dept Biol & Agr Engn College Stn TX USA

meta-heuristic algorithms, such as the genetic algorithm and ant colony optimization, have received considerable attention in recent years due to their higher ability for solving difficult engineering optimization problems. This paper employs these techniques for estimating parameters of commonly used flood frequency distributions, and compares them with some conventional methods such as maximum likelihood, moments and probability weighted moments using annual maximum discharge data of 14 rivers from East-Azarbaijan, Iran. The results indicate that both the genetic algorithm and ant colony optimization are suitable parameter estimation alternatives. Also, the results of Monte Carlo simulation for various sample sizes, ranging from 20 to 100, demonstrate that the meta-heuristic algorithms yield accurate quantile estimates.

关键词： Hydrologic frequency analysis Conventional methods meta-heuristic algorithms Parameter estimation Monte Carlo simulation

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meta-heuristic algorithms for solving nonlinear differential equations based on multivariate Bernstein polynomials

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SOFT COMPUTING 2022年第2期26卷 605-619页

作者： Abo-bakr, Rasha M. Mohamed, N. A. Mohamed, S. A. Zagazig Univ Fac Sci Dept Math POB 44519 Zagazig Egypt Fac Engn Dept Phys & Engn Math POB 44519 Zagazig Egypt

This paper concerns with solving nonlinear boundary value problems with Dirichlet boundary conditions by a novel approximation technique based on Bernstein polynomials and meta-heuristic algorithms. A trial solution is expressed as a weighted sum of Bernstein polynomials of degree n. Some weights of this trial solution are determined by enforcing exact satisfaction of the Dirichlet boundary conditions. The remaining weights are determined such that they minimize the sum of squares of the residuals of the differential equation computed at arbitrary set of interior points in the domain of the problem. Error bounds for the approximate solutions for ordinary and partial differential equations are derived. The resulting optimization problem is solved using meta-heuristic algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), and a hybrid PSO-GA algorithm. The accuracy of the proposed approach is demonstrated by solving some nonlinear boundary value problems including Bratu problems in one- and two-dimensional spaces.

关键词： Nonlinear differential equations Bernstein polynomial meta-heuristic algorithms Bratu differential equations Finite difference methods

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