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A novel metaheuristic algorithm: advanced social memory optimization

PHYSICA SCRIPTA 2025年第5期100卷 055004-055004页

作者： Fan, Shijie Wang, Ruichen Su, Kang Shijiazhuang Tiedao Univ Sch Mech Engn Shijiazhuang 050043 Peoples R China

The field of optimization problems has garnered significant attention due to its importance across various applications, particularly driven by the demand for efficient solutions to complex engineering challenges. Numerous metaheuristic algorithms inspired by animal behaviour or swarm intelligence have been proposed;however, these algorithms often pursue a multitude of strategies, resulting in excessive parameters that complicate tuning and hinder convergence and balance. Additionally, algorithms based on human behaviour remain scarce. To address these limitations, extensive research has been conducted on the interplay between social memory and individual memory, leading to the introduction of a novel human-behaviour-inspired metaheuristic algorithm, named Advanced Social Memory Optimization (ASMO). This algorithm seeks to address the complexities of parameter management, convergence, and balance more effectively with a streamlined set of strategies. Furthermore, a mathematical model based on the mechanisms of social memory formation and individual memory updating underpins the algorithm. Rigorous performance evaluations, utilizing the Wilcoxon Rank-Sum Test and the Friedman Test across multiple benchmark suites (CEC2017, CEC2019, and CEC2022), demonstrate that ASMO, with only two algorithmic strategies, outperforms or matches established algorithms on more than half of the test functions. These findings suggest promising new avenues for research in the field of optimization and, given the succinctness of ASMO's strategies, underscore its potential as a powerful tool for enhancing and developing solutions to complex engineering design problems. The code for the ASMO algorithm is available in appendix D.

关键词： advanced social memory optimization social memory individual memory engineering applications metaheuristic algorithm

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A metaheuristic algorithm for efficient aircraft sequencing and scheduling in terminal maneuvering areas

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OPTIMIZATION LETTERS 2025年第3期19卷 579-604页

作者： Gui, Dongdong Le, Meilong Luo, Xiaodong Huang, Zhouchun Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China

Air traffic controllers face a daily challenge ensuring the safe and orderly flow of aircraft in terminal maneuvering areas, known academically as the Aircraft Sequencing and Scheduling Problem (ASSP). Typically formulated as a mixed integer linear programming problem, the strong NP-hard nature of the ASSP necessitates the use of heuristics or metaheuristics for practical, time-efficient solutions. In this paper, we propose a new metaheuristic algorithm to address the computational complexity of the problem. The algorithm employs a single-solution-based search methodology that starts from a well-constructed initial solution and iteratively explores a fix-and-optimize procedure to find an improved solution. Specifically, this procedure solves a variant of the ASSP that contains all the continuous variables and a selected subset of binary variables of the original problem, with the remaining binary variables being temporarily fixed to the values determined based on previous iterations. Additionally, the algorithm incorporates tabu search to increase the possibility of quickly finding optimal solutions. A rolling horizon control approach further accelerates the solution derivation process by decomposing the original problem into smaller subproblems. Our computational experiments using real traffic data from Guangzhou Baiyun International Airport demonstrates the effectiveness of the algorithm in terms of solution quality and computational time.

关键词： Aircraft sequencing and scheduling metaheuristic algorithm Mixed integer linear programming Fix-and-optimize

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Virus diffusion algorithm: a novel metaheuristic algorithm for segmenting the spun crack

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第4期36卷 045601-045601页

作者： Wan, Miao Lin, Yong-Cheng Chen, Ming-Song Zeng, Ning-Fu Wu, Gui-Cheng Zhang, Hui-Jie Cent South Univ Sch Mech & Elect Engn Changsha 410083 Peoples R China State Key Lab Precis Mfg Extreme Serv Performance Changsha 410083 Peoples R China Rongcheng Huadong Met Forming Machinery Co Ltd Rongcheng 264300 Peoples R China

metaheuristic algorithms are extensively utilized in engineering due to their outstanding capacity for solving optimization problems with restricted computing resources or incomplete data. However, its extended use is constrained by the low optimization accuracy and premature convergence. The rapid spread and extensive reach of the COVID-19 virus have inspired the proposal of a new virus diffusion algorithm (VDA) to overcome the limitations of the metaheuristic algorithm. This article utilizes the VDA algorithm to segment spun cracks, providing a method for intelligent detection of spinning process. The algorithm integrates global diffusion and local diffusion mechanisms to simulate both the random walk and local disturbance modes of virus diffusion, thereby enhancing its accuracy. Additionally, it introduces the competition mechanism and infection center rate to enhance the diversity of the population and expand the algorithm's search range. The effectiveness and robustness of the VDA algorithm is validated using the CEC'17 test benchmark function. Subsequently, the VDA algorithm is used to segment images with cracks in thin-walled spun parts. The experimentally obtained results illustrate that the VDA-based segmentation algorithm attains a PSNR of 23.6798 and an SSIM of 0.9864 for crack images, surpassing other segmentation algorithms in challenging conditions.

关键词： metaheuristic algorithm machine vision spinning detection image segmentation intelligent monitoring

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From constraints fusion to manifold optimization: A new directional transport manifold metaheuristic algorithm

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INFORMATION FUSION 2025年 113卷

作者： Snasel, Vaclav Kong, Lingping Das, Swagatam VSB Tech Univ Ostrava Dept Comp Sci Listpodu 17 Ostrava Czech Republic Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

The ascent of geometry-based models and methodologies, exemplified by geometric deep learning and manifold numerical optimization algorithms, has inaugurated a novel domain across various applications that grapple with geometric data complexities, such as electroencephalogram signals represented by symmetric positive definite matrix manifold, hierarchical data represented by hyperbolic manifold. The imperative fuels this inevitable paradigm shift to encapsulate the intricacies and richness inherent in data, areas where traditional methods prove inadequate. While metaheuristic algorithms are renowned for their versatile adaptability across applications, offering practical solutions within reasonable timeframes. However, the conventional metaheuristic algorithms fail on manifold applications with meaningless solutions. From an extrinsic optimization perspective, we treat manifold optimization problems as general optimization problems with multiple fused constraints that limit the optimization path to the manifold. This study pioneered the proposal and implementation of a metaheuristic manifold optimization, introducing a novel directional transport operator to rectify previously identified issues. Through experimentation across five sets of 25 problems, comparing against five algorithms, including both gradient-free and gradient-dominant counterparts, our proposed algorithm emerges as the optimal performer within the gradient-free category, demonstrating competitiveness even against gradient-dominant algorithms. Furthermore, we applied the proposed algorithm to the robot dynamic manipulation problem, achieving a close-optimal solution that eludes gradient-dominant approaches. This paper delves into the inherent capabilities and establishes the generalization of a metaheuristic algorithm within non-Euclidean functional landscapes. The source code will be available at https://***/lingpingfuzzy/metaheuristic-manifold-optimization.

关键词： Optimization on manifolds Matrix manifolds metaheuristic algorithm Extrinsic manifold Intrinsic manifold

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Seismic Optimization of Steel Mega-Braced Frame With Improved Prairie Dog metaheuristic Optimization algorithm

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2025年第3期34卷

作者： Payamifar, Tohid Sojoudizadeh, Reza Azizian, Hadi Rahimi, Loghman Islamic Azad Univ Dept Civil Engn Mahabad Branch Mahabad Iran

A mega-braced frame is one of the well-known structural systems used in tall buildings to resist lateral forces, which is composed of networks of diagonal braces and structural elements strategically placed within the building's frame to enhance its stiffness and stability. Determining the optimal angle of mega bracing in buildings presents challenges due to complex structural considerations, such as lateral load distribution, ensuring compatibility with building geometry and layout, and optimizing the overall structural efficiency while maintaining seismic integrity. For this purpose, a novel approach is proposed in this study for seismic optimization of steel mega-braced frames by employing an enhanced version of the prairie dog optimization (PDO) algorithm as one of the recently proposed metaheuristic algorithms. The new improved version of this algorithm called I-PDO is developed based the Levy flight concept, whereas the conventional Brownian randomization is replaced by Levy flight in the main search loop of the algorithm. For numerical investigations, a 10-story, 1-bay, and a 24-story, 5-bay, frame structures are considered. An optimization problem is developed based on the topology optimization of the mega bracing system, whereas a size optimization is also conducted for optimal determination of the design sections for structural elements. For comparative investigations, some of the well-known metaheuristics are also used for performance evaluation of the I-PDO in dealing with the structural optimization problems. The results demonstrate the capability of the I-PDO in providing better optimization results in dealing with the topology and size optimization of the mega-braced frame structures.

关键词： improved prairie dog optimization mega-braced frame structure metaheuristic algorithm seismic optimization

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metaheuristic algorithm-Based Optimal Energy Operation Scheduling and Energy System Sizing Scheme for PV-ESS Integrated Systems in South Korea

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INTERNATIONAL JOURNAL OF ENERGY RESEARCH 2024年第1期2024卷

作者： Park, Sungwoo Oh, Jinyeong Hwang, Eenjun Korea Univ Sch Elect & Elect Engn Seoul South Korea

To efficiently utilize the power generated by a photovoltaic (PV) system, integrating it with an energy storage system (ESS) is essential. Furthermore, maximizing the economic benefits of such PV-ESS integrated systems requires selecting the optimal capacity and performing optimal energy operation scheduling. Although many studies rely on rule-based energy operation scheduling, these methods prove inadequate for complex real-world scenarios. Moreover, they often focus solely on determining the ESS capacity to integrate into existing PV systems, thereby limiting the possibility of achieving optimal economic benefits. To address this issue, we propose an optimal energy operation scheduling and system sizing scheme for a PV-ESS integrated system based on metaheuristic algorithms. The proposed scheme employs a zero-shot PV power forecasting model to estimate the potential power generation from a planned PV system. A systematic analysis of the installation, operation, and maintenance costs is then incorporated into the economic analysis. We conducted extensive experiments for comparing economic benefits of various scheduling methods and capacities using real electrical load data collected from a private university in South Korea and estimated PV power data. According to the results, the most effective metaheuristic algorithm for scheduling is simulated annealing (SA). Additionally, the optimal PV system, battery, and power conversion system capacities for the university are 13,000 kW each, 10% of the PV system capacity, and 60% of the battery capacity, respectively. The estimated annual electricity tariff calculated from the data used in the experiment is $3,315,484. In contrast, SA-based scheduling in the optimal PV-ESS integrated system achieved annual economic benefits of $875,000, an improvement of approximately 7% over rule-based scheduling of $817,730.

关键词： energy operation scheduling energy storage system energy system sizing metaheuristic algorithm photovoltaic system zero-shot photovoltaic power forecasting

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metaheuristic algorithm Similarity Analysis Based on Performance Metric Mapping of Fractional Ranking 9

Metaheuristic Algorithm Similarity Analysis Based on Perform...

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9th IEEE International Conference on Information and Automation for Sustainability (ICIAfS)

作者： Zhang, Yong-Wei Halgamuge, Saman K. Jiangsu Univ Sci & Technol Coll Elect & Informat Zhenjiang Jiangsu Peoples R China Australian Natl Univ Res Sch Engn Coll Engn & Comp Sci Canberra ACT Australia Univ Melbourne Dept Mech Engn Sch Elect Mech & Infrastruct Engn Parkville Vic Australia

ISBN: (纸本)9781538694183

Although the diversity of metaheuristic algorithms has been frequently highlighted, the similarity of these algorithms is not studied comprehensively. This work studies the similarity of metahruristic algorithms from their performance perspective captured in a newly proposed fractional ranking method, which can map comprehensive performance measures into a scalar framework. The fractional ranking data is clustered using a k-medoids clustering to find similarities between algorithms. Results show that the proposed similarity analysis scheme reveals a new perspective of metaheuristic algorithms.

关键词： metaheuristic algorithm algorithm similarity clustering fractional ranking evolutionary algorithm algorithm evaluation algorithm ranking distance measure clustering validity

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metaheuristic algorithm of Multi-passengers Routing Path for Ride-Sharing Vehicle 1

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4th International Conference on Internet of Vehicles (IOV)

作者： Chien, Wei-Che Cho, Hsin-Hung Chang, Yao-Chung Lai, Chin-Feng Chao, Han-Chieh Natl Cheng Kung Univ Dept Engn Sci Tainan Taiwan Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan Taiwan Natl Taitung Univ Dept Comp Sci & Informat Engn Taitung Taiwan Natl Dong Hwa Univ Dept Elect Engn Hualien Taiwan

ISBN: (数字)9783319723297

ISBN: (纸本)9783319723297;9783319723280

With the standard of medical treatment raised up, the number of population is increased. People need to the extent new space for living. The convenient traffic is part of people life. Internet of things (IoT) promotes the development of riding-sharing which solving traffic blocked problem and made transportation flexible. This paper focuses on multi-people path planning by sharing vehicle problem to have more convenient traffic. In the current, public transports have fixed route so that passengers need to wait for the bus at the bus stop. The route path always cannot satisfy the request of customers. This paper adopts novelty concept of the ride-sharing to solve multi-path and passenger schedule planning problem to have highly flexible traffic method. We considering the waiting time of passenger and short path problem, and then propose two algorithms based on metaheuristic algorithm. Simulation represents that proposed method can design the best-shared vehicle path for different environments.

关键词： metaheuristic algorithm Multi-passengers routing path Path planning Ride-sharing vehicle

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Efficient routing for multiple AGVs in container terminals using hybrid deep learning and metaheuristic algorithm

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Ain Shams Engineering Journal 2025年第8期16卷

作者： Bui Minh Hau Sam-Sang You Le Ngoc Bao Long Hwan-Seong Kim Department of Convergence Interdisciplinary Education of Maritime & Ocean Contents (Logistics System) Korea Maritime and Ocean University 727 Taejong-ro Yeongdo-gu Busan 49112 Republic of Korea Division of Mechanical Engineering Korea Maritime and Ocean University 727 Taejong-ro Yeongdo-gu Busan 49112 Republic of Korea Northeast-Asia Shipping and Port Logistics Research Center Korea Maritime and Ocean University 727 Taejong-ro Yeongdo-gu Busan 49112 Republic of Korea Department of Logistics Korea Maritime and Ocean University 727 Taejong-ro Yeongdo-gu Busan 49112 Republic of Korea

This study aims to present the efficient routing of multiple agent systems by optimizing the automated guided vehicle (AGV) movement, waiting time, or container-lifting actions, providing optimal routing solutions in automated container terminals (ACTs). Through integrated scheduling, the AGV agent can determine the efficient route to the container, then pick it up and transport it to the final destination. Users can provide the starting position of the AGVs, container position, and drop-down location. The algorithm returns action lists for the AGV to perform. AGV route is implemented based on mapping the action in the action lists and the layout, preventing collisions and deadlocks among AGVs. We utilized the advantage actor-critic (A2C) reinforcement learning method combined with the ant colony optimization (ACO) of a swarm intelligence algorithm to solve the optimal routing problem in AGV-based ACTs. More specifically, this study presents the optimal action strategy that ACO-A2C finds for each AGV and a route scheme that each AGV can travel without colliding with other AGVs and obstacles. This novel method can potentially improve ACTs’ equipment utilization for efficient and competitive management.

关键词： AGV Container terminal Route planning Deep reinforcement learning metaheuristic algorithm

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Review of Nature Inspired metaheuristic algorithm Selection for Combinatorial t-Way Testing

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IEEE ACCESS 2022年 10卷 27404-27431页

作者： Muazu, Aminu Aminu Hashim, Ahmad Sobri Sarlan, Aliza Univ Teknol PETRONAS Fac Sci & Informat Technol Comp & Informat Sci Dept Seri Iskandar 32610 Perak Malaysia Umaru Musa Yaradua Univ Fac Nat & Appl Sci Comp Sci Dept Katsina 820102 Nigeria

The metaheuristic algorithm is a very important area of research that continuously improves in solving optimization problems. Nature-inspired is one of the metaheuristic algorithm classifications that has grown in popularity among researchers over the last few decades. Nature-inspired metaheuristic algorithms contribute significantly to tackling many standing complex problems (such as the combinatorial t-way testing problem) and achieving optimal results. One challenge in this area is the combinatorial explosion problem, which is always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation has been selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-Time Hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization, whilst others were not. This research paper outlines a hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021), such as the Coronavirus Optimization algorithm, Ebola Optimization algorithm, Harmony Search, Tiki-Taka algorithm, and so on. The purpose of this review is to revisit and carry out an up-to-date review of these distinguished algorithms with their respective current states of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics have a simple structure that can be adopted in different areas to become more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters settings to achieve an optimal solution.

关键词： metaheuristics Testing Heuristic algorithms Explosions Software algorithms Licenses Genetic algorithms metaheuristic algorithm nature inspired combinatorial t-way testing test case optimization

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