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Multi-scale offshore wind farm site selection decision framework based on GIS, MCDM and meta-heuristic algorithm

OCEAN ENGINEERING 2025年 316卷

作者： Shao, Meng Mao, Zhimou Sun, Jinwei Guan, Xiao Shao, Zhuxiao Tang, Tao Ocean Univ China Coll Engn Qingdao 266520 Peoples R China

Offshore wind energy has emerged as a significant driving force to establish a sustainable energy system. Nevertheless, the field of wind farm site selection faces challenges regarding the lack of coherent scale of site selection and incomplete consideration of methodologies. To address these issues, this study proposes a multiscale framework and methodology, which combines Geographic Information System (GIS), Multi-Criteria Decision Making (MCDM), and meta-heuristic algorithms. The goal of this study is to determine the optimal scheme, its location and boundary, by integrating large, small, and micro-scale site selection. Within this framework, a combined weighting method based on Group Intuitionistic Fuzzy Analytic Hierarchy Process (GIAHP) and the Method based on the Removal Effects of Criteria (MEREC) is proposed to calculate the criteria weights. Additionally, the improved ORESTE method based on Cumulative Prospect Theory (CPT) is employed to rank the alternatives. Finally, the improved Variable Neighborhood Search (VNS) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms are implemented to design the offshore wind farm and determine its location and boundary. The proposed framework is then applied in the Shandong sea area in China. The results, obtained through sensitivity analysis and comparative analysis, can serve as a foundation for decision makers.

关键词： Offshore wind energy Multi-scale site selection GIS MCDM meta-heuristic algorithm

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A hierarchical registration method for optical surface measurement based on a meta-heuristic algorithm

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第4期36卷 045008-045008页

作者： Cheng, Bohan Guo, Yanjun Yang, Xiaojing Qin, Yafei Yao, Tong Miao, Wenhua Lou, Fang Kunming Univ Sci & Technol Fac Mech & Elect Engn Kunming 650500 Peoples R China

Optical surface measurement and characterization precision are significantly influenced by the performance of surface registration methods. Most existing optical surface registration methods suffer from poor efficiency or lower precision. To address these challenges, this investigation proposes a hierarchical registration method based on a meta-heuristic algorithm, primarily targeting freeform and grid surfaces (relatively simple non-rotationally symmetric surfaces) and rotationally symmetric surfaces in optical applications. First, to enhance efficiency and avoid unnecessary registration, the importance of six degrees of freedom (DOFs) is evaluated and classified into four categories. The registration process then follows the importance level of the DOFs, prioritizing those of higher significance. The deviation value is obtained by considering the last registration result as a constraint condition and employing a meta-heuristic algorithm to minimize the fit function. Simulation and experimental results demonstrate that the proposed method achieves lower average, minimum, and standard deviation values compared to other registration methods. Notably, when the population size and the number of iterations are reduced, the standard deviation decreases by over 99% compared to conventional methods. The proposed method exhibits significant advantages in both precision and efficiency, and its practicality has been validated through experiments on freeform, aspheric, and sinusoidal grid surfaces.

关键词： surface registration meta-heuristic algorithm hierarchical registration optical surface

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Security-aware resource allocation in fog computing using a meta-heuristic algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第2期28卷 1-18页

作者： Mohammadi, Mina Bahranipour, Fatemeh Mood, Sepehr Ebrahimi Farshi, Mohammad Yazd Univ Dept Comp Sci Daneshgah Blvd Yazd *** Iran

In recent years, the number of smart devices and wireless data transmissions has increased worldwide. These emerging applications and services require not only extensive computing capabilities and high battery power, but also elevated data transmissions as well. Nonetheless, the computing capacity of this equipment is constrained, resulting in significant consequences on the performance and operating costs of services in 5th-generation wireless networks. Recent advantages of Fog computing have increased the use of this model to fulfill above requirements in the IoT context. A new Fog computing network model has been proposed in order to address these issues by providing cloud computing services at the network's edge. In Fog computing, mobile devices are not required to offload all their tasks to remote and central servers. However, since other users are exposed to offloaded tasks, they are vulnerable to malicious attacks and eavesdropping. In this paper, we investigate security-aware resource allocation in device-to-device based fog computing systems. In order to enhance task offloading, a novel multi-objective function is proposed to optimize delay and energy savings compared to local computing, as well as security breach costs. Several multi-objective meta-heuristic algorithms, such as Non-dominated Sorting Genetic algorithm II (NSGA-II), have been proposed over the past decade. Optimizing objectives, such as energy consumption and delay was the goal of using these algorithms. Subsequently, the improved NSGA-II algorithm is employed to solve the problem. In this algorithm, the Sigma Scaling, a technique used to adjust fitness values to maintain diversity in the population, is utilized to control selection pressure. By incorporating Sigma Scaling, the exploration and exploitation capabilities of the algorithm are effectively managed, enhancing its ability to escape local optima and prevent premature convergence. The performance of our modified NSGA-II is superior t

关键词： Non-dominated sorting genetic algorithm-II Security aware Device-to-device communication meta-heuristic algorithm

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Improving the interpretation of undrained shear strength from piezocone penetration tests by integrating soil physical properties using a hybrid meta-heuristic algorithm

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Journal of Rock Mechanics and Geotechnical Engineering 2025年

作者： Wu, Meng Zhao, Zening Cai, Guojun School of Earth Sciences and Engineering Hohai University Nanjing 210098 China Institute of Geotechnical Engineering Southeast University Nanjing 211189 China Institut für Geotechnik Universität für Bodenkultur Vienna 1180 Austria Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong China School of Civil Engineering Anhui Jianzhu University Hefei 230601 China

Conventional empirical equations for estimating undrained shear strength (su) from piezocone penetration test (CPTu) data, without incorporating soil physical properties, often lack the accuracy and robustness require... 详细信息

Conventional empirical equations for estimating undrained shear strength (s_u) from piezocone penetration test (CPTu) data, without incorporating soil physical properties, often lack the accuracy and robustness required for geotechnical site investigations. This study introduces a hybrid virus colony search (VCS) algorithm that integrates the standard VCS algorithm with a mutation-based search mechanism to develop high-performance XGBoost learning models to address this limitation. A dataset of 372 seismic CPTu and corresponding soil physical properties data from 26 geotechnical projects in Jiangsu Province, China, was collected for model development. Comparative evaluations demonstrate that the proposed hybrid VCS-XGBoost model exhibits superior performance compared to standard meta-heuristic algorithm-based XGBoost models. The results highlight that the consideration of soil physical properties significantly improves the predictive accuracy of s_u, emphasizing the importance of considering additional soil information beyond CPTu data for accurate s_u estimation. © 2025 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences

关键词： Extreme gradient boosting meta-heuristic algorithm Piezocone penetration test Undrained shear strength

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meta-heuristic algorithm for aircraft pre-tactical conflict resolution with altitude and heading angle change maneuvers

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TOP 2021年第3期29卷 629-647页

作者： Cecen, Ramazan Kursat Sarac, Tugba Cetek, Cem Eskisehir Osmangazi Univ Eskisehir Turkey Eskisehir Tech Univ Eskisehir Turkey

Aircraft conflict resolution is of great importance for the safe and efficient management of air traffic. This study proposes a two-step optimization approach for the aircraft conflict resolution problems within the pre-tactical time window in generic free route airspace. Safe separation between aircraft pairs is maintained using either altitude or heading angle change maneuvers in a pre-defined buffer zone within the boundaries of the airspace. The first step of the model aims to minimize the total number of conflicting aircraft and the total fuel consumption together using altitude change maneuvers. A mixed-integer linear programming model is proposed for the first step but, due to the high computational time, a metaheuristic algorithm (simulated annealing) was developed. If the altitude change does not resolve all conflicts in the first step, the proposed model implements heading angle change maneuvers with minimum extra fuel burn in the second step. A nonlinear programming model is presented for the second step. Numerical results show that the proposed approach can resolve all conflicts in less than 4 min for the highest traffic flow rate.

关键词： Aircraft conflict resolution Air traffic management meta-heuristic algorithm Non-linear programming model Mixed-integer linear programming model Free route airspace

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Development of a hybrid meta-heuristic algorithm for combinatorial optimisation and its application for optimal design of laminated composite cylindrical skirt

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COMPUTERS & STRUCTURES 2008年第7-8期86卷 796-815页

作者： Rao, A. Rama Mohan Shyju, P. P. Struct Engn Res Ctr Madras Tamil Nadu India

This paper presents a hybrid meta-heuristic algorithm called multiple start guided neighbourhood search (MSGNS) algorithm for combinatorial optimisation which combines the good features of popular guided local search algorithms like simulated annealing and tabu search. It has been organized as a multiple start algorithm to maintain a good balance between intensification and diversification. The proposed hybrid meta-heuristic algorithm has been employed to solve optimal stacking sequence design problem of laminate composite structures. First, the algorithm has been employed to solve the problem of optimal stacking sequence of a composite plate for which the results of various algorithms are available in the literature. This study is basically to validate and also to demonstrate the effectiveness of the proposed algorithm over several existing meta-heuristic algorithms. Later, a practical design example of fiber-reinforced composite cylindrical skirt of solid rocket motor of aerospace vehicle is investigated. A skirt is a potential element for weight reduction in rocket motors as it leads to reduction of the total weight of solid rocket motor. Due to its significance for solid rocket motors, it is proposed to optimise the weight as well as cost of the fiber-reinforced composite cylindrical skirt subjected to a buckling strength constraint and an overstressing strength constraint under aerodynamic torque and axial thrust. This is achieved by arriving at an optimal stacking sequence for the cylinder satisfying all the design constraints and also by employing multiple composite materials. Classical laminate theory combining with elastic stability theory of thin shells is used to arrive at buckling strength and overstressing strength of the fiber-reinforced composite cylindrical skirt. The Tsai-Wu failure criterion is employed to assess the first ply failure. Buckling strength and failure strength of the cylindrical skirt is described by using buckling load factor and ove

关键词： combinatorial optimisation laminate composites meta-heuristic algorithm tabu search simulated annealing cylindrical shell

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An energy efficient service composition mechanism using a hybrid meta-heuristic algorithm in a mobile cloud environment

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2020年 143卷 77-87页

作者： Ibrahim, Godar J. Rashid, Tarik A. Akinsolu, Mobayode O. Salahaddin Univ Erbil Dept Software & Informat Engn Erbil Iraq Univ Kurdistan Hewler Comp Sci & Engn Dept Erbil Iraq Wrexham Glyndwr Univ Fac Arts Sci & Technol Mold Rd Wrexham LL11 2AW Wales

By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy saving problem in mobile nodes. In order to mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping algorithm and Genetic algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Mobile cloud computing Service composition Energy consumption meta-heuristic algorithm

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Statistical tolerance-cost-service life optimization of blade bearing of controllable pitch propeller considering the marine environment conditions through meta-heuristic algorithm

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2022年第2期9卷 689-705页

作者： Zeng, Wenhui Yi, Jin Lin, Rongfu Lu, Wenlong Jianghan Univ Intelligent Mfg Coll Wuhan 430074 Hubei Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong 999077 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China

Blade bearing is the key mechanism of the controllable pitch propellers (CPPs) to adjust the pitch to change the thrust. In the marine environment, wear and deformation of the contact surface of the blade bearing are quite large, which may result in leakage to reduce the service life of CPP. However, most studies on tolerance optimization of blade bearing disregard deformations and wear, and it could result in unreasonable tolerances and impair the CPP's service life. In this paper, to establish the statistical tolerance-cost-service life optimization model of the blade bearing of the CPP considering the marine environment effects in propellers, a prediction model about the relationship between the thrust and rotation speed of the CPP considering environment conditions of wind, wave, and ocean currents is constructed. Based on this, the wear and deformation are simulated according to the predicted rotation speed in the marine environment. Considering the deformation and wear in the marine environment, a tolerance analysis model of the assembly deviations of the blade bearing is introduced, and the assembly characteristics and functional requirements are established and converted into probability constraints to establish the service-life model based on statistical tolerance analysis and Monte Carlo simulation methods. Afterward, a statistical tolerance-cost-service life optimization model is established and transformed into a single-objective optimization problem through the normalization method and the weighted sum method, and solved by a meta-heuristic algorithm named cuckoo search. Finally, the application of the proposed model is demonstrated through a case involving the blade bearing of a CPP, and the optimized results show that the manufacturing cost of the blade bearing is reduced and its service life is extended.

关键词： marine environment effects in propellers controllable pitch propeller statistical tolerance-cost-service life optimization meta-heuristic algorithm

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A new method for human resource allocation in cloud-based e-commerce using a meta-heuristic algorithm

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KYBERNETES 2022年第6期51卷 2109-2126页

作者： Al-Shourbaji, Ibrahim Zogaan, Waleed Jazan Univ Dept Comp & Network Engn Jazan Saudi Arabia Jazan Univ Dept Comp Sci Jazan Saudi Arabia

Purpose The human resource (HR) allocation problem is one of the critical dimensions of the project management process. Due to this nature of the problem, researchers are continually optimizing one or more critical scheduling and allocation challenges in different ways. This study aims to optimize two goals, increasing customer satisfaction and reducing costs using the imperialist competitive algorithm. Design/methodology/approach Cloud-based e-commerce applications are preferred to conventional systems because they can save money in many areas, including resource use, running expenses, capital costs, maintenance and operation costs. In web applications, its core functionality of performance enhancement and automated device recovery is important. HR knowledge, expertise and competencies are becoming increasingly valuable carriers for organizational competitive advantage. As a result, HR management is becoming more relevant, as it seeks to channel all of the workers' energy into meeting the organizational strategic objectives. The allocation of resources to maximize benefit or minimize cost is known as the resource allocation problem. Since discovering solutions in polynomial time is complicated, HR allocation in cloud-based e-commerce is an Nondeterministic Polynomial time (NP)-hard problem. In this paper, to promote the respective strengths and minimize the weaknesses, the imperialist competitive algorithm is suggested to solve these issues. The imperialist competitive algorithm is tested by comparing it to the literature's novel algorithms using a simulation. Findings Empirical outcomes have illustrated that the suggested hybrid method achieves higher performance in discovering the appropriate HR allocation than some modern techniques. Practical implications The paper presents a useful method for improving HR allocation methods. The MATLAB-based simulation results have indicated that costs and waiting time have been improved compared to other algorithms, which cau

关键词： Human resource Cloud computing Project management Allocation E-Commerce meta-heuristic algorithm

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Design and applications of an advanced hybrid meta-heuristic algorithm for optimization problems

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ARTIFICIAL INTELLIGENCE REVIEW 2021年第8期54卷 5931-6010页

作者： Parouha, Raghav Prasad Verma, Pooja Indira Gandhi Natl Tribal Univ Dept Math Amarkantak MP India

This paper designed an advanced hybrid algorithm (haDEPSO) to solve the optimization problems, based on multi-population approach. It integrated with suggested advanced DE (aDE) and PSO (aPSO). Where in aDE a novel mutation strategy and crossover probability along with the slightly changed selection scheme are introduced, to avoid premature convergence. And aPSO consists of the novel gradually varying inertia weight and acceleration coefficient parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. The algorithms efficiency is verified through 23 basic, 30 CEC 2014 and 30 CEC 2017 test suite and comparing the results with various state-of-the-art algorithms. The numerical, statistical and graphical analysis shows the effectiveness of these algorithms in terms of accuracy and convergence speed. Finally, three real world problems have been solved to confirm problem-solving capability of proposed algorithms. All these analyses confirm the superiority of the proposed algorithms over the compared algorithms.

关键词： Optimization meta-heuristic algorithm Particle swarm optimization Differential evolution Hybrid algorithm

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