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Addressing a Collaborative Maintenance Planning Using Multiple Operators by a Multi-Objective Metaheuristic algorithm

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 606-618页

作者： Tian, Guangdong Zhang, Lele Fathollahi-Fard, Amir M. Kang, Qi Li, Zhiwu Wong, Kuan Yew Beijing Univ Civil Engn & Architecture Sch Mech Elect & Vehicle Engn Beijing 100044 Peoples R China Minist Transport Transport Management Inst Beijing 101601 Peoples R China Univ Victoria Peter B Gustavson Sch Business Victoria BC V8P5C2 Canada Univ Teknol Malaysia Fac Mech Engn Skudai 81310 Malaysia Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Xidian Univ Sch Elect Mech Engn Xian 710071 Peoples R China Univ Teknol Malaysia Fac Mech Engn Skudai 81310 Malaysia

Selective maintenance has a significant impact on the sustainable management of maintenance operations. The collaboration of multiple maintenance teams/operators is helpful to achieve sustainability for selective maintenance sequence planning. For products with a large number of components, a single maintenance team/operator is inefficient due to a long completion time which is not acceptable for emergency planning. Providing specific and efficient maintenance sequence planning is critical to effectively handle different types of emergencies (e.g., wartime) while avoiding vague task assignments to multiple maintenance teams/operators. For scheduling many maintenance jobs while improving the efficiency and quality of maintenance operations, this study proposes a collaborative maintenance planning based on the concept of imperfect maintenance. In this regard, this study develops a multi-objective optimization model to optimize parallel maintenance sequences considering maintenance profit, maintenance cost, maintenance team, and resource limitations. We show the feasibility of the proposed multi-objective optimization model through a real case of maintenance practice for the components of an assistor device. For analyzing the complexity of the proposed maintenance sequence planning problem, this study introduces a new multi-objective metaheuristic algorithm which is an enhanced multi-objective gravitational search algorithm (EMOGSA) to find high-quality Pareto solutions for the proposed problem. Different multi-objective evaluation metrics are used to study the performance of the proposed algorithm. From the results, the proposed model and developed solution algorithm can help maintenance decision-makers to determine complex maintenance planning. Note to Practitioners-This paper deals product with a maintenance and proposes gravitational search algorithm based on only maintenance task, which maintenance task. The goal of this paper is to analyze the maintenance problem

关键词： Selective maintenance parallel sequences gravitational search algorithm heuristic search algorithms maintenance planning

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Efficient gravitational search algorithm for optimum design of retaining walls

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STRUCTURAL ENGINEERING AND MECHANICS 2013年第1期45卷 111-127页

作者： Khajehzadeh, Mohammad Taha, Mohd Raihan Eslami, Mahdiyeh Natl Univ Malaysia Dept Civil & Struct Engn Bangi Selangor Malaysia Islamic Azad Univ Dept Elect Engn Sci & Res Branch Kerman Iran

In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and CO2 emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.

关键词： retaining wall minimum weight minimum cost minimum CO2 emissions gravitational search algorithm

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Optimization of cutting conditions for end milling of Ti6Al4V Alloy by using a gravitational search algorithm (GSA)

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MECCANICA 2013年第7期48卷 1701-1715页

作者： Al-Zubaidi, Salah Ghani, Jaharah A. Haron, Che Hassan Che Univ Kebangsaan Malaysia Dept Mech & Mat Engn Fac Engn & Built Environm Bangi 43600 Selangor Malaysia

Surface roughness is commonly used to indicate the quality of machine parts. Optimizing cutting parameters throughout the machining process is an important aspect for manufacturers, as it allows them to achieve a minimum surface value. During this study, a new optimization technique known as the gravitational search algorithm (GSA) was employed in order to achieve minimum surface roughness when end milling a Ti6Al4V alloy under dry cutting conditions, with both PVD coated and uncoated cutting tools. Regression models have been created based on the results of real experimental data. Through use of SPSS software, it was possible to formulate the objective (fitness) functions which were used in the GSA optimization for each cutting tool. A MATLAB code was then created to instigate the optimization process. The results indicated that high cutting speed and low feed rate and depth of cut could result in a minimum surface roughness value of (0.6255 mu m), based on the objective function for the PVD cutting tool. Alternatively, surface roughness of around (0.4165 mu m) could be achieved by using an uncoated tool on a lower feed rate, depth of cut and cutting speed. The same GSA technique was used in another case study optimized by Genetic algorithm (GA). The GSA achieved the same results, and proved that it is faster than GA: GSA could reach the optimum solution in the third iteration;GA could only reach it in the 67th.

关键词： Optimization Ti6Al4V alloy gravitational search algorithm Surface roughness

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Combining Genetic and gravitational search algorithms for the Optimal Management of Battery Energy Storage Systems in Real-Time Pricing Markets

Combining Genetic and Gravitational Search Algorithms for th...

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IEEE-Industry-Applications-Society Annual Meeting

作者： Lujano-Rojas, Juan M. Yusta, Jose M. Dominguez-Navarro, Jose A. Osorio, Gerardo J. Shafie-khah, Miadreza Wang, Fei Catalao, Joao P. S. Univ Zaragoza Zaragoza Spain Univ Beira Interior C MAST Covilha Portugal Univ Vaasa Sch Technol & Innovat Vaasa 65200 Finland North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China Univ Porto Fac Engn Porto Portugal INESC TEC Porto Portugal

ISBN: (纸本)9781728171920

Determining the optimal management in terms of operative decisions (charging, discharging, or disconnection) as well as their magnitudes (charging/discharging current/power), considering the nonlinearities of battery energy storage system (BESS) is a crucial process on the successful acceptance of energy storage technologies. This work presents an optimization model for the management of BESS operating in real-time electricity markets in order to maximize the economic profits by energy arbitrage. The optimization model proposed combines genetic algorithm (GA) with gravitational search algorithm (GSA). On one hand, GA uses an integer codification, where charging, discharging, and disconnection are represented. On the other hand, GSA optimizes the maximum charging or discharging energy. The proposed combination of optimization algorithms allows determining the integer and continuous variables involved in the management problem, taking into account the nonlinear behavior of BESS. The proposed approach was implemented considering lead acid and vanadium redox flow batteries under the conditions of Spanish electricity market.

关键词： Battery energy storage system Genetic algorithm gravitational search algorithm Lead acid battery Vanadium redox flow battery

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Energy-Efficient Hybrid Adaptive Clustering for Dynamic MANETs

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IEEE ACCESS 2025年 13卷 51319-51331页

作者： Yilmaz, Kudret Kara, Resul Katircioglu, Ferzan Duzce Univ Duzce Vocat Sch Dept Comp Technol TR-81620 Duzce Turkiye Duzce Univ Dept Comp Engn TR-81620 Duzce Turkiye Duzce Univ Dept Mechatron Engn TR-81620 Duzce Turkiye

Mobile ad hoc network (MANET) is a wireless, mobile node network in which the nodes move randomly and operate without centralized management. In MANETs, the network structure increases the energy consumption of the nodes, which shortens the network lifetime and affects packet transmission. The process of clustering in MANETs (Mobile Ad-hoc Network) can be achieved through the division of the network into virtual groups, known as clusters. The Cluster Head (CH) of each cluster is in charge of data transmission within the cluster. In this study, a two-stage Hybrid Adaptive Clustering algorithm for Dynamics MANETs (HACADM) is proposed to improve the network performance in MANETs. In the first stage, based on the Weighted Clustering algorithm (WCA) for selecting optimal CHs, criteria such as node degree, neighborhood distance, power of battery and mobility are optimized using the Gravity search algorithm (GSA). In the second phase, the clustering is executed by identifying the member nodes and their roles of the selected CHs using the Enhanced Density Based Spatial Clustering of Applications with Noise (Enhanced-DBSCAN) algorithm, which is one of the unsupervised learning methods. Moreover, this approach serves to reduce the load on the CHs and enhance the stability of the cluster by selecting gateway nodes for inter-cluster communication. This study represents a significant step towards optimizing energy efficiency and extending network lifetime by enhancing the adaptability of clustering processes in MANETs under dynamic network conditions. The proposed HACADM method has the potential to enhance the performance of MANETs by ensuring a more balanced load distribution compared to existing clustering approaches. The HACADM method was compared with the EE-WCA, E-MAVMMF, TSDR and MORS-ASO methods using critical performance metrics such as remaining energy, end-to-end delay, packet delivery ratio and throughput. For example, experimental results on remaining energy show tha

关键词： Ad hoc networks Mobile computing Clustering algorithms Energy efficiency Energy consumption Routing Heuristic algorithms Routing protocols Mobile nodes Particle swarm optimization Adaptive clustering energy efficiency gravitational search algorithm machine learning MANET

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Collaborative planning of electric vehicle integrated charging and swapping stations and distribution network for carbon emission reduction

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ENERGY REPORTS 2024年 12卷 5846-5862页

作者： Liu, Wenjin Xin, Shijin Zhang, Zhaoxu Fan, Changhui Hao, Guojie Xu, Qing State Grid Gansu Elect Power Co Baiyin Power Supply Co Baiyin 730900 Peoples R China

Electric vehicle (EV) integration into the distribution network (DN) is a significant measure for reducing carbon dioxide emissions, yet existing studies have not fully explored the carbon reduction potential of the EV. This article proposes an innovative collaborative planning framework for electric vehicle integrated charging and swapping stations (EVICSS) and DN for carbon emission reduction. Initially, the division of responsibilities and mechanism analysis for emission reduction in DN are presented. Subsequently, the ladder carbon price model is presented to incentivize carbon mitigation efforts. And, considering the architecture and functioning mode of the EVICSS, the operational model for EVICSS equipment is established to deliver high-quality services to EV users, taking into account fast charging stations (FCS), fast battery swapping stations (FBSS), and cascade energy storage system (CESS) comprehensively. Taking into account the economic implications of carbon trading, a collaborative planning strategy for EVICSS and the DN is formulated to minimize overall costs while maximizing carbon emission reductions. Then an modified integrated gravitational search algorithm and particle swarm optimization algorithm (MIGSA-PSOA) is customizedly developed to solve this complicated optimization problem. Finally, case studies based a real DN are conducted to illustrate the effectiveness of the collaborative planning model. The results demonstrated it can effectively respond to DN power regulation and realize carbon emission reduction. Compared with independent planning, collaborative planning can increase the planning cost and total cost to 15.6% and 33.1% respectively.

关键词： Electric vehicle Collaborative planning Electric vehicle integrated charging and swapping stations Low carbon gravitational search algorithm Particle swarm optimization algorithm

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Optimal static state estimation using improved particle swarm optimization and gravitational search algorithm

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期52卷 254-265页

作者： Mallick, Sourav Ghoshal, S. P. Acharjee, P. Thakur, S. S. Natl Inst Technol Dept Elect Engn Durgapur 713209 India

In this paper, two novel evolutionary search techniques based on Improved Particle Swarm Optimization (IPSO) algorithm and gravitational search algorithm (GSA), have been proposed to solve the static State Estimation (SE) problem as an optimization problem. The proposed methods are tested on five IEEE standard test systems along with two ill-conditioned test systems under different simulated conditions and the results are compared with the same of standard Weighted Least Square State Estimation (WLS-SE) technique, Particle Swarm Optimization (PSO) based SE and Hybrid Particle Swarm Optimization gravitational search algorithm (PSOGSA) based SE technique. The optimization performance and the statistical error analysis show the superiority of the proposed GSA based SE technique over the other two techniques. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Static state estimation Improved particle swarm optimization gravitational search algorithm Ill-conditioned system

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Strategic bidding using fuzzy adaptive gravitational search algorithm in a pool based electricity market

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APPLIED SOFT COMPUTING 2013年第5期13卷 2445-2455页

作者： Kumar, J. Vijaya Kumar, D. M. Vinod Edukondalu, K. Natl Inst Technol Dept Elect Engn Warangal 506004 Andhra Pradesh India

A novel stochastic optimization approach to solve optimal bidding strategy problem in a pool based electricity market using fuzzy adaptive gravitational search algorithm (FAGSA) is presented. Generating companies (suppliers) participate in the bidding process in order to maximize their profits in an electricity market. Each supplier will bid strategically for choosing the bidding coefficients to counter the competitors bidding strategy. The gravitational search algorithm (GSA) is tedious to solve the optimal bidding strategy problem because, the optimum selection of gravitational constant (G). To overcome this problem, FAGSA is applied for the first time to tune the gravitational constant using fuzzy "IF/THEN" rules. The fuzzy rule-based systems are natural candidates to design gravitational constant, because they provide a way to develop decision mechanism based on specific nature of search regions, transitions between their boundaries and completely dependent on the problem. The proposed method is tested on IEEE 30-bus system and 75-bus Indian practical system and compared with GSA, particle swarm optimization (PSO) and genetic algorithm (GA). The results show that, fuzzification of the gravitational constant, improve search behavior, solution quality and reduced computational time compared against standard constant parameter algorithms. (C) 2012 Elsevier B. V. All rights reserved.

关键词： Electricity market Bidding strategies Market clearing price (MCP) Fuzzy inference gravitational search algorithm

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Hybridization of the gravitational search algorithm and Big Bang-Big Crunch algorithm for Data Clustering

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FUNDAMENTA INFORMATICAE 2013年第4期126卷 319-333页

作者： Hatamlou, Abdolreza Hatamlou, Masoumeh Islamic Azad Univ Khoy Branch Tehran Iran

Clustering is a very important technique in knowledge discovery. It has been widely used in data mining, image processing, machine learning, bioinformatics, marketing and other fields. Clustering discern the objects into groups called clusters, based on certain criteria. The similarity of objects is high within the clusters, but low between the clusters. In this work, we investigate a hybridization of the gravitational search algorithm (GSA) and big bang-big crunch algorithm (BB-BC) on data clustering. In the proposed approach, namely GSA-BB, GSA is used to explore the search space for finding the optimal locations of the clusters centroids. Whenever GSA loses its exploration, BB-BC algorithm is used to diversify the population. The performance of the proposed method is compared with GSA, BB-BC and K-means algorithms using six standard and real datasets taken from the UCI machine learning repository. Experimental results indicate that there is significant improvement in the quality of the clusters obtained by the proposed hybrid method over the non-hybrid methods.

关键词： Data Clustering gravitational search algorithm Big Bang-Big Crunch algorithm

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Orthogonal array based performance improvement in the gravitational search algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第1期21卷 174-185页

作者： Altinoz, Okkes Tolga Yilmaz, Asim Egemen Weber, Gerhard Wilhelm Hacettepe Univ Bala Vocat Sch Dept Elect Technol Ankara Turkey Ankara Univ Dept Elect & Elect Engn TR-06100 Ankara Turkey Middle E Tech Univ Inst Appl Math TR-06531 Ankara Turkey

The gravitational search algorithm (GSA) is a novel heuristic method inspired by Newton's gravity and velocity equations. In addition, it is a population-based algorithm, in which each member (called an agent) in the population has a mass, velocity, and acceleration. Beginning with the first population state, agents influence each other via mass and velocity relations. This mutual effect causes agents to reach the optimum. Hence, the performance of the GSA to attain the optimum is related to the initial population formation, like other population-based algorithms. In this study, the orthogonal array (OA) concept is applied and injected to the GSA algorithm in the initialization phase. Hence, the GSA benefits from the homogenized agent distribution tendency of the OA. The implementation results are utilized to compare the conventional and proposed methods (i.e. conventional GSA and the so-called "OA-GSA"), and the efficiency of the proposed method is demonstrated.

关键词： Benchmark functions gravity based search gravitational search algorithm orthogonal array Taguchi method

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