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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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Identifying groundwater contaminant sources based on a KELM surrogate model together with four heuristic optimization algorithms

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ADVANCES IN WATER RESOURCES 2020年 138卷 103540-103540页

作者： Zhao, Ying Qu, Ruizhuo Xing, Zhenxiang Lu, Wenxi Northeast Agr Univ Sch Water Conservancy & Civil Engn Harbin 150030 Peoples R China Minist Agr China Key Lab High Efficiency Utilizat Agr Water Soil R Harbin 150030 Peoples R China Harbin Inst Technol Sch Environm Harbin 150090 Peoples R China Jilin Univ Coll New Energy & Environm Changchun 130021 Peoples R China

Identifying groundwater contaminant sources involves a reverse determination of the source characteristics by monitoring contaminant concentrations in a few observation wells. However, due to the ill-posed nature and high time consumption of identification, an efficient identification process with accurate estimated results is particularly important. To improve the efficiency of identifying groundwater contaminant sources, a kernel-based extreme learning machine was used as a surrogate for the time-consuming simulation model. Four heuristic search algorithms were used to improve the accuracy of the identification results. The proposed approach was tested in both hypothetical and actual cases. The conclusions are: 1. By forward and backward calculation of the surrogate model, the time cost of identifying groundwater sources can be reduced significantly;2. When a traditional genetic algorithm and a particle swarm optimization algorithm are combined with quantum computing, computational efficiency and accuracy are both improved;and 3. By using various search algorithms to identify unknown contaminant sources in the actual case, the range of release histories of each contaminant source can be obtained, decreasing the ill-posed nature of the identification result obtained by a single algorithm and improving the reliability of the identification results.

关键词： Groundwater contaminant heuristic search algorithms Identifying contaminant sources Kernel-based extreme learning machine

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Gravitational search algorithm with both attractive and repulsive forces

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SOFT COMPUTING 2019年第3期23卷 783-825页

作者： Zandevakili, Hamed Rashedi, Esmat Mahani, Ali Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran Grad Univ Adv Technol Dept Elect Engn Kerman Iran

The gravitational search algorithm (GSA) is a meta-heuristic optimization algorithm which is inspired by the gravity force. This algorithm uses Newton's gravity and motion laws to calculate the masses interactions and shows high performance in solving optimization problems. The premature convergence is the common drawback of heuristic search algorithms in high-dimensional problems, and GSA is not an exception. In this paper, a new version of GSA is proposed to improve the power of GSA in exploration and exploitation. The proposed algorithm has both attractive and repulsive forces. In this algorithm, the heavy particles attract some particles and repulse some others, in which the forces are inversely proportional to their distances. For better evaluation, the GSA with both attractive and repulsive forces (AR-GSA) is tested using CEC 2013 benchmark functions and the results are compared with some well-known meta-heuristic algorithms. The simulation results show that AR-GSA can improve the convergence rate, the exploration, and the exploitation capabilities of GSA.

关键词： Optimization heuristic search algorithms Gravitational search algorithm Centripetal force

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Summarization of legal judgments using gravitational search algorithm

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NEURAL COMPUTING & APPLICATIONS 2019年第12期31卷 8631-8639页

作者： Kanapala, Ambedkar Jannu, Srikanth Pamula, Rajendra Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad Bihar India Vaagdevi Engn Coll Dept Comp Sci & Engn Warangal Andhra Pradesh India

Text summarization is an extraction of important text from the original document. The objective of any automatic text summarization system, especially in legal domain, is to produce a summary which is close to human-generated summaries. In this article, we present the summarization of legal documents as binary optimization problem where fitness of the solution is derived based on the weighting of individual statistical features of each sentence such as length of the sentence, sentence position, degree of similarity, term frequency-inverse sentence frequency and keywords to generate summary of the document. In this paper, a gravitational search algorithm is adopted that works on the basis of the law of gravity to optimize the summary of the document. To show the efficacy of the proposed method, we compare the experimental results with particle swarm optimization, genetic algorithm, TextRank, latent semantic analysis, MEAD, MS-Word, SumBasic using ROUGE evaluation metrics on the FIRE-2014 data set. The experimental results of the proposed method show better than the existing state-of-the-art methods in terms of various performance metrics.

关键词： Legal summarization heuristic search algorithms Gravitational search algorithm PSO Genetic algorithm

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heuristic algorithms for feature selection under Bayesian models with block-diagonal covariance structure 17

Heuristic algorithms for feature selection under Bayesian mo...

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4th International Workshop on Computational Network Biology - Modeling, Analysis, and Control (CNB-MAC) - Bioinformatics

作者： Pour, Ali Foroughi Dalton, Lori A. Ohio State Univ Dept Elect & Comp Engn 2015 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Biomed Informat 250 Lincoln Tower1800 Cannon Dr Columbus OH 43210 USA

ISBN: (纸本)9781450347228

Background: Many bioinformatics studies aim to identify markers, or features, that can be used to discriminate between distinct groups. In problems where strong individual markers are not available, or where interactions between gene products are of primary interest, it may be necessary to consider combinations of features as a marker family. To this end, recent work proposes a hierarchical Bayesian framework for feature selection that places a prior on the set of features we wish to select and on the label-conditioned feature distribution. While an analytical posterior under Gaussian models with block covariance structures is available, the optimal feature selection algorithm for this model remains intractable since it requires evaluating the posterior over the space of all possible covariance block structures and feature-block assignments. To address this computational barrier, in prior work we proposed a simple suboptimal algorithm, 2MNC-Robust, with robust performance across the space of block structures. Here, we present three new heuristic feature selection algorithms. Results: The proposed algorithms outperform 2MNC-Robust and many other popular feature selection algorithms on synthetic data. In addition, enrichment analysis on real breast cancer, colon cancer, and Leukemia data indicates they also output many of the genes and pathways linked to the cancers under study. Conclusions: Bayesian feature selection is a promising framework for small-sample high-dimensional data, in particular biomarker discovery applications. When applied to cancer data these algorithms outputted many genes already shown to be involved in cancer as well as potentially new biomarkers. Furthermore, one of the proposed algorithms, SPM, outputs blocks of heavily correlated genes, particularly useful for studying gene interactions and gene networks.

关键词： Feature selection Bayesian learning Biomarker discovery heuristic search algorithms

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heuristic algorithms for feature selection under Bayesian models with block-diagonal covariance structure

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BMC BIOINFORMATICS 2018年第3-Sup期19卷 70-70页

关键词： Feature selection Bayesian learning Biomarker discovery heuristic search algorithms

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heuristic search for equivalence checking

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SOFTWARE AND SYSTEMS MODELING 2016年第2期15卷 513-530页

作者： De Francesco, Nicoletta Lettieri, Giuseppe Santone, Antonella Vaglini, Gigliola Univ Pisa Dipartimento Ingn Informaz Pisa Italy Univ Sannio Dipartimento Ingn Benevento Italy

Equivalence checking plays a crucial role in formal verification since it is a natural relation for expressing the matching of a system implementation against its specification. In this paper, we present an efficient procedure, based on heuristic search, for checking well-known bisimulation equivalences for concurrent systems specified through process algebras. The method tries to improve, with respect to other solutions, both the memory occupation and the time required for proving the equivalence of systems. A prototype has been developed to evaluate the approach on several examples of concurrent system specifications.

关键词： heuristic search algorithms Bisimulation Concurrent systems Model checking

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Recommender System for Model Driven Software Development 2017

Recommender System for Model Driven Software Development

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11th Joint Meeting of European Software Engineering Conference (ESEC) / ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE)

作者： Koegel, Stefan Ulm Univ Inst Software Engn & Programming Languages Ulm Germany

ISBN: (纸本)9781450351058

Models are key artifacts in model driven software engineering, similar to source code in traditional software engineering. Integrated development environments help users while writing source code, e.g. with typed auto completions, quick fixes, or automatic refactorings. Similar integrated features are rare for modeling IDEs. The above source code IDE features can be seen as a recommender system. A recommender system for model driven software engineering can combine data from different sources in order to infer a list of relevant and actionable model changes in real time. These recommendations can speed up working on models by automating repetitive tasks and preventing errors when the changes are atypical for the changed models. Recommendations can be based on common model transformations that are taken from the literature or learned from models in version control systems. Further information can be taken from instance-to meta-model relationships, modeling related artifacts (e.g. correctness constraints), and versions histories of models under version control. We created a prototype recommender that analyses the change history of a single model. We computed its accuracy via cross-validation and found that it was between 0.43 and 0.82 for models from an open source project. In order to have a bigger data set for the evaluation and the learning of model transformation, we also mined repositories from Eclipse projects for Ecore meta models and their versions. We found 4374 meta models with 17249 versions. 244 of these meta models were changed at least ten times and are candidates for learning common model transformations. We plan to evaluate our recommender system in two ways: (1) In off-line evaluations with data sets of models from the literature, created by us, or taken from industry partners. (2) In on-line user studies with participants from academia and industry, performed as case studies and controlled experiments.

关键词： Model Driven Software Engineering Recommender System Data Mining Machine Learning heuristic search algorithms

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Prediction of Academic Performance Using Gravitational search Based Neural Network Algorithm

Prediction of Academic Performance Using Gravitational Searc...

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International Conference on Inventive Computing and Informatics (ICICI)

作者： Chaudhary, Somendra Imran, Abdul Kolekar, Sucheta V. Manipal Univ Dept Informat & Commun Technol Manipal Inst Technol Manipal India

ISBN: (纸本)9781538640319

Education is the way towards encouraging learning, or the procurement of information, abilities, values, convictions, & propensities. Education often happens under the direction of instructors. Academic performance is the result of education - the degree to which a pupil, educator or institute has accomplished their educative targets. Education is a critical issue with respect to the advancement of a nation, particularly in India, where tutoring is a component firmly connected with social mobility;accordingly, it is of extraordinary enthusiasm to recognize the pupils who are at hazard of failing, at the earliest opportunity, & also to understand which variables affect this. A Data Mining model is an appropriate instrument to include these assignments. An approach where supervised learning, learning where a training set of correctly identified observations is available is ideal. Statistical classification or classification, which is an instance of supervised learning is chosen as the method to achieve aforementioned tasks. A data set from two Portuguese schools, a country where educational & social mobility are closely related, much like India, was chosen. Broadly, three classification algorithms are used to construct data mining models: Naive Bayes Classifier, a Decision Tree Classifier (C4.5) & Neural Networks (Feedforward Neural Networks) using Gravitational search Algorithm. Firstly, a C4.5 Decision Tree in Java is used to test & train the data for both pruned & unpruned trees. Further, both Naive Bayes Classifier & C4.5 are used to train models using cross validation & cost matrices. Lastly, a Feedforward Neural Network is trained for the dataset in which weights are updated using GSA which further takes in consideration, the error of FNN. Neural Networks (Feedforward Neural Networks) using Gravitational search Algorithm tends to have better accuracy than its counterparts.

关键词： Educational Data Mining (EDM) Classification Dropout Prediction Attrition Optimization heuristic search algorithms Gravitational search Algorithm FNN Neural network Learning Neural Network Feedforward Neural Networks Law of gravity

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Evolutionary Computation based Feature Selection: A Survey

Evolutionary Computation based Feature Selection: A Survey

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International Conference on Electronics, Communication and Aerospace Technology

作者： Suresh Dara Mamidi Jagadeeshwara Reddy Nageswara Rao Eluri Department of CSE B V Raju Institute of Technology (UGC Autonomous) Department of Computer Science King Khalid University

ISBN: (纸本)9781538609668

In previous years, different Lateral thinking optimization techniques have been developed based on evolutionary computation. Many of these methods are inspired by spill out behaviors in nature. In this Paper, a new optimization algorithm based on the law of gravity and mass interactions named as Gravitational search Algorithm (GSA) is discussed for solving feature selection. In GSA, the searcher agents are a collection of masses which will interact with each other based on the law of motion and Newtonian gravity which gives the binary evolutionary optimized high performance. The detailed feature selection has been discussed in this paper and The GSA method has been compared with some well-known optimized search methods such as GA (Genetic Algorithm), PSO (Particle Swarm Optimization).

关键词： Gravitational search algorithm heuristic search algorithms Law of Gravity Optimization

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