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Model review and algorithm comparison on multi-objective disassembly line balancing

JOURNAL OF MANUFACTURING SYSTEMS 2020年 56卷 484-500页

作者： Laili, Yuanjun Li, Yulin Fang, Yilin Pham, Duc Truong Zhang, Lin Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China Univ Birmingham Sch Mech Engn Birmingham B15 2TT W Midlands England

As the disassembly of end-of-life products is affected by several dynamic and uncertain issues, many mathe-matical models and solution approaches have been established. However, with more extended objectives, constraints and different methods of disassembly, inconsistent models relating to product representations and types of disassembly lines have become the main barriers for the transfer of research to practise. In this paper, a systematic overview of recent models to summarise the input data, parameters, decision variables, constraints and objectives of disassembly line balancing are presented. After discussing the adaptation and extensibility of these models for different environments, a unified encoding scheme is designed to apply typical multi-objective evolutionary algorithms on this problem with extensive decision variables and seven significant objectives. algorithm comparison on four typical cases is then carried out based on seven commonly used products to verify the optimisation process for the integrated version of existing models and demonstrate the overall performance of the typical multi-objective evolutionary algorithms on this problem. Experimental results can be a baseline for further algorithm design and practical algorithm selection on these disassembly line balancing scenarios.

关键词： Disassembly line balancing Objectives and constraints transformation Multi-objective optimisation algorithm comparison

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Real-time prediction of multivariate ROP (rate of penetration) based on machine learning regression algorithms: algorithm comparison, model evaluation and parameter analysis

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ENERGY EXPLORATION & EXPLOITATION 2023年第5期41卷 1779-1801页

作者： Liu, Wei Fu, Jiasheng Tang, Chunjing Huang, Xinyu Sun, Ting CNPC Engn Technol R&D Co Ltd Beijing Peoples R China Natl Engn Res Ctr Oil & Gas Drilling & Complet Tec Beijing Peoples R China China Univ Petr Coll Petr Engn Beijing Peoples R China China Univ Petr Beijing Peoples R China

ROP (Rate of Penetration) is a comprehensive indicator of the rock drilling process and how efficiently predicting drilling rates is important to optimize resource allocation, reduce drilling costs and manage drilling hazards. However, the traditional model is difficult to consider the multiple factors, which makes the prediction accuracy difficult to meet the real drilling requirements. In order to provide efficient, accurate and comprehensive information for drilling operation decision-making, this study evaluated the applicability of four typical regression algorithms based on machine learning for predicting pore pressure in Troll West field, namely SVR (Support Vector Regression), Linear regression, Regression Tree and Gradient Boosting regression. These methods allow more parameters input. By comparing the prediction results of these typical regression algorithms based on R-2(R-Square), explained variance, mean absolute error, mean squared error, median absolute error and other performance indicators, it was found that each method predicted different results, among which Gradient Boosting regression has the best results, their prediction accuracy is high and the error is very low. The prediction accuracy of these methods is positively correlated with the proportion of the training data set. With the increase of logging features, the prediction accuracy is gradually improved. In the prediction of adjacent wells, the ROP prediction methods can achieve a certain prediction effect, which shows that this method is suitable for ROP prediction in Troll West field.

关键词： ROP machine learning real-time prediction regression algorithm algorithm comparison parameters combination analysis

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The algorithm comparison for solving eigen problem in open optical waveguides

The algorithm comparison for solving eigen problem in open o...

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Conference on Optical Design and Testing II

作者： Zhu, JX Tang, SY Zhejiang Univ Dept Matemat Hangzhou 310027 Peoples R China

ISBN: (纸本)0819455938

In this paper, based on the Rayleigh quotient and its generalized Rayleigh quotient, two treatments are developed to solve the complex eigenvalue problems, which arise from the analysis of the optical wave propagation in Stab waveguides with some perfectly matched layers. Numerical examples illustrate that these treatments are efficient and feasible, and the generalized Rayleigh quotient method is better than the Rayleigh quotient method for some eigenvalues with smaller norm.

关键词： algorithm comparison Rayleigh quotient slab waveguide PML eigenvalue

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Data compression and archiving software implementation and their algorithm comparison

Data compression and archiving software implementation and t...

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作者： Jung, Young Je Naval Postgraduate School

学位级别：硕士

Although data compression has been studied for over 30 years, many new techniques are still evolving. There is considerable software available that incorporates compression schemes and archiving techniques. The U. S. Navy is interested in knowing the performance of this software. This thesis studies and compares the software. The testing files consist of the file types specified by the U. S. Naval Security Detachment at Pensacola, Florida.

关键词： Data Compression and Archiving Software Performance Analysis Compression Ratios and Execution Time comparison algorithm comparison

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Heuristics and Metaheuristics for Solving Capacitated Vehicle Routing Problem: An algorithm comparison

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Procedia Computer Science 2024年 234卷 494-501页

作者： Dihin Muriyatmoko Arif Djunaidy Ahmad Muklason Program Studi Teknik Informatika Universitas Darussalam Gontor Ponorogo Jawa Timur 63471 Indonesia Program Studi Sistem Informasi Institut Teknologi Sepuluh Nopember Surabaya Jawa Timur 60111 Indonesia

The Capacitated Vehicle Routing Problem (CVRP) is an optimization problem that involves arranging vehicle routes while considering vehicle capacity. This research aims to compare the effectiveness of several heuristic (Path Cheapest Arc, Path Most Constrained Arc, Savings, Christofides) and metaheuristic (Greedy Descent, Guided Local Search, Simulated Annealing, Tabu Search) algorithms for determining the routing scenarios and vehicle types for faculty transportation between the male campus in Ponorogo and the female campus in Mantingan Ngawi at Universitas Darussalam Gontor. The research involves decision variables for vehicle routing determination and the objective of minimizing the distance traveled. The constraint function includes two options: one vehicle with a capacity of 60 passengers and four vehicles. This research utilizes Google OR Tools with the Python programming language using Google Colab to facilitate the calculation process. The research results indicate that metaheuristic algorithms outperform heuristics for complex case studies (four vehicles). This study recommends using metaheuristic methods, specifically Christofides Guided Local Search and Christofides Simulated Annealing, for determining the best routes with the shortest distance and time. Further research was developed using algorithms such as hyperheuristics or matheuristics.

关键词： Capacitated Vehicle Routing Problem heuristics metaheuristics Google OR Tools algorithm comparison

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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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Optimal designs for mixture choice experiments by simulated annealing

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2025年 257卷

作者： Mao, Yicheng Kessels, Roselinde Maastricht Univ Dept Data Analyt & Digitalizat POB 616 NL-6200 MD Maastricht Netherlands Univ Antwerp City Campus Dept Econ Prinsstr 13 B-2000 Antwerp Belgium

Mixture choice experiments investigate people's preferences for products composed of different ingredients. To ensure the quality of the experimental design, many researchers use Bayesian optimal design methods. Efficient search algorithms are essential for obtaining such designs. Yet, research in the field of mixture choice experiments is not extensive. Our paper pioneers the use of a simulated annealing (SA) algorithm to construct Bayesian optimal designs for mixture choice experiments. Our SA algorithm not only accepts better solutions, but also has a certain probability of accepting inferior solutions. This approach effectively prevents rapid convergence, enabling broader exploration of the solution space. Although our SA algorithm may start more slowly than the widely used mixture coordinate-exchange method, it generally produces higher-quality mixture choice designs after a reasonable runtime. We demonstrate the superior performance of our SA algorithm through extensive computational experiments and a real-life example.

关键词： Choice experiment Bayesian integrated variance Mixture experiment algorithm comparison Simulated annealing Mixture coordinate exchange

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Multi-objective optimization in high-dimensional patent layout using deep Bayesian network and hybrid algorithm

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DISCOVER COMPUTING 2025年第1期28卷 1-22页

作者： Wu, Minghua Jiang, Wenwu Weixian Enterprise Management Consulting Co Ltd Patent Technol Anal Dept Anhui Hefei 230029 Anhui Peoples R China Univ Sci & Technol China Sch Humanities & Social Sci Hefei 230031 Anhui Peoples R China

This study introduces a novel multi-objective optimization framework (DBNO) that integrates deep Bayesian networks with a hybrid algorithm combining random search and innovation diffusion to address high-dimensional patent layout optimization challenges. The framework was developed in response to the increasing complexity of patent layout decisions, where traditional single-objective optimization methods prove inadequate for simultaneously addressing multiple conflicting objectives such as profit, risk, and sustainability. To evaluate the framework's effectiveness, we conducted comprehensive experiments comparing DBNO against established algorithms including genetic algorithm (GA), particle swarm optimization (PSO), and traditional Bayesian optimization methods. Performance metrics encompassed convergence speed, computational efficiency, optimization stability, and solution quality across multiple objectives. The results demonstrate that DBNO consistently outperforms benchmark algorithms, particularly in optimizing sustainability objectives. Notably, DBNO exhibited superior stability and higher success rates in the optimization process compared to GA and PSO, highlighting its robustness in handling complex high-dimensional optimization problems. Furthermore, the integration of innovation diffusion mechanisms significantly enhanced both the efficiency and accuracy of the optimization process. The primary contribution of this research lies in the novel combination of deep Bayesian networks with ensemble random search techniques, resulting in a powerful multi-objective optimization framework. This approach provides an effective solution for high-dimensional patent layout problems while offering new perspectives for patent strategic decision-making. The findings advance the field of multi-objective optimization and establish a foundation for future research in patent portfolio optimization.

关键词： Deep Bayesian network Multi-objective optimization Integrated random search Innovation diffusion Patent layout algorithm comparison Convergence speed Computational efficiency

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A Comparative Study of Predictive Analysis Using Machine Learning Techniques: Performance Evaluation of Manual and AutoML algorithms

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第1期16卷 12-31页

作者： Rezaul, Karim Mohammed Jewel, Md. Sudhan, Anjali Khan, Mifta Uddin Fernando, Maharage Roshika Sathsarani Siddiquee, Kazy Noor e Alam Jannat, Tajnuva Rahman, Muhammad Azizur Islam, Md Shabiul Wrexham Univ Fac Arts Sci & Technol Wrexham LL11 2AW Wales Ctr Appl Res Software & IT CARSIT 80a Ashfield St London E1 2BJ England Multimedia Univ Fac Engn FOE Cyberjaya 63100 Malaysia Cardiff Metropolitan Univ Dept Comp Sci Llandaff CampusWestern Ave Cardiff CF5 2YB Wales

In this study, we have compared manual machine learning with automated machine learning (AutoML) to see which performs better in predictive analysis. Using data from past football matches, we tested a range of algorithms to forecast game outcomes. By exploring the data, we discovered patterns and team correlations, then cleaned and prepped the data to ensure the models had the best possible inputs. Our findings show that AutoML, especially when using logistic regression can outperform manual methods in prediction accuracy. The big advantage of AutoML is that it automates the tricky parts, like data cleaning, feature selection, and tuning model parameters, saving time and effort compared to manual approaches, which require more expertise to achieve similar results. This research highlights how AutoML can make predictive analysis easier and more accurate, providing useful insights for many fields. Future work could explore using different data types and applying these techniques to other areas to show how adaptable and powerful machine learning can be.

关键词： Machine learning predictive analytics sports forecasting automated machine learning (AutoML) feature engineering model evaluation data pre-processing algorithm comparison football analytics sports betting team performance metrics exploratory data analysis (EDA) cross-validation techniques

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Constructing Bayesian optimal designs for discrete choice experiments by simulated annealing

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JOURNAL OF CHOICE MODELLING 2025年 55卷

作者： Mao, Yicheng Kessels, Roselinde van der Zanden, Tom Maastricht Univ Dept Data Analyt & Digitalizat POB 616 NL-6200 MD Maastricht Netherlands Univ Antwerp Dept Econ City CampusPrinsstr 13 B-2000 Antwerp Belgium

Discrete choice experiments (DCEs) investigate the attributes that influence individuals' choices when selecting among various options. To enhance the quality of the estimated choice models, researchers opt for Bayesian optimal designs that utilize existing information about the attributes' preferences. Given the nonlinear nature of choice models, the construction of an appropriate design requires efficient algorithms. Among these, the coordinate-exchange (CE) algorithm is commonly employed for constructing designs based on the MNL model. However, as a hill-climbing method, the CE algorithm tends to quickly converge to local optima, potentially limiting the quality of the resulting designs. We propose the use of a simulated annealing (SA) algorithm to construct Bayesian optimal designs. This algorithm accepts both superior and inferior solutions, avoiding premature convergence and allowing a more thorough exploration of potential solutions. Consequently, it ultimately obtains higher-quality choice designs compared to the CE algorithm. Our work represents the first application of an SA algorithm in constructing Bayesian optimal designs for DCEs. Through extensive computational experiments, we demonstrate that the SA designs generally outperform the CE designs in terms of statistical efficiency, especially when the prior preference information is highly uncertain.

关键词： Discrete choice experiments Bayesian optimal design algorithm comparison Simulated annealing Coordinate exchange

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