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Clustering Ensemble: A multiobjective genetic algorithm based Approach

Procedia Technology 2013年 10卷 443-449页

作者： Sujoy Chatterjee Anirban Mukhopadhyay Department of Computer Science and Engineering University of Kalyani Kalyani - 741235 India

Clustering ensemble refers to the problem of obtaining a final clustering of some data set from a set of input clustering solutions. In this article, the clustering ensemble problem has been modeled as a multiobjective optimization problem and a multiobjective evolutionary algorithm has been used for this purpose. The proposed multiobjective evolutionary clustering ensemble algorithm (MOECEA) evolves a clustering solution from the input clusterings by optimizing two criteria simultaneously. The first objective is to maximize the similarity of the resultant clustering with all the input clusterings, where the similarity between two clustering solutions is computed using adjusted Rand index. The second criteria is to minimize the standard deviation among the similarity scores in order to prevent the evolved clustering solution to be very similar with one of the input clusterings and very dissimilar with the others. The performance of the proposed algorithm has been compared with that of other well-known existing cluster ensemble algorithms for a number of artificial and real-life data sets.

关键词： Clustering Ensemble Validity indices multiobjective genetic algorithm Pareto optimality

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Application of a multiobjective genetic algorithm to Reduce Wet Weather Sanitary Sewer Overflows and Surcharge

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JOURNAL OF SUSTAINABLE WATER IN THE BUILT ENVIRONMENT 2017年第3期3卷

作者： Itaquy, Bruno Ogidan, Olufunso Giacomoni, Marcio H. Merrick & Co 401 East Sonterra BlvdSuit 250 San Antonio TX 78258 USA Univ Texas San Antonio Dept Civil & Environm Engn One UTSA Circle San Antonio TX 78249 USA

Sanitary sewer overflow (SSO) is the discharge of wastewater from the collection network into the environment. SSOs are significant environmental and public health hazards. The EPA estimates that up to 75,000 SSO events occur in the United States each year. One of the main causes of SSOs is excessive rainfall derived inflow and infiltration into the sanitary sewer network. This study applies a multiobjective genetic algorithm (MOGA) to identify near-optimal solutions to maximize the reduction of the number of SSOs, volume of SSOs, and surcharge, while minimizing costs for sewer rehabilitation. Three approaches are investigated: (1) enhancing the flow capacity of the collection and conveyance system by pipe diameter increase, (2) peak flow reduction using decentralized inline storage tanks, and (3) combining pipe capacity enhancement with inline storages. The approach is tested in a 15.3-square-kilometer sewer network located west of downtown San Antonio, Texas. The results show that the Combination strategy outperforms both the pipe capacity enhancement and the inline storage strategies. Reduction of surcharge lowers the pressure in the entire network and also provides substantial reduction of SSOs volumes. (C) 2017 American Society of Civil Engineers.

关键词： Sanitary sewer overflow multiobjective genetic algorithm Sanitary sewer systems Surcharge

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On maximizing solution diversity in a multiobjective multidisciplinary genetic algorithm for design optimization

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2004年第4期32卷 491-514页

作者： Gunawan, S Farhang-Mehr, A Azarm, S Univ Maryland Dept Mech Engn College Pk MD 20742 USA

This article presents a new method for multiobjective multidisciplinary design optimization. The method obtains an estimate of the Pareto frontier with maximum solution diversity using a quality index, referred to as entropy index. Unlike previous methods that maintain diversity in the solution set heuristically, our method improve, overall quality of solutions by explicitly optimizing the entropy index at every system-level iteration, and then using this information to bias the search process toward obtaining a solution set with maximum diversity. Our method utilizes a multiobjective genetic algorithm as an optimizer in each subproblem of a multidisciplinary optimization problem. To demonstrate the proposed approach, we applied our method to a mechanical design problem of a speed reducer and the results are compared with those obtained by a few other multiobjective optimization methods. A minimal set of quality indexes is used to compare the diversity and optimality of the obtained solution sets from the different methods on a quantitative basis.

关键词： multidisciplinary optimization multiobjective genetic algorithm solution diversity entropy

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A multiobjective Hybrid genetic algorithm for TFT-LCD Module Assembly Scheduling

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2014年第3期11卷 692-705页

作者： Chou, Che-Wei Chien, Chen-Fu Gen, Mitsuo Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 30013 Taiwan Fuzzy Log Syst Inst Fukuoka 8200067 Japan

The thin-film transistor-liquid crystal display (TFT-LCD) module assembly production is a flexible job-shop scheduling problem that is critical to satisfy the customer demands on time. On the module assembly shop floor, each workstation has identical and non-identical parallel machines that access the jobs at various processing velocities depending on the product families. To satisfy the various jobs, the machines need to be set up as the numerous tools to conduct consecutive products. This study aims to propose a novel approach to address the TFT-LCD module assembly scheduling problem by simultaneously considering the following multiple and often conflicting objectives such as the makespan, the weighted number of tardy jobs, and the total machine setup time, subject to the constraints of product families, non-identical parallel machines, and sequence-dependent setup times. In particular, we developed a multiobjective hybrid genetic algorithm (MO-HGA) that hybridizes with the variable neighborhood descent (VND) algorithm as a local search and TOPSIS evaluation technique to derive the best compromised solution. To estimate the validity of the proposed MO-HGA, experiments based on empirical data were conducted to compare the results with conventional approaches. The results have shown the validity of this approach. This study concludes with a discussion of future research directions. Note to Practitioners-Because of short product lifecycles, cycle time reduction and on-time delivery are crucial in high-tech industries such as the TFT-LCD and semiconductor manufacturing. To address these needs in real settings, a novel multiobjective hybrid genetic algorithm (MO-HGA) was developed, hybridizing with a variable neighborhood descent (VND) algorithm as a local search and TOPSIS technique to select the best compromised solution for the TFT-LCD module assembly scheduling problem. Experiments have shown practical viability of this approach. Future studies can be done to exten

关键词： multiobjective genetic algorithm scheduling thin-film transistor-liquid crystal display (TFT-LCD) TOPSIS variable neighborhood descent

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AN APPLICATION OF genetic algorithm TOWARD SOLVING THE RELIABILITY PROBLEM OF multiobjective SERIES-PARALLEL SYSTEMS

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EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY 2012年第3期14卷 243-248页

作者： Zarinchang, Amir Faghih, Nezameddin Zarinchang, Jafar Islamic Azad Univ Shiraz Branch Dept Ind Engn Shiraz Iran Shiraz Univ Dept Management Shiraz Iran Shiraz Univ Dept Mech Engn Shiraz Iran

Since developing an appropriate solution for reliability optimization problem with mathematical programming methods has been considered as difficult techniques, the heuristic approaches increasingly has been applied. Multiobjectve genetic algorithm (MGA) has been among heuristic methods that was developed to find solutions for series-parallel systems to obtain maximum reliability, and minimum cost and weight at the system level. These are very common problems in engineering design such as mechanical and electrical systems. It has been shown that the Multiobjectve genetic algorithm offers proper results to these problems while it respects to the several objective functions such as reliability, cost and weight. This paper presents the combination of probabilistic search, and one of the decision making methods called Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The Multiobjectve genetic algorithm, allows us to achieve a proper design solution while it saves a considerable time compared with some other approaches. At the same time as the reliability, cost and weight were chosen as objective functions, the results obtained by this method showed an overall improvement in comparison to the existing GA method considering cost and weight as constraints.

关键词： multiobjective genetic algorithm Reliability Optimization Redundancy Apportionment Series-Parallel Systems TOPSIS Method

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multiobjective Optimization of MPLS-IP Networks with a Variable Neighborhood genetic algorithm

Multiobjective Optimization of MPLS-IP Networks with a Varia...

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作者： Renata da Encarnação Onety Federal University Of Minas Gerais

学位级别：博士

The demand for different levels of Quality of Service (QoS) in IP networks is growing, mainly to attend multimedia applications. However, not only indicators of quality have conflicting features, but also the problem of determining routes covered by more than two QoS constraints is NP-complete (Nondeterministic Polynomial Time Complete). This work proposes an algorithm to optimize multiple Quality of Service indices of Multi Protocol Label Switching (MPLS) IP networks. Such an approach aims at minimizing the network cost and the amount of simultaneous requests rejection, as well as performing load balancing among routes. The proposed algorithm, the Variable Neighborhood multiobjective genetic algorithm (VN-MGA), is a genetic algorithm based on the Elitist Non-Dominated Sorted genetic algorithm (NSGA-II), with a particular feature that different parts of a solution are encoded differently, at Level 1 and Level 2. In order to improve results, both representations are needed. At Level 1, the first part of the solution is encoded by considering as decision variables the arrows that form the routes to be followed by each request (whilst the second part of the solution is kept constant), whereas at Level 2, the second part of the solution is encoded by considering the sequence of requests as decision variables, and first part is kept constant. Pareto-fronts obtained by VN-MGA dominate fronts obtained by fixed-neighborhood encoding schemes. Besides potential benefits of the proposed approach application to packet routing optimization in MPLS networks, this work raises the theoretical issue of the systematic application of variable encodings, which allow variable neighborhood searches, as operators inside general evolutionary computation algorithms.

关键词： Routing multiobjective genetic algorithm Variable encoding

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Global optimization of an accelerator lattice using multiobjective genetic algorithms

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NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT 2009年第1期609卷 50-57页

作者： Yang, Lingyun Robin, David Sannibale, Fernando Steier, Christoph Wan, Weishi Univ Calif Berkeley Lawrence Berkeley Lab Adv Light Source Berkeley CA 94720 USA

Storage ring lattice design is a highly constrained multiobjective optimization problem. The objectives can include lattice functions or derived quantities like emittance, brightness, or luminosity while simultaneously fulfilling constraints such as linear stability of the lattice. In this paper we explore the use of multiobjective genetic algorithms (MOGA) to find globally optimized lattice settings in a storage ring. Using the Advanced Light Source (ALS) for illustration, three examples of MOGA are shown and analyzed-(i) using three fit parameters to optimize the straight section betatron function and the natural emittance, (ii) using three fit parameters to optimize the photon brightness of bending magnet and insertion device source points in the lattice and (iii) a six parameter fit creating alternating high and low horizontal betatron functions in subsequent straight sections while still minimizing the natural emittance. Making use of one of the main benefits. of MOGA, we also study the trade-offs in the optimization objectives between sets of optimal solutions. Published by Elsevier B.V.

关键词： Optimization Lattice design Emittance Brightness multiobjective genetic algorithm

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ARS-Flow 2.0: An enhanced design space exploration flow for accelerator-rich system based on active learning

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INTEGRATION-THE VLSI JOURNAL 2025年 101卷

作者： Huang, Shuaibo Ye, Yuyang Yan, Hao Shi, Longxing Southeast Univ Natl ASIC Syst Engn Technol Res Ctr Nanjing Peoples R China

Surrogate model-based design space exploration (DSE) is the mainstream method to search for optimal microarchitecture designs. However, building accurate models for accelerator-rich systems within limited samples is challenging due to their high dimensionality. Additionally, these models often fall into local optima or have difficulty converging. To address these issues, we propose a DSE flow based on active learning, called ARS-Flow. This method features particle-swarm-optimized Gaussian process regression modeling (PSOGPR), a multiobjective genetic algorithm with self-adaptive hyperparameter control (SAMOGA), and a Pareto-regionoriented stochastic resampling method (PRSRS). Using the gem5-SALAM system for evaluation, the proposed method can build more accurate models and find better microarchitecture designs with acceptable runtime costs.

关键词： Design space exploration Accelerator-rich system Active learning Gaussian process regression multiobjective genetic algorithm Pareto region stochastic resampling

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Analysing skewness of credibilistic coherent trapezoidal fuzzy numbers: Implications for portfolio selection

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2025年第6期56卷 1143-1170页

作者： Mandal, Pawan Kumar Thakur, Manoj Mittal, Garima Indian Inst Technol Mandi Sch Math & Stat Sci Mandi 175075 Himachal Prades India Indian Inst Management Lucknow Decis Sci Lucknow Uttar Pradesh India

The outset of this manuscript involves introducing an analytical formula for the credibility of coherent trapezoidal fuzzy numbers, which represent an extended version of the traditional trapezoidal fuzzy numbers. With this formula, we present a precise counterpart to compute the credibilistic mean, semivariance, and skewness of coherent trapezoidal fuzzy numbers. Leveraging these analytical formulations, we construct a novel tri-objective portfolio selection problem, integrating credibilistic mean, semivariance, and skewness as objectives, along with some practical constraints. When used with the returns of the portfolio as a whole, the derived analytical expressions help to overcome the otherwise computationally expensive approach involving the simulation of results using individual assets' returns. The proposed model is solved by adapting an efficient multiobjective genetic algorithm. The algorithm has been specifically designed to solve portfolio selection models of this type. The efficiency of the proposed model is then demonstrated by taking a real-world scenario of financial stock market datasets from the NSE India. Based on the results of this study, it appears that the proposed novel tri-objective portfolio selection model produces promising results compared to the baseline model and the NIFTY 50 Index considered as a benchmark.

关键词： Coherent trapezoidal fuzzy numbers skewness multiobjective portfolio optimization multiobjective genetic algorithm K-medoids clustering

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Novel explicit models for assessing the frictional resistance of pipe piles subjected to seismic effects

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JOURNAL OF SAFETY SCIENCE AND RESILIENCE 2025年第1期6卷 29-37页

作者： Al-Jeznawi, Duaa Sadik, Laith Alzabeebee, Saif Al-Janabi, Musab Aied Qissab Keawsawasvong, Suraparb Al Nahrain Univ Coll Engn Dept Civil Engn Jadriya Baghdad Iraq Univ Cincinnati Dept Civil & Architectural Engn & Construct Manage Cincinnati 45221 OH USA Univ Al Qadisiyah Dept Rd & Transport Engn Al Qadisiyah Iraq Thammasat Univ Thammasat Sch Engn Dept Civil Engn Res Unit Sci & Innovat Technol Civil Engn Infrastr Bangkok 12120 Pathumthani Thailand

This paper introduces novel explicit models to predict the frictional resistance of open and closed-ended pipe piles subjected to seismic loading. This research employs genetic programming (GP) and multiobjective genetic algorithm-based evolutionary polynomial regression (EPR-MOGA) to develop closed-form expressions for estimating pile frictional resistance, utilizing widely used input parameters for enhanced practicality and applicability in engineering practice. The proposed models are developed using only three input variables: the corrected standard penetration test (SPT) blow count (N1)60, the pile slenderness ratio (L/D), and the peak ground acceleration (PGA). This deliberate reduction in input complexity significantly enhances the models' applicability across a wide range of geotechnical scenarios and industries. The accuracy of the developed models was assessed via the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). In the case of the GP model, the evaluation metrics for the testing set for open-ended piles (R2, RMSE, and MAE values) are 0.89, 0.43, and 0.35, respectively, whereas the corresponding values for closed-ended piles are 0.93, 0.38, and 0.3, respectively. On the other hand, the EPR-MOGA approach achieves similarly encouraging results, with performance metrics of 0.92, 0.37, and 0.29 for open-ended piles and 0.91, 0.39, and 0.30 for closed-ended piles.

关键词： Seismic resilience genetic programming Evolutionary polynomial regression multiobjective genetic algorithm Pipe piles Frictional resistance Seismic excitation Corrected SPT test blow count

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