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Automated extraction of extended structured motifs using multi-objective genetic algorithm

EXPERT SYSTEMS WITH APPLICATIONS 2010年第3期37卷 2421-2426页

作者： Kaya, Mehmet Firat Univ Dept Comp Engn TR-23169 Elazig Turkey

A structured motif is defined as a collection of highly conserved simple motifs with pre-specified sizes and gaps between them. In structured motif extraction, while all simple motifs are unknown, all gap ranges are known earlier. In this paper, we propose a novel method using multi-objective evolutionary algorithm to extract automatically extended structured motifs in which all simple motifs and gap ranges are unknown. The method employs three conflicting objectives;similarity and support maximization and total gap range minimization. To the best of our knowledge, this is the first effort in this direction. The proposed method can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding motifs. We compare our method with the two well-known structured motif extraction methods, EXMOTIF and RISOTTO. Experiments conducted on synthetics and real data set demonstrate that the proposed method exhibits good performance over the other methods in terms of runtime and accuracy. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Motif discovery Data mining multi-objective genetic algorithm

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A multi-objective genetic algorithm approach to rule mining for affective product design

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第8期39卷 7411-7419页

作者： Fung, K. Y. Kwong, C. K. Siu, K. W. M. Yu, K. M. Hong Kong Polytech Univ Dept Ind & Syst Engn Kowloon Hong Kong Peoples R China Hong Kong Polytech Univ Sch Design Kowloon Hong Kong Peoples R China

A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithm Affective product design Rule mining

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Optimization of an aluminum alloy anti-collision side beam hot stamping process using a multi-objective genetic algorithm

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ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING 2013年第3期13卷 401-411页

作者： Zhou, Jing Wang, Baoyu Lin, Jianguo Fu, Lei Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Univ London Imperial Coll Sci Technol & Med Dept Mech Engn London SW7 2AZ England

The process parameters of aluminum alloy hot stamping are essential for product forming quality. In the case of an anti-collision side beam inside car doors, the finite-element model of aluminum alloy hot stamping is set up, and the forming quality is investigated under an ordinary process condition. The blank hold force (BHF) has a significant impact on the forming quality in hot stamping. Using the Latin hypercube method to sample the simulation data points and the finite-element (FE) model to calculate the forming quality indices of the data points according to the response value of the indices, the quadratic response surfaces between the process parameter inputs and the forming quality indices are initialized. Using the multi-objective genetic algorithm NSGA-II (non-dominated sorting genetic algorithm) to optimize the responses of the process parameters, the Pareto solutions corresponding to combinations of the blank hold force and stamping speed are obtained. Finally, based on the optimal process parameters, stamping tests are carried out. Compared with the results of the stamping trial and numerical simulation, it is demonstrated that the finite-element model can predict forming defects and be consistent with the actual condition and that the optimization procedure proposed in the paper is feasible. (C) 2013 Politechnika Wroclawska. Published by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

关键词： Aluminum alloy hot stamping Process parameters Response surface model multi-objective genetic algorithm

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Optimal answer generation by equivalent transformation incorporating multi-objective genetic algorithm

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SOFT COMPUTING 2022年第19期26卷 10535-10546页

作者： Miura, Katsunori Powell, Courtney Munetomo, Masaharu Otaru Univ Dept Informat & Management Sci 3-5-21 Midori Otaru Hokkaido 0478501 Japan Hokkaido Univ Informat Initiat Ctr Kita Ku Kita 11Nishi 5 Sapporo Hokkaido 0600811 Japan

This paper proposes a framework for time-efficiently finding multiple answers that meet multiple logical constraints and are Pareto-optimal for multiple objective functions. The proposed framework determines a set of optimal answers by iteratively replacing a set of definite clauses. Clause replacement is performed using a SAT solver consisting of equivalent transformation rules (ETRs). An ETR replaces a definite clause with one or more clauses while preserving the declarative meaning of the union of the original clause and problem clauses. To efficiently find optimal answers, in this paper, we define a new class of ETRs that are generated based on the evaluation results of a multi-objective genetic algorithm (MOGA) and propose a method for generating ETRs that belong to the new class. ETRs belonging to the new class help to replace definite clauses according to user objectives such as cost-benefit performance, reliability, and financial constraints. Thus, a SAT solver that uses the new ETR class in addition to extant ETR classes can preferentially replace definite clauses that produce the optimal answer for user objectives. Experimental results indicate that the proposed framework can significantly reduce the computation time and memory usage necessary to determine a set of optimal answers for user objectives.

关键词： Equivalent transformation multi-objective genetic algorithm Clause replacement Optimization Pareto-optimal answer Predicate logic

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Hydraulic performance and parameter optimisation of a microporous ceramic emitter using computational fluid dynamics, artificial neural network and multi-objective genetic algorithm

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BIOSYSTEMS ENGINEERING 2020年 189卷 11-23页

作者： Zhou, Wei Zhang, Lin Wu, Pute Cai, Yaohui Zhao, Xiao Yao, Chunping Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Are Minist Educ Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Inst Soil & Water Conservat Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Inst Water Saving Agr Arid Areas China Yangling 712100 Shaanxi Peoples R China

As the core of an irrigation system, microporous ceramic emitters (MPCEs) are closely related to the design, operation, and management of underground irrigation systems. In this study, computational fluid dynamics (CFD), an artificial neural network (ANN), and a multi-objective genetic algorithm (MOGA) were used to investigate the hydraulic performance of an MPCE and conduct parameter optimisation. A CFD software package was used to analyse the influence of the working parameters, structural parameters, and material properties on the flow characteristics of MPCE. Subsequently, an ANN and MOGA were used to optimise fabrication cost, flow rate, and flow index of the emitter. The results showed that a decrease in the bottom thickness or wall thickness of the MPCE increased the flow index of the emitter and the sensitivity of the flowrate to changes in pressure. Low working pressure was conducive to maintaining the active irrigation characteristic of the MPCE but the flowrate decreased. For crops with low water requirement, these conditions are ideal. The flow in the MPCE microchannel was mainly affected by the viscous resistance, whereas the inertial resistance only had an effect when the flow velocity was large. For field applications, the parameters can be optimised in the range obtained by the MOGA optimisation depending on the requirements of crop water demand, irrigation quality, and fabrication cost to achieve optimal irrigation performance. The results of this study provide references for the standardised fabrication of MPCEs. (C) 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Subsurface drip irrigation Microporous ceramic emitters Computational fluid dynamics Artificial neural network multi-objective genetic algorithm

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Stochastic quality-cost optimization system hybridizing multi-objective genetic algorithm and quality function deployment

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JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 2015年第4期21卷 407-422页

作者： Lim, Tae-Kyung Jang, Won-Suk Choi, Jae-ho Lee, Dong-Eun Kyungpook Natl Univ Sch Architecture & Civil Engn Taegu 702701 South Korea Yeungnam Univ Dept Civil Engn Gyeongsa Si 712749 Gyeongsangbuk D South Korea Dong A Univ Dept Civil Engn Pusan 604714 South Korea Kyungpook Natl Univ Sch Architecture & Civil Engn Taegu 702701 South Korea

This paper introduces an automated tool, the stochastic quality-cost optimization (SQCO) system, that hybridizes multi-objective genetic algorithm (MOGA) and Quality Function Deployment (QFD). The system identifies the optimal trade-off between a construction owner's satisfaction and a contractor's satisfaction. It is important to reconcile the project participants' conflicting interests because the construction owner aims to maximize the quality of construction while the contractor aims to minimize the cost of construction. MOGA is used to optimize resource allocation when owner satisfaction and contractor satisfaction are pursued at the same time under a limited budget. multi-objective optimization is integrated with simulation to effectively deal with the uncertainties of the QFD input and the variability of the QFD output. This study is of value to practitioners because SQCO allows for the establishment of a quality plan that satisfies all of the multi project participants. The study is also of relevance to researchers in that it allows researchers to expeditiously identify an optimal design alternative of construction methods and operations. A test case implemented with a curtain-wall unit verifies the usability and validity of the system in practice.

关键词： quality multi-objective genetic algorithm optimization Quality Function Deployment (QFD) curtain-wall building envelope cost

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Energy audit and management of environmental GHG emissions based on multi-objective genetic algorithm and data envelopment analysis: An agriculture case

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ENERGY REPORTS 2023年 10卷 1507-1520页

作者： Taherzadeh-Shalmaei, Nahid Rafiee, Mahta Kaab, Ali Khanali, Majid Rad, Mohammad Amin Vaziri Kasaeian, Alibakhsh Univ Tehran Coll Agr Dept Agr Machinery Engn Karaj Iran Univ Tehran Fac New Sci & Technol Dept Renewable Energies & Environm Tehran Iran

This study aimed to address the challenges of energy consumption and environmental emissions in the mushroom production sector through an energy audit analysis and life cycle assessment approach. The main objective of the study was to estimate energy and environmental indicators, optimize their outputs using multi-objective genetic algorithm and data envelopment analysis methods, and identify opportunities for energy savings in the mushroom production process. The energy flow analysis revealed that the total input energy for mushroom production was 1022537.82 MJ/m2, while the total output energy was only 11125.94 MJ/m2, resulting in an energy use efficiency rate of 0.01, indicating significant energy imbalance and inefficiency. Electrical energy consumption had the largest share of total consumed energy, approximately 80.6%. The life cycle assessment results showed that the mushroom production chain emits about 8.50 x 10+3 kg of CO2 GHG. The results of energy optimization demonstrate that between 6.5% and 25% reductions in energy utilization can be achieved during mushroom production. Among the input energies, compost had the largest quota in energy savings of almost 7% and 26% by data envelopment analysis and multi-objective genetic algorithm techniques, respectively. Thus, it is recommended to implement the multi-objective genetic algorithm technique to identify opportunities for energy savings and reduce environmental emissions in the mushroom production sector, which can lead to significant energy savings and contribute to environmental sustainability while reducing operating costs.& COPY;2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Energy audit Environmental sustainability Optimization multi-objective genetic algorithm

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Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2022年第3期60卷 663-681页

作者： Deng, Xiongshi Li, Min Deng, Shaobo Wang, Lei Sch Informat Engn Nanchang Inst Technol Nanchang 330099 Jiangxi Peoples R China Jiangxi Prov Key Lab Water Informat Cooperat Sens Nanchang 330099 Jiangxi Peoples R China

Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in significant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked using an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield a group comprising the most relevant genes related to the class. In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes' group using a multi-objective optimization genetic algorithm. We performed comprehensive experiments to compare XGBoost-MOGA with other state-of-the-art feature selection methods using two well-known learning classifiers on 14 publicly available microarray expression datasets. The experimental results show that XGBoost-MOGA yields significantly better results than previous state-of-the-art algorithms in terms of various evaluation criteria, such as accuracy, F-score, precision, and recall.

关键词： XGBoost Feature selection Microarray gene expression multi-objective genetic algorithm Classification

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A multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problem

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第2-3期45卷 115-125页

作者： Wang, Xiaojuan Gao, Liang Zhang, Chaoyong Li, Xinyu Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

In many real-world applications, processing times may vary dynamically due to human factors or operating faults and there are some other uncertain factors in the scheduling problems. Flexible job-shop scheduling problem (FJSP) is an extended traditional job-shop scheduling problem, which more approximates to practical scheduling problems. This paper presents a genetic algorithm based on immune and entropy principle to solve the multi-objective fuzzy FJSP. In this improved multi-objective algorithm, the fitness scheme based on Pareto-optimality is applied, and the immune and entropy principle is used to keep the diversity of individuals and overcome the problem of premature convergence. Efficient crossover and mutation operators are proposed to adapt to the special chromosome structure. The computational results demonstrate the effectiveness of the proposed algorithm.

关键词： fuzzy flexible job-shop scheduling multi-objective genetic algorithm Pareto-optimality immune and entropy principle

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multi-objective optimisation for minimum quantity lubrication assisted milling process based on hybrid response surface methodology and multi-objective genetic algorithm

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ADVANCES IN MECHANICAL ENGINEERING 2019年第4期11卷

作者： Mumtaz, Jabir Li, Zhang Imran, Muhammad Yue, Lei Jahanzaib, Mirza Sarfraz, Shoaib Shehab, Essam Ismail, Sikiru Oluwarotimi Afzal, Kaynat Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Natl Univ Sci & Technol NUST Business Sch Dept Management & HR Islamabad Pakistan Univ Engn & Technol Dept Ind Engn Taxila Pakistan Cranfield Univ Mfg Dept Sch Aerosp Transport & Mfg Cranfield Beds England Univ Hertfordshire Sch Engn & Comp Sci Hatfield Herts England

Parametric modelling and optimisation play an important role in choosing the best or optimal cutting conditions and parameters during machining to achieve the desirable results. However, analysis of optimisation of minimum quantity lubrication-assisted milling process has not been addressed in detail. Minimum quantity lubrication method is very effective for cost reduction and promotes green machining. Hence, this article focuses on minimum quantity lubrication-assisted milling machining parameters on AISI 1045 material surface roughness and power consumption. A novel low-cost power measurement system is developed to measure the power consumption. A predictive mathematical model is developed for surface roughness and power consumption. The effects of minimum quantity lubrication and machining parameters are examined to determine the optimum conditions with minimum surface roughness and minimum power consumption. Empirical models are developed to predict surface roughness and power of machine tool effectively and accurately using response surface methodology and multi-objective optimisation genetic algorithm. Comparison of results obtained from response surface methodology and multi-objective optimisation genetic algorithm depict that both measured and predicted values have a close agreement. This model could be helpful to select the best combination of end-milling machining parameters to save power consumption and time, consequently, increasing both productivity and profitability.

关键词： Response surface methodology Box-Behnken design multi-objective genetic algorithm end-milling machiningparameters surface roughness power consumption

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