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

INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2013年第2-3期47卷 280-288页

作者： Wang, Xiaojuan Li, Wenfeng Zhang, Ying Wuhan Univ Technol Sch Logist Engn Wuhan Peoples R China

In many real-world applications, processing time may vary dynamically due to human factors or operating faults and there are some other uncertain factors in the scheduling problems. In this paper, fuzzy sets are used to model uncertain processing time and due date. In addition, an improved multi-objective genetic algorithm is presented to solve the multi-objective fuzzy Flexible Job-shop Scheduling Problem (FJSP). About this improved multi-objective genetic algorithm, Pareto-optimality is applied, including the non-dominated sorting scheme and an improved elite reservation strategy based on NSGA-II. Meanwhile, the immune and entropy principle is used to preserve the diversity of individuals. Moreover, the advanced crossover and the mutation operators are used to adapt to the special chromosome structure. The computational results demonstrate the effectiveness of the proposed algorithm.

关键词： fuzzy FJSP multi-objective genetic algorithm pareto-optimality immune and entropy principle

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Selection of security system design via games of imperfect information and multi-objective genetic algorithm

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2013年 112卷 59-66页

作者： Lins, Isis Didier Rego, Leandro Chaves Moura, Marcio das Chagas Droguett, Enrique Lopez Univ Fed Pernambuco UFPE CTG CEERMA BR-50740550 Recife PE Brazil Univ Fed Pernambuco UFPE DEP CTG BR-50740530 Recife PE Brazil Univ Fed Pernambuco UFPE DE CCEN BR-50740540 Recife PE Brazil

This work analyzes the strategic interaction between a defender and an intelligent attacker by means of a game and reliability framework involving a multi-objective approach and imperfect information so as to support decision-makers in choosing efficiently designed security systems. A multi-objective genetic algorithm is used to determine the optimal security system's configurations representing the tradeoff between the probability of a successful defense and the acquisition and operational costs. Games with imperfect information are considered, in which the attacker has limited knowledge about the actual security system. The types of security alternatives are readily observable, but the number of redundancies actually implemented in each security subsystem is not known. The proposed methodology is applied to an illustrative example considering power transmission lines in the Northeast of Brazil, which are often targets for attackers who aims at selling the aluminum conductors. The empirical results show that the framework succeeds in handling this sort of strategic interaction. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Selection of compromise solutions Security system design Game of imperfect information multi-objective genetic algorithm

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An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2013年第1期26卷 114-126页

作者： Peiravi, Ali Mashhadi, Habib Rajabi Javadi, S. Hamed Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran

In this study, an optimal method of clustering homogeneous wireless sensor networks using a multi-objective two-nested genetic algorithm is presented. The top level algorithm is a multi-objective genetic algorithm (GA) whose goal is to obtain clustering schemes in which the network lifetime is optimized for different delay values. The low level GA is used in each cluster in order to get the most efficient topology for data transmission from sensor nodes to the cluster head. The presented clustering method is not restrictive, whereas existing intelligent clustering methods impose certain conditions such as performing two-tiered clustering. A random deployed model is used to demonstrate the efficiency of the proposed algorithm. In addition, a comparison is made between the presented algorithm other GA-based clustering methods and the Low Energy Adaptive Clustering Hierarchy protocol. The results obtained indicate that using the proposed method, the network's lifetime would be extended much more than it would be when using the other methods. Copyright (C) 2011 John Wiley & Sons, Ltd.

关键词： clustering energy-efficient algorithm multi-objective genetic algorithm wireless sensor network

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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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Marine shaft optimization using surrogate models and multi-objective optimization

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STRUCTURES 2024年 63卷

作者： Nickabadi, Saeid Hosseini, Seyed Mohammadreza Bathaee, Hasan Alirezaeipour, Saeid Univ Imam Khomeini Marine Sci Fac Mech Engn Nowshahr Iran Univ Tehran Fac Mech Engn Tehran Iran Univ Tarbiat Modares Fac Mech Engn Tehran Iran

This paper aims to obtain an application software using Ansys software in which a static, dynamic, and modal analysis for the power transmission of ships is presented. The objective functions of this research are maximum static stress, maximum dynamic stress, shaft mass, and modal shaft frequency analysis. A complete, comprehensive, and generalizable model for metal shaft types is presented. Then, using the kriging response surface and multi-objective genetic algorithm (NSGA II), these goals are optimized simultaneously. In this research, the optimization parameters are geometric parameters of the shaft design, including the position of the supports and the inner and outer diameters of the shaft. As a result of this optimization, the shaft's maximum static and dynamic stresses are reduced by 36% and 42%, respectively. The shaft's mass, which is one of the most critical factors during the shaft and its vibrations, is reduced by about 9%. In this regard, the first natural frequency of the optimized shaft increased by about 28.5% compared to the initial shaft.

关键词： Ship propulsion shaft Metal shaft Static and dynamic analysis Modal analysis Optimization Kriging response level multi-objective genetic algorithm

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FG-HFS: A feature filter and group evolution hybrid feature selection algorithm for high-dimensional gene expression data

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 245卷

作者： Xu, Zhaozhao Yang, Fangyuan Tang, Chaosheng Wang, Hong Wang, Shuihua Sun, Junding Zhang, Yudong Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo 454000 Henan Peoples R China Henan Polytech Univ Dept Gynecol Oncol Affiliated Hosp 1 Jiaozuo 454000 Henan Peoples R China Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH England Xian Jiaotong Liverpool Univ Dept Biol Sci Suzhou 215123 Jiangsu Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Jiangsu Peoples R China

High dimensional and small samples characterize gene expression data and contain a large number of genes unrelated to disease. Feature selection improves the efficiency of disease diagnosis by selecting a small number of important genes. Unfortunately, existing algorithms do not consider the correlation between features, and search algorithms tend to fall into the local optimal solution in the feature search process. To this end, this paper proposes a feature filter and group evolution hybrid feature selection algorithm (FG-HFS) for high-dimensional gene expression data. Unlike existing algorithms, we propose using spectral clustering to group redundant features into a group. Then, we propose a redundant feature filter algorithm. According to the principle of approximate Markov blanket, grouped feature groups are filtered to delete these redundant features. Among them, filtered features are evenly divided by density according to the feature exponential strategy. Most importantly, we propose using the group evolution multi-objective genetic algorithm to search the filtered feature subsets and evaluate the candidate feature subsets according to the in-group and out-group so as to select the feature subsets with the highest accuracy and the least number. Experimental results show that the average accuracy (ACC) and Matthews correlation coefficient (MCC) indexes of the selected feature subsets (FSs) by the FG-HFS algorithm on 5 gene expression datasets are 92.76% and 88.76%, respectively, which are significantly better than the existing algorithms. In addition, the FSs and ACC/FSs indexes of the FG-HFS algorithm are also better than the existing algorithms, which fully proves the superiority of the FG- HFS algorithm. More importantly, the Wilcoxon and Friedman statistical experiments results show that the feature selection effect of FG-HFS algorithm is significantly better than that of existing algorithms, no matter in pairwise comparison or multiple comparison.

关键词： Gene expression data Feature selection Spectral clustering Symmetric uncertainty multi-objective genetic algorithm

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Study of multiobjective genetic algorithm on Taylor External Fixation

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IEEE ACCESS 2022年 10卷 46986-46999页

作者： Guo, Jianmin Su, Jialong Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China

The rapid development of today's society and the rapidly increasing incidence of skeletal injuries caused by traffic and industrial accidents have led to a significant increase in the number of patients with fractures accompanied by severe soft tissue injuries, as well as an increased incidence of osteomyelitis and bone defects. Many of these patients cannot be treated with internal fixators and can only be treated with external fixators. The Taylor Bone External Fixator is the most advanced bone external fixation brace in the field of orthopedics, in which the algorithm study of the forward and backward solution of the computer software that accompanies the Taylor Bone External Fixator is the key. In this paper, we analyze the positional kinematic model based on the Taylor Spatial Frame and derive the equations for solving the six positional parameters of the fracture segment in the dynamic platform with this model. A multi-objective genetic algorithm and Pareto optimization theory are combined to propose a solution to the kinematic positive solution problem. The algorithm proposed in this paper has a minimum accuracy improvement of about 0.8 mm compared to the conventional Newton-Raphson in terms of the accuracy of the Taylor frame mounting parameters solution. Finally, based on the human tibial fracture as the test object, the healing process of the tibial fracture end was simulated using the prescription data generated by the orthopedic system, and the experimental results proved the accuracy and feasibility of the software system, and achieved the expected orthopedic effect.

关键词： Kinematics Heuristic algorithms Bones genetic algorithms Mathematical models Optimization Strain Taylor bone external fixator multi-objective genetic algorithm Pearson correlation coefficient kinematic positive solution correction prescription

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Ensembled local mean decomposition and genetic algorithm approach to investigate tool chatter features at higher metal removal rate

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JOURNAL OF VIBRATION AND CONTROL 2022年第1-2期28卷 30-44页

作者： Gupta, Pankaj Singh, Bhagat Jaypee Univ Engn & Technol Guna 473226 Madhya Pradesh India

Improper selection of cutting parameters leads to regenerative chatter and loss in productivity. In the present work, a methodology has been proposed to select a proper combination of input cutting parameters for stable turning with improved metal removal rate. Chatter signals generated during the turning of Al6061-T6 have been acquired using a microphone. Stability lobes diagram has been plotted to access the stability regime. Further, to study the effect of feed rate on stability, the recorded signals have been processed using local mean decomposition signal processing technique, followed by the selection of dominating product functions using Fourier transform. The decomposed signals have been used to evaluate the new output parameter, that is, chatter index. Prediction models of chatter index and metal removal rate have been developed. Moreover, these prediction models have been optimized using multi-objective genetic algorithm for ascertaining the optimal range of cutting parameters for stable turning with higher metal removal rate. Finally, obtained stable range has been validated by performing more experiments.

关键词： Tool chatter local mean decomposition nonstationary signal processing response surface methodology multi-objective genetic algorithm Nakagami distribution

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multi-objective optimization of comprehensive performance in airfoil channel PCHEs using Bezier curves

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APPLIED THERMAL ENGINEERING 2025年 261卷

作者： Liu, Rui Zhang, Chuanliang Zhao, Bin Chen, Jiaxiang Wang, Ziyi Changsha Univ Sci & Technol Coll Energy & Power Engn Changsha 410114 Peoples R China Changsha Univ Sci & Technol Sch Elect & Informat Engn Changsha 410114 Peoples R China

The airfoil fin (AFF) Printed circuit heat exchanger (PCHE) has attracted significant attention for its excellent comprehensive performance. This study proposes an optimized design for AFF PCHE to enhance the comprehensive performance by integrating Bezier curves, computational fluid dynamics (CFD), and multi-objective genetic algorithm (MOGA). A set of 12 Bezier curve-based variables is utilized to define and control the airfoil geometry, with optimization targets set on two comprehensive evaluation criteria: the first enhanced ratio (n1) and the third enhanced ratio (n3). The MOGA-generated Pareto front reveals the evolution of AFF structures in relation to n1 and n3. Results show that as the leading and trailing edges of the AFFs become sharper and the thickness decreases, the n1 of the PCHE channel gradually increases, while n3 decreases. Conversely, as the thickness of the AFFs increases and the trailing edge shape transitions from blunt to elliptical and finally to round, n3 significantly increases while n1 decreases. Furthermore, when changes focus mainly on the leading edge of the AFFs, n3 improves without markedly affecting n1. Compared to the traditional airfoil channel, the n1 of the Fin-b channel increases by 3.1%-10.8%, demonstrating its greater suitability under identical flow rate conditions. Similarly, the n3 of the Fin-g channel is 1.4%-11.6% higher than that of the traditional airfoil channel, highlighting its superior performance under identical pumping power conditions. The present work provides a valuable reference for optimizing the design of AFF PCHEs under identical flow rate and pumping power conditions.

关键词： Airfoil fin PCHE Bezier curves Pareto front multi-objective genetic algorithm Comprehensive performance

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Hybrid modeling and optimization of fiber laser hole cutting of austenitic stainless-steel sheets using response surface

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LASER PHYSICS 2024年第4期34卷 045602-045602页

作者： Niu, Zhenhua Dehkordi, Mohammad Hossein Razavi Ghazi, Mohammadreza Akbari, Mohammad Coll Electromech Engn Anyang Vocat & Tech Coll Anyang 455000 Peoples R China Islamic Azad Univ Dept Mech Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Aerosp & Energy Convers Res Ctr Najafabad Branch Najafabad Iran

In this study, an efficient approach was proposed to systematically model and optimize the laser small hole cutting process parameters using a hybrid approach for the design of experiment and multi-objective genetic algorithm optimization. The central composite design and response surface methodology were used to effectively model the impact of four main factors: cutting speed, laser power, gas pressure and focal distance on the responses. The responses considered were hole diameter circularity tolerance, spattering and cut kerf width, which were used to evaluate the quality of the laser hole cutting. The regression equations were used to model the effect of process parameters and their interactions on the responses. These regression models were then used as objective functions for optimization. The results show that the focal distance and laser power have had a significant influence on the hole diameter circularity tolerance and the variation in size of the cut kerf. In particular, the melted material spattering rate increased threefold when the focal distance increased from 0.4 to 0.8 mm. The optimization results highlighted that the best outcomes in terms of minimum deviation, spatter, and the cut-kerf width were achieved at low power (between 605 and 685 W) and low speeds (in the range of 11.1-12.7 m min-1). The optimal focal distance for all solutions was found to be 0 mm for the gas pressure (between 6.5 and 8 bars) to minimize the objective functions.

关键词： laser hole cutting response surface methodology multi-objective genetic algorithm dimensional tolerances optimization

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