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A multi-objective genetic Algorithm for overlapping community detection based on edge encoding

INFORMATION SCIENCES 2018年 462卷 290-314页

作者： Bello-Orgaz, Gema Salcedo-Sanz, Sancho Camacho, David Univ Autonoma Madrid Comp Sci Dept Madrid Spain Univ Alcala Dept Signal Theory & Commun Madrid Spain

The Community Detection Problem (CDP) in Social Networks has been widely studied from different areas such as Data Mining, Graph Theory Physics, or Social Network Analysis, among others. This problem tries to divide a graph into different groups of nodes (communities), according to the graph topology. A partition is a division of the graph where each node belongs to only one community. However, a common feature observed in real-world networks is the existence of overlapping communities, where a given node can belong to more than one community. This paper presents a new multi-objective genetic Algorithm (MOGA-OCD) designed to detect overlapping communities, by using measures related to the network connectivity. For this purpose, the proposed algorithm uses a phenotype-type encoding based on the edge information, and a new fitness function focused on optimizing two classical objectives in CDP: the first one is used to maximize the internal connectivity of the communities, whereas the second one is used to minimize the external connections to the rest of the graph. To select the most appropriate metrics for these objectives, a comparative assessment of several connectivity metrics has been carried out using real-world networks. Finally, the algorithm has been evaluated against other well-known algorithms from the state of the art in CDP. The experimental results show that the proposed approach improves overall the accuracy and quality of alternative methods in CDP, showing its effectiveness as a new powerful algorithm for detecting structured overlapping communities. (C) 2018 Published by Elsevier Inc.

关键词： multi-objective genetic algorithms Overlapping community detection Graph clustering Network metrics Edge-based encoding

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An on-line variable fidelity metamodel assisted multi-objective genetic Algorithm for engineering design optimization

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APPLIED SOFT COMPUTING 2018年 66卷 438-448页

作者： Shu, Leshi Jiang, Ping Zhou, Qi Shao, Xinyu Hu, Jiexiang Meng, Xiangzheng Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Hubei Peoples R China

Population-based algorithms, which require a large number of fitness evaluations, can become computationally intractable when applied in engineering design optimization problems involving computational expensive simulations. To address this challenge, this paper proposes an on-line variable-fidelity meta model assisted multi-objective genetic Algorithm (OLVFM-MOGA) approach. In OLVFM-MOGA, the variable-fidelity metamodel (VFM) is constructed to replace the expensive simulation models to ease the computational burden. Besides, a novel model updating strategy is developed to address the issues of 1) which sample points should be sent for simulation analysis to improve the optimization quality, and 2) whether the low-fidelity (LF) model or the high-fidelity (HF) model should be selected to run for a selected sample point. Six numerical examples and an engineering case with different degrees of complexity are used to demonstrate the applicability and efficiency of the proposed approach. Results illustrate that the proposed OLVFM-MOGA is able to obtain comparable convergence and diversity of the Pareto frontier as to that obtained by MOGA with HF model, while at the same time significantly reducing the computational cost. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Engineering design optimization Fitness approximation Variable-fidelity metamodel multi-objective genetic algorithms Metamodel uncertainty

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Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on multi-objective genetic algorithms

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ENERGIES 2011年第3期4卷 532-544页

作者： Fang, Lincun Qin, Shiyin Xu, Gang Li, Tianli Zhu, Kemin Shenzhen Univ Coll Mechatron & Control Engn Shenzhen Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China

Compared to conventional vehicles Hybrid Electric Vehicles (HEVs) provide fairly high fuel economy with lower emissions. To enhance HEV performance in terms of fuel economy and emissions, and ensure user satisfaction with driving performance, the need for simultaneous optimization for the main parameters of powertrain components and control system is inevitable. However, this problem is challenging due to the large amount of coupling design parameters, conflicting design objectives and nonlinear constraints. Considering the defect of the methods which convert multi-objective optimization problems into single-objective ones, a comprehensive methodology based on the non-dominated sorting genetic algorithms II (NSGA II) to achieve parameter optimization for powertrain components and control system simultaneously and successfully find the Pareto-optimal solutions set is presented in this paper. A case simulation is carried out and simulated by ADVISOR, The simulation results show that this method can produce many Pareto-optimal solutions and a satisfactory solution can be selected by decision-makers according to their requirements. The results demonstrate the effectiveness of the algorithms proposed in this paper.

关键词： hybrid electric vehicles simultaneous optimization multi-objective genetic algorithms Pareto optimal solution

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Precast production scheduling using multi-objective genetic algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第7期38卷 8293-8302页

作者： Ko, Chien-Ho Wang, Shu-Fan Natl Pingtung Univ Sci & Technol Dept Civil Engn Pingtung 91201 Taiwan

The goal of production scheduling is to achieve a profitable balance among on-time delivery, short customer lead time, and maximum utilization of resources. However, current practices in precast production scheduling are fairly basic, depending heavily on experience, thereby resulting in inefficient resource utilization and late delivery. Moreover, previous methods ignoring buffer size between stations typically induce unfeasible schedules. Certain computational techniques have been proven effective in scheduling. To enhance precast production scheduling, this research develops a multi-objective precast production scheduling model (MOPPSM). In the model, production resources and buffer size between stations are considered. A multi-objective genetic algorithm is then developed to search for optimum solutions with minimum makespan and tardiness penalties. The performance of the proposed model is validated by using five case studies. The experimental results show that the MOPPSM can successfully search for optimum precast production schedules. Furthermore, considering buffer sizes between stations is crucial for acquiring reasonable and feasible precast production schedules. (C) 2011 Published by Elsevier Ltd.

关键词： Precast production Scheduling multi-objective genetic algorithms Buffer

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Application of multi-objective genetic algorithms to two case studies of reliability efficiency analysis and optimal expansion of electrical transmission networks

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2011年第O3期225卷 365-374页

作者： Cadini, F. Zio, E. Golea, L. R. Petrescu, C. A. Politecn Milan LASAR Dept Nucl Engn I-20133 Milan Italy Ecole Cent Paris Supelec Paris France

Two applications of multi-objective genetic algorithms to the analysis and optimization of electrical transmission networks are reported to show the potential of these combinatorial optimization schemes in the treatment of highly interconnected, complex systems. In a first case study, an analysis of the topological structure of an electrical power transmission system in the literature is carried out to identify the most important groups of elements of different sizes in the network. The importance is quantified in terms of group closeness centrality. In the second case study, an optimization method is developed for identifying strategies of expansion of an electrical transmission network by addition of new lines of connection which are optimally identified with respect to the objective of improving the transmission reliability, while limiting the investment cost.

关键词： critical infrastructures reliability efficiency group closeness centrality measure multi-objective genetic algorithms

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An improved genetic-fuzzy system for classification and data analysis

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 83卷 49-62页

作者： Lahsasna, Adel Seng, Woo Chaw Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur 50603 Malaysia

Interpretability of classification systems, which refers to the ability of these systems to express their behavior in an understandable way, has recently gained more attention and it is considered as an important requirement especially for knowledge-based systems. The main objective of this study is to improve the ability of a well-known fuzzy classifier proposed in Ishibuchi and Nojima (2007) to maximize the accuracy while preserve its interpretability. To achieve the above-mentioned objective, we propose two variants of the original fuzzy classifier. In the first variant classifier, the same components of the original classifier were used except NSGA-II which was replaced by an enhanced version called Controlled Elitism NSGA-II. This replacement aims at improving the ability of the first variant classifier to find non dominated solutions with better interpretability-accuracy trade-off. In the second variant classifier, we further improve the first variant classifier by enhancing the selection method of the antecedent conditions of the rules generated in the initial population of genetic algorithm. Unlike the method applied in the original classifier and the first variant classifier, which uses a random selection of the antecedent conditions, we proposed a feature-based selection method to favor the antecedent conditions associated with the most relevant features. The results show that the two variant classifiers find more non-dominated fuzzy rule-based systems with better generalization ability than the original method which suggests that Controlled Elitism NSGA-II algorithm is more efficient than NSGA-II. In addition, feature-based selection method applied in the second variant classifier allowed this method to successfully obtain high-quality solutions as it has consistently achieved the best error rates for all the data sets compared to the original method and the first variant classifier. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Fuzzy rule-based systems Interpretability multi-objective genetic algorithms NSGA-II

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A Fitness Approximation and On-Line Variable-Fidelity Metamodel Based multi-objective genetic Algorithm

A Fitness Approximation and On-Line Variable-Fidelity Metamo...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEE IEEM)

作者： Shu, Leshi Zhou, Qi Hu, Jiexiang Meng, Xiangzheng Jiang, Ping Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9781538609484

Population-based algorithms can become computationally intractable when applying in practical engineering design optimization involving computational expensive simulation. To address this challenge, this paper proposes an on-line variable-fidelity metamodel (VFM) assisted multi-objective genetic algorithms (OLVFM-MOGA) approach. In OLVFM-MOGA, the VFM integrates information from low-fidelity (LF) and high-fidelity (HF) models is constructed to replace the simulation model during the optimization process to ease the computational burden. Besides, a novel model updating strategy is developed to address the issues of 1) which individuals will be sent for running simulations. 2) whether the LF model or the HF model should be selected to run for a selected individual. The effectiveness and merits of the proposed OLVFM-MOGA approach are demonstrated on the design optimization problem of a torque arm.

关键词： Engineering design optimization fitness approximation variable-fidelity metamodel multi-objective genetic algorithms metamodel uncertainty

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Topology optimization of compliant structures and mechanisms using constructive solid geometry for 2-d and 3-d applications

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SOFT COMPUTING 2017年第5期21卷 1157-1179页

作者： Pandey, Anmol Datta, Rituparna Bhattacharya, Bishakh Indian Inst Technol Kanpur Dept Mech Engn Kanpur Uttar Pradesh India

This research focuses on the establishment of a constructive solid geometry-based topology optimization (CSG-TOM) technique for the design of compliant structure and mechanism. The novelty of the method lies in handling voids, non-design constraints, and irregular boundary shapes of the design domain, which are critical for any structural optimization. One of the most popular models of multi-objective genetic algorithm, non-dominated sorting genetic algorithm is used as the optimization tool due to its ample applicability in a wide variety of problems and flexibility in providing non-dominated solutions. The CSG-TOM technique has been successfully applied for 2-D topology optimization of compliant mechanisms and subsequently extended to 3-D cases. For handling these cases, a new software framework involving optimization routine for geometry and mesh generation with FEA solver has been developed. The efficacy of the approach has been demonstrated for 2-D and 3-D geometries and also compared with state of the art techniques.

关键词： Structural and topology optimization Finite element analysis (FEA) multi-objective genetic algorithms Compliant structures

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Computer-Aided Drafting of Urban Designs for Walkability 17th

Computer-Aided Drafting of Urban Designs for Walkability

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17th International Conference on Computational Science and its Applications (ICCSA)

作者： Blecic, Ivan Cecchini, Arnaldo Trunfio, Giuseppe A. Univ Cagliari Dept Civil & Environm Engn & Architecture Via Corte dAppello 87 I-09124 Cagliari Italy Univ Sassari Dept Architecture Planning & Design Palazzo Pou SalitPiazza Duomo 6 I-07041 Alghero Italy

ISBN: (纸本)9783319624013;9783319624006

We present a design support tool which generates outlines of urban projects targeting user-defined walkability objectives. The tool is part of our ongoing research effort to develop not only evaluative, but also generative tools, to be used during the design process, assisting architects and urban planners in designing more walkability of cities. The tool couples the capability-wise walkability score (CAWS) evaluation method with the NSGA-II multi-objective genetic algorithm, to generate a set of non-dominated solutions whose properties and expected effects can be explored within the software tool.

关键词： Walkability Urban design Design support systems Walkability explorer multi-objective genetic algorithms

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Social Trends in the Iterated Prisoner's Dilemma 17

Social Trends in the Iterated Prisoner's Dilemma

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genetic and Evolutionary Computation Conference (GECCO)

作者： Finocchiaro, Jessica Mathias, H. David Florida Southern Coll Dept Comp Sci Lakeland FL 33801 USA

ISBN: (纸本)9781450349390

In this paper, we utilize a multi-objective genetic algorithm (GA) to investigate the Iterated Prisoner's Dilemma problem with a population of players that don't have uniform objectives. Each of the members of our population has one of four objective pairs. We simulate a tournament similar to those in previous work to investigate patterns of convergence in objective pairs when they are free to change. We also consider the most successful objective pair within a population when members' objective pairs are fixed.

关键词： Prisoner's Dilemma multi-objective genetic algorithms

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