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An evolutionary multiobjective optimization Based Fuzzy Method for Overlapping Community Detection

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2020年第11期28卷 2841-2855页

作者： Tian, Ye Yang, Shangshang Zhang, Xingyi Anhui Univ Inst Phys Sci Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Anhui Univ Minist Educ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China

In the last decade, the detection of overlapping communities has received increasing attention in network science. Among various clustering techniques, the fuzzy clustering has been widely adopted in overlapping community detection, since the soft assignment provided by it naturally meets the overlapping between multiple communities. The crucial step of fuzzy-clustering-based overlapping community detection is to find the optimal community centers, so that the overlapping communities can be obtained according to the membership degrees between nodes and community centers. In this article, we propose an evolutionary multiobjective optimization-based fuzzy method for overlapping community detection. In contrast to traditional fuzzy clustering methods, the proposed method optimizes the community centers by using a specially tailored multiobjective evolutionary algorithm. Moreover, it can also find an appropriate fuzzy threshold for each node, so that diverse overlapping community structures can be uncovered. In the experiments, we compare the proposed method with six state-of-the-art overlapping community detection approaches on synthetic and real-world networks with different scales and characteristics. The statistical results demonstrate that the proposed method can obtain the best results on most test instances.

关键词： Complex network evolutionary multiobjective optimization fuzzy clustering overlapping community detection

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evolutionary multiobjective optimization algorithm for multimedia delivery in critical applications through Content-Aware Networks

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JOURNAL OF SUPERCOMPUTING 2017年第3期73卷 993-1016页

作者： Batalla, Jordi Mongay Mavromoustakis, Constandinos X. Mastorakis, George Negru, Daniel Borcoci, Eugen Seidor SA Barcelona Spain Natl Inst Telecommun Warsaw Poland Univ Nicosia Nicosia Cyprus Technol Educ Inst Crete Iraklion Greece Univ Bordeaux CNRS LaBRI Talence France Univ Politehn Bucuresti Bucharest Romania

Critical applications which need to deliver multimedia through the Internet, may achieve the required quality of service thanks to the Content-Aware Networks (CAN). The key element of CAN is an efficient decision algorithm responsible for the selection of the best content source and routing paths for content delivery. This paper proposes a two-phase decision algorithm, exploiting the evolutionary multiobjective optimization (EMO) approach. It allows to consider valid information in different time scales, adapting decision-maker to the evolving network and server conditions as well as to get the optimal solution in different shapes of Pareto front. The simulation experiments performed in a large-scale network model, confirm the effectiveness of the proposed two-phase EMO algorithm, comparing to other multi-criteria decision algorithms used in CAN.

关键词： Content-Aware Networks Critical applications evolutionary multiobjective optimization Multi-path multi-source Content server selection

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A Constrained Decomposition Approach With Grids for evolutionary multiobjective optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2018年第4期22卷 564-577页

作者： Cai, Xinye Mei, Zhiwei Fan, Zhun Zhang, Qingfu Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210023 Jiangsu Peoples R China Shantou Univ Sch Engn Guangdong Prov Key Lab Digital Signal & Image Pro Shantou 515063 Peoples R China Shantou Univ Sch Engn Dept Elect Engn Shantou 515063 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Hong Kong Peoples R China

Decomposition-based multiobjective evolutionary algorithms (MOEAs) decompose a multiobjective optimization problem (MOP) into a set of scalar objective subproblems and solve them in a collaborative way. Commonly used decomposition approaches originate from mathematical programming and the direct use of them may not suit MOEAs due to their population-based property. For instance, these decomposition approaches used in MOEAs may cause the loss of diversity and/or be very sensitive to the shapes of Pareto fronts (PFs). This paper proposes a constrained decomposition with grids (CDG) that can better address these two issues thus more suitable for MOEAs. In addition, different subproblems in CDG defined by the constrained decomposition constitute a grid system. The grids have an inherent property of reflecting the information of neighborhood structures among the solutions, which is a desirable property for restricted mating selection in MOEAs. Based on CDG, a constrained decomposition MOEA with grid (CDG-MOEA) is further proposed. Extensive experiments are conducted to compare CDG-MOEA with the domination-based, indicator-based, and state-of-the-art decomposition-based MOEAs. The experimental results show that CDG-MOEA outperforms the compared algorithms in terms of both the convergence and diversity. More importantly, it is robust to the shapes of PFs and can still be very effective on MOPs with complex PFs (e.g., extremely convex, or with disparately scaled objectives).

关键词： Constrained decomposition evolutionary multiobjective optimization grids robust to Pareto front (PF)

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A multicriteria integral framework for agent-based model calibration using evolutionary multiobjective optimization and network-based visualization

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DECISION SUPPORT SYSTEMS 2019年 124卷 113111-113111页

作者： Moya, Ignacio Chica, Manuel Cordon, Oscar Univ Granada Andalusian Res Inst DaSCI Data Sci & Computat Int E-18071 Granada Spain Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia

Automated calibration methods are a common approach to agent-based model calibration as they can estimate those parameters which cannot be set because of the lack of information. The modeler requires to validate the model by checking the parameter values before the model can be used and this task is very challenging when the model considers two or more conflicting outputs. We propose a multicriteria integral framework to assist the modeler in the calibration and validation of agent-based models that combines evolutionary multiobjective optimization with network-based visualization, which we believe is the first integral approach to model calibration. On the one hand, evolutionary multiobjective optimization provides several sets of calibration solutions (i.e., parameter values) with different trade-offs for the considered objectives in a single run. On the other hand, network-based visualization is used to better understand the decision space and the set of solutions from the obtained Pareto set approximation. To illustrate our proposal, we face the calibration of three agent-based model examples for marketing which consider two conflicting criteria: the awareness of the brand and its word-of-mouth volume. The final analysis of the calibrated solutions shows how our proposed framework eases the analysis of Pareto sets with high cardinality and helps with the identification of flexible solutions (i.e., those having close values in the design space).

关键词： Agent-based modeling Model calibration evolutionary multiobjective optimization Information visualization

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A Clustering-Based Adaptive evolutionary Algorithm for multiobjective optimization With Irregular Pareto Fronts

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IEEE TRANSACTIONS ON CYBERNETICS 2019年第7期49卷 2758-2770页

作者： Hua, Yicun Jin, Yaochu Hao, Kuangrong Donghua Univ Engn Res Ctr Digitized Text & Apparel Technol Minist Educ Shanghai 201620 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Taiyuan Univ Sci & Technol Dept Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

Existing multiobjective evolutionary algorithms (MOEAs) perform well on multiobjective optimization problems (MOPs) with regular Pareto fronts in which the Pareto optimal solutions distribute continuously over the objective space. When the Pareto front is discontinuous or degenerated, most existing algorithms cannot achieve good results. To remedy this issue, a clustering-based adaptive MOEA (CA-MOEA) is proposed in this paper for solving MOPs with irregular Pareto fronts. The main idea is to adaptively generate a set of cluster centers for guiding selection at each generation to maintain diversity and accelerate convergence. We investigate the performance of CA-MOEA on 18 widely used benchmark problems. Our results demonstrate the competitiveness of CA-MOEA for multiobjective optimization, especially for problems with irregular Pareto fronts. In addition, CA-MOEA is shown to perform well on the optimization of the stretching parameters in the carbon fiber formation process.

关键词： Carbon fiber evolutionary multiobjective optimization hierarchical clustering irregular Pareto front

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An Improvement Study of the Decomposition-Based Algorithm Global WASF-GA for evolutionary multiobjective optimization 18th

An Improvement Study of the Decomposition-Based Algorithm Gl...

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18th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA)

作者： Gonzalez-Gallardo, Sandra Saborido, Ruben Ruiz, Ana B. Luque, Mariano Univ Malaga Programa Doctorado Econ & Empresa C Ejido 6 E-29071 Malaga Spain Concordia Univ Dept Comp Sci & Software Engn 1455 Maisonneuve Blvd West Montreal PQ H3G 1M8 Canada Univ Malaga Dept Appl Econ Math C Ejido 6 E-29071 Malaga Spain

ISBN: (纸本)9783030003746;9783030003739

The convergence and the diversity of the decomposition-based evolutionary algorithm Global WASF-GA (GWASF-GA) relies on a set of weight vectors that determine the search directions for new non-dominated solutions in the objective space. Although using weight vectors whose search directions are widely distributed may lead to a well-diversified approximation of the Pareto front (PF), this may not be enough to obtain a good approximation for complicated PFs (discontinuous, non-convex, etc.). Thus, we propose to dynamically adjust the weight vectors once GWASF-GA has been run for a certain number of generations. This adjustment is aimed at re-calculating some of the weight vectors, so that search directions pointing to overcrowded regions of the PF are redirected toward parts with a lack of solutions that may be hard to be approximated. We test different parameters settings of the dynamic adjustment in optimization problems with three, five, and six objectives, concluding that GWASF-GA performs better when adjusting the weight vectors dynamically than without applying the adjustment.

关键词： evolutionary multiobjective optimization Decomposition-based algorithm GWASF-GA Weight vector

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multiobjective evolutionary Data Mining for Performance Improvement of evolutionary multiobjective optimization

Multiobjective Evolutionary Data Mining for Performance Impr...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Nojima, Yusuke Tanigaki, Yuki Masuyama, Naoki Ishibuchi, Hisao Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan Southern Univ Sci & Technol SUSTech Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9781538666500

In recent years, evolutionary multiobjective optimization (EMO) algorithms have frequently been used for engineering problems with some conflicting objective functions to be simultaneously optimized EMO algorithms can provide a number of Pareto optimal solutions to users. Two scenarios are considered in the practical use of EMO algorithms. One is that a decision maker selects a single solution from the obtained ones after the EMO process. The other is that a decision maker utilizes the solutions to analyze the relationship between design variables and objective functions of the corresponding problem. In this paper, we apply fuzzy genetics-based machine learning to the second scenario in order to generate if-then rule-based classifiers which represent the relationship between design variables and objective functions. We also utilize this method during the EMO process to pre-screen candidate offspring solutions. The classifier detects non-promising offspring solutions. Then, they are discarded before their fitness evaluation, so that the computation resource is used only for promising solutions. We apply this method to one engineering problem and examine its effect on the search performance of an EMO algorithm.

关键词： evolutionary multiobjective optimization data mining multiobjective fuzzy genetics-based machine learning pattern classification

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evolutionary multiobjective optimization for the Pickup and Delivery Problem with Time Windows and Demands

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MOBILE NETWORKS & APPLICATIONS 2016年第1期21卷 175-190页

作者： Phan, Dung H. Suzuki, Junichi Univ Massachusetts Dept Comp Sci Boston MA 02125 USA 100 Morrissey Blvd Boston MA 02125 USA

This paper studies an evolutionary algorithm to solve a new multiobjective optimization problem, the Pickup and Delivery Problem with Time Windows and Demands (PDP-TW-D), which is applicable to operational optimization in various mobile network systems. With respect to multiple optimization objectives, PDP-TW-D is to find a set of Pareto-optimal routes for a fleet of vehicles (e.g., mobile robots, drones and autonomous heavy-haulage trucks) in order to serve given transportation requests. The proposed algorithm uses a population of individuals, each of which represents a solution candidate, and evolves them through generations to seek the Pareto-optimal solutions. In addition to the evolution-based global search process, the proposed algorithm allows individuals to improve their optimality in each generation with a local search process, which is designed based on iterative neighborhood search. Experimental results demonstrate that the integration of global and local search processes improves the optimality of individuals and expedites convergence speed. The proposed algorithm outperforms two well-known existing EMOAs, NSGA-II and MOEA/D, in relatively large-scale problems that have up to 400 pickup and delivery locations.

关键词： evolutionary multiobjective optimization Genetic algorithms Pickup and delivery problem with time windows and demands

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multiobjective Data Mining from Solutions by evolutionary multiobjective optimization 17

Multiobjective Data Mining from Solutions by Evolutionary Mu...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Nojima, Yusuke Tanigaki, Yuki Ishibuchi, Hisao Osaka Prefecture Univ Sakai Osaka Japan Southern Univ Sci & Technol SUSTech Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781450349208

One research direction in the field of evolutionary multiobjective optimization (EMO) is a post-analytical process of non-dominated solutions in order to analyze the relationship between design variables and objective functions for optimization problems. For this purpose, data mining techniques have been used in some studies. From a practical point of view, this process itself should be considered as a multiobjective optimization problem. In this paper, multiobjective genetic fuzzy rule selection is applied to the post-analytical process of solutions obtained by EMO algorithms. First, multiple regions of interest are specified in the objective space. Each region with a number of solutions is handled as a different class. A set of patterns is generated by the labeled solutions. Second, a number of fuzzy if-then rules are generated by classification rule mining. Finally, an EMO algorithm is applied to combinatorial optimization of fuzzy if-then rules in order to obtain a number of non-dominated fuzzy classifiers with respect to accuracy and complexity. Through computational experiments using two engineering problems, we show that we can obtain various classifiers with a variety of complexity-accuracy tradeoff.

关键词： evolutionary multiobjective optimization data mining genetic fuzzy rule selection pattern classification

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GECCO 2016 Tutorial on evolutionary multiobjective optimization

GECCO 2016 Tutorial on Evolutionary Multiobjective Optimizat...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Brockhoff, Dimo Wagner, Tobias INRIA Lille Nord Europe DOLPHIN Team Parc Sci Haute Borne 40Ave Halley Bat B Pk Plaza F-59650 Villeneuve Dascq France Tech Univ Dortmund Inst Machining Technol ISF MB 3Baroper Str 303 D-44227 Dortmund Germany

ISBN: (纸本)9781450343237

Many optimization problems are multiobjective in nature in the sense that multiple, conflicting criteria need to be optimized simultaneously. Due to the conflict between objectives, usually, no single optimal solution exists. Instead, the optimum corresponds to a set of so-called Pareto-optimal solutions for which no other solution has better function values in all objectives. evolutionary multiobjective optimization (EMO) algorithms are widely used in practice for solving multiobjective optimization problems due to several reasons. As randomized blackbox algorithms, EMO approaches allow to tackle problems with nonlinear, nondifferentiable, or noisy objective functions. As set-based algorithms, they allow to compute or approximate the full set of Pareto-optimal solutions in one algorithm run - opposed to classical solution-based techniques from the multicriteria decision making (MCDM) field. Using EMO approaches in practice has two other advantages: they allow to learn about a problem formulation, for example, by automatically revealing common design principles among (Pareto-optimal) solutions (innovization) and it has been shown that certain single-objective problems become easier to solve with randomized search heuristics if the problem is reformulated as a multiobjective one (multiobjectivization). This tutorial aims at giving a broad introduction to the EMO field and at presenting some of its recent research results in more detail. More specifically, we are going to (i) introduce the basic principles of EMO algorithms in comparison to classical solution-based approaches, (ii) show a few practical examples which motivate the use of EMO in terms of the mentioned innovization and multiobjectivization principles, and (iii) present a general overview of state-of-the-art algorithms and techniques. Moreover, we will present some of the most important research results in areas such as indicator-based EMO, preference articulation, surrogate-assisted EMO, and performance

关键词： tutorial multiobjective optimization evolutionary multiobjective optimization

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