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Large-Scale Experimental Evaluation of Cluster Representations for multiobjective evolutionary Clustering

IEEE TRANSACTIONS ON evolutionary COMPUTATION 2014年第1期18卷 36-53页

作者： Garcia-Piquer, Alvaro Fornells, Albert Bacardit, Jaume Orriols-Puig, Albert Golobardes, Elisabet Inst Space Sci Bellaterra 08193 Spain La Salle Univ Ramon Llull Grp Recerca Sistemes Intelligents Barcelona 08022 Spain Newcastle Univ Sch Comp Sci Newcastle Upon Tyne NE1 7RU Tyne & Wear England

multiobjective evolutionary clustering algorithms are based on the optimization of several objective functions that guide the search following a cycle based on evolutionary algorithms. Their capabilities allow them to find better solutions than with conventional clustering algorithms if the suitable individual representation is selected. This paper provides a detailed analysis of the three most relevant and useful representations-prototype-based, label-based, and graph-based-through a wide set of synthetic data sets. Moreover, they are also compared to relevant conventional clustering algorithms. Experiments show that multiobjective evolutionary clustering is competitive with regard to other clustering algorithms. Furthermore, the best scenario for each representation is also presented.

关键词： Clustering data mining multiobjective evolutionary algorithms

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multiobjective genetic algorithms applied to solve optimization problems

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IEEE TRANSACTIONS ON MAGNETICS 2002年第2期38卷 1133-1136页

作者： Dias, AHF de Vasconcelos, JA Acesita Co BR-180000 Timoteo Minas Geras Brazil Univ Fed Minas Gerais Dept Elect Engn BR-31270901 Belo Horizonte MG Brazil

In this paper, we discuss multiobjective optimization problems solved by evolutionary algorithms. We present the nondominated sorting genetic algorithm (NSGA) to solve this class of problems and its performance is analyzed in comparing its results with those obtained with four others algorithms. Finally, the NSGA is applied to solve the TEAM benchmark problem 22 without considering the quench physical condition to map the Pareto-optimum front. The results in both analytical and electromagnetic problems show its effectiveness.

关键词： electromagnetics multiobjective evolutionary algorithms nondominated sorting genetic algorithms

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multiobjective optimization based on reputation

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INFORMATION SCIENCES 2014年 286卷 125-146页

作者： Jiang, Siwei Zhang, Jie Ong, Yew-Soon Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Singapore Inst Mfg Technol SIMTech Singapore Singapore

To improve the robustness and ease-of-use of evolutionary algorithms (EM), adaptation on evolutionary operators and control parameters shows significant advantages over fixed operators with default parameter settings. To date, many successful research efforts to adaptive EAs have been devoted to Single-objective Optimization Problems (SOPs), whereas, few studies have been conducted on multiobjective Optimization Problems (MOPS). Directly inheriting the adaptation mechanisms of SOPs in the MOPs context faces challenges due to the intrinsic differences between these two kinds of problems. To fill in this gap, in this paper, a novel multiobjective evolutionary Algorithm (MOEA) based on reputation is proposed as a unified framework for general MOEAs. The reputation concept is introduced for the first time to measure the dynamic competency of evolutionary operators and control parameters across problems and stages of the search in MOEAs. Based on the notion of reputation, individual solutions then select highly reputable evolutionary operators and control parameters. Experimental studies on 58 benchmark MOPs in jMetal confirm its superior performance over the classical MOEAs and other adaptive MOEAs. (C) 2014 Elsevier Inc. All rights reserved.

关键词： multiobjective optimization multiobjective evolutionary algorithms Adaption Reputation jMetal

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multiobjective Optimal Reactive Power Flow Using Elitist Nondominated Sorting Genetic Algorithm: Comparison and Improvement

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2010年第1期5卷 70-78页

作者： Li, Zhihuan Li, Yinhong Duan, Xianzhong Huazhong Univ Sci & Technol Dept Elect & Elect Engn Wuhan Peoples R China

Elitist nondominated sorting genetic algorithm (NSGA-II) is adopted and improved for multiobjective optimal reactive power flow (ORPF) problem. multiobjective ORPF, formulated as a multiobjective mixed integer nonlinear optimization problem, minimizes real power loss and improves voltage profile of power grid by determining reactive power control variables. NSGA-II-based ORPF is tested on standard IEEE 30-bus test system and compared with four other state-of-the-art multiobjective evolutionary algorithms (MOEAs). Pareto front and outer solutions achieved by the five MOEAs are analyzed and compared. NSGA-II obtains the best control strategy for ORPF, but it suffers from the lower convergence speed at the early stage of the optimization. Several problem-specific local search strategies (LSSs) are incorporated into NSGA-II to promote algorithm's exploiting capability and then to speed up its convergence. This enhanced version of NSGA-II (ENSGA) is examined on IEEE 30 system. Experimental results show that the use of LSSs clearly improved the performance of NSGA-II. ENSGA shows the best search efficiency and is proved to be one of the efficient potential candidates in solving reactive power optimization in the real-time operation systems.

关键词： Optimal reactive power flow Elitist nondominated sorting genetic algorithm Pareto optimal multiobjective evolutionary algorithms

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multiobjective evolutionary optimization of traffic flow and pollution in Montevideo, Uruguay

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APPLIED SOFT COMPUTING 2018年 70卷 472-485页

作者： Peres, Matias Ruiz, German Nesmachnow, Sergio Olivera, Ana C. Univ Republica Montevideo Uruguay CONICET CIT GSJ Caleta Olivia Argentina

Traffic congestion and pollution are important problems in modern cities. As improving traffic flow via infrastructure modifications is expensive and intrusive, approaches using simulations emerge as economic alternatives to test different policies, with less negative impact on cities. This article proposes a specific methodology combining simulation and multiobjective evolutionary methods to simultaneously optimize traffic flow and vehicular emissions via traffic lights planning in urban areas. The experimental evaluation is performed over three real areas in Montevideo (Uruguay). Significant improvements on travel times and pollution are reported over the current configuration of traffic lights cycles and also over other traffic regulation techniques. Moreover, the multiobjective approach provides policy-makers with a set of alternatives to choose from, allowing the evaluation of several scenarios and the dynamic modification of traffic light cycles. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Traffic flow Pollution multiobjective evolutionary algorithms Simulation

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multiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions

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SOFT COMPUTING 2011年第12期15卷 2303-2318页

作者： Alcala, Rafael Nojima, Yusuke Herrera, Francisco Ishibuchi, Hisao Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Naka Ku Osaka 5998531 Japan

multiobjective genetic fuzzy rule selection is based on the generation of a set of candidate fuzzy classification rules using a preestablished granularity or multiple fuzzy partitions with different granularities for each attribute. Then, a multiobjective evolutionary algorithm is applied to perform fuzzy rule selection. Since using multiple granularities for the same attribute has been sometimes pointed out as to involve a potential interpretability loss, a mechanism to specify appropriate single granularities at the rule extraction stage has been proposed to avoid it but maintaining or even improving the classification performance. In this work, we perform a statistical study on this proposal and we extend it by combining the single granularity-based approach with a lateral tuning of the membership functions, i.e., complete contexts learning. In this way, we analyze in depth the importance of determining the appropriate contexts for learning fuzzy classifiers. To this end, we will compare the single granularity-based approach with the use of multiple granularities with and without tuning. The results show that the performance of the obtained classifiers can be even improved by obtaining the appropriate variable contexts, i.e., appropriate granularities and membership function parameters.

关键词： Fuzzy rule-based classifiers multiobjective evolutionary algorithms Granularity learning Lateral tuning of membership functions

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multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

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SOFT COMPUTING 2010年第6期14卷 579-597页

作者： Demir, G. Nildem Uyar, A. Sima Gunduz-Oguducu, Sule Istanbul Tech Univ Dept Comp Engn TR-34469 Istanbul Turkey

In this study, we experiment with several multiobjective evolutionary algorithms to determine a suitable approach for clustering Web user sessions, which consist of sequences of Web pages visited by the users. Our experimental results show that the multiobjective evolutionary algorithm-based approaches are successful for sequence clustering. We look at a commonly used cluster validity index to verify our findings. The results for this index indicate that the clustering solutions are of high quality. As a case study, the obtained clusters are then used in a Web recommender system for representing usage patterns. As a result of the experiments, we see that these approaches can successfully be applied for generating clustering solutions that lead to a high recommendation accuracy in the recommender model we used in this paper.

关键词： multiobjective clustering multiobjective evolutionary algorithms Sequence clustering Graph clustering User session clustering

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A multiobjective Genetic Algorithm to Find Communities in Complex Networks

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2012年第3期16卷 418-430页

作者： Pizzuti, Clara Natl Res Council Italy Inst High Performance Comp & Networking I-87036 Cosenza Italy

A multiobjective genetic algorithm to uncover community structure in complex network is proposed. The algorithm optimizes two objective functions able to identify densely connected groups of nodes having sparse inter-connections. The method generates a set of network divisions at different hierarchical levels in which solutions at deeper levels, consisting of a higher number of modules, are contained in solutions having a lower number of communities. The number of modules is automatically determined by the better tradeoff values of the objective functions. Experiments on synthetic and real life networks show that the algorithm successfully detects the network structure and it is competitive with state-of-the-art approaches.

关键词： Complex networks multiobjective clustering multiobjective evolutionary algorithms

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multiobjective meta-heuristic with iterative parameter distribution estimation for aeroelastic design of an aircraft wing

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ENGINEERING WITH COMPUTERS 2022年第1期38卷 695-713页

作者： Wansasueb, Kittinan Pholdee, Nantiwat Panagant, Natee Bureerat, Sujin Khon Kaen Univ Dept Mech Engn Sustainable Infrastructure Res & Dev Ctr Fac Engn Khon Kaen 40002 Thailand

This paper proposes a new self-adaptive meta-heuristic (MH) algorithm for multiobjective optimisation. The adaptation is accomplished by means of estimation of distribution. The differential evolution reproduction strategy is modified and used in this dominance-based multiobjective optimiser whereas population-based incremental learning is used to estimate the control parameters. The new method is employed to solve aeroelastic multiobjective optimisation of an aircraft wing which optimises structural weight and flutter speed. Design variables in the aeroelastic design problem include thicknesses of ribs, spars and composite layers. Also, the ply orientation of the upper and lower composite skins are assigned as the design variables. Additional benchmark test problems are also use to validate the search performance of the proposed algorithm. The performance validation reveals that the proposed optimiser is among the state-of-the-art multiobjective meta-heuristics. The concept of using estimation of distribution algorithm for tuning meta-heuristic control parameters is efficient and effective and becomes a new direction for improving MH performance.

关键词： Aeroelasticity Aircraft wing Surrogate models multiobjective evolutionary algorithms Estimation of distribution algorithm

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A multiobjective optimization of a UWB antenna using a self organizing genetic algorithm

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2012年第8期54卷 1824-1828页

作者： Silva, C. R. M. Lins, H. W. C. Martins, S. R. Barreto, E. L. F. d'Assuncao, A. G. Fed Univ Rio Grande do Norte UFRN Dept Commun Engn BR-59072970 Natal RN Brazil

This article presents the optimization process of a microstrip antenna designed to operate with ultra-wideband applications. This optimization process was accomplished by applying a self organizing multiobjective genetic algorithm (GA) to optimize a geometry aspect of a ring monopole antenna (a slit in the ground plane) considering three objectives simultaneously (bandwidth, return loss and central frequency deviation). Each of these objectives are modeled as dependent of two variables (the slit dimensions) and used in a single weighted compound aggregate objective function in which weights are defined by an adjuster GA that uses another GA to evaluate each combination of possible weights. The results were compared with the ones obtained to the same antenna using a simulator program (Ansoft HFSS) and with the results of a real prototype antenna built from the slit dimension optimal values obtained after the optimization process. (C) 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:18241828, 2012;View this article online at ***. DOI 10.1002/mop.26945

关键词： microstrip antennas multiobjective evolutionary algorithms genetic algorithms optimization

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