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On generating fuzzy systems based on pareto multi-objective cooperative coevolutionary algorithm

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2007年第4期5卷 444-455页

作者： Xing, Zong-Yi Zhang, Yong Hou, Yuan-Long Jia, Li-Min Nanjing Univ Sci & Technol Sch Mech Engn Automat Jiangsu Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing Peoples R China

An approach to construct multiple interpretable and precise fuzzy systems based on the Pareto Multi-objective Cooperative coevolutionary algorithm (PMOCCA) is proposed in this paper. First, a modified fuzzy clustering algorithm is used to construct antecedents of fuzzy system, and consequents are identified separately to reduce computational burden. Then, the PMOCCA and the interpretability-driven simplification techniques are executed to optimize the initial fuzzy system with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets;thus both the precision and the interpretability of the fuzzy systems are improved. In order to select the best individuals from each species, we generalize the NSGA-II algorithm from one species to multi-species, and propose a new non-dominated sorting technique and collaboration mechanism for cooperative coevolutionary algorithm. Finally, the proposed approach is applied to two benchmark problems, and the results show its validity.

关键词： coevolutionary algorithm fuzzy modeling fuzzy system multi-objectives

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A coevolutionary algorithm for the flexible delivery and pickup problem with time windows

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2013年第1期141卷 4-13页

作者： Wang, Hsiao-Fan Chen, Ying-Yen Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 30013 Taiwan

This paper addresses a flexible delivery and pickup problem with time windows (FDPPTW) and formulates the problem into a mixed binary integer programming model in order to minimize the number of vehicles and to minimize the total traveling distance. This problem is shown to be NP-hard. In this study, therefore, a coevolutionary algorithm incorporated with a variant of the cheapest insertion method is developed to speed up the solution procedure. The FDPPTW scheme overcomes the shortcomings of the existing schemes for the delivery and pickup problems. By testing with some revised Solomon's benchmark problems, the computational results have shown the efficiency and the effectiveness of the developed algorithm. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Bi-directional logistics Delivery and pickup Vehicle routing problem with backhaul coevolutionary algorithm

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Multiobjective nondominated neighbor coevolutionary algorithm with elite population

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SOFT COMPUTING 2015年第5期19卷 1329-1349页

作者： Mu, Caihong Jiao, Licheng Liu, Yi Li, Yangyang Xidian Univ Int Res Ctr Intelligent Percept & Computat Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Shannxi Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

A nondominated neighbor coevolutionary algorithm (NNCA) with a novel coevolutionary mechanism is proposed for multiobjective optimization, where elite individuals are used to guide the search. All the nondominated individuals are divided into two subpopulations, namely, the elite population and the common population according to their crowding-distance values. The elite individual located in less-crowded region will have more chances to select more team members for its own team and thus this region can be explored more sufficiently. Therefore, the elite population will guide the search to the more promising and less-crowded region. Secondly, to avoid the 'search stagnation' situation which means that algorithms fail to find enough nondominated solutions, a size guarantee mechanism (SGM) is proposed for elite population by emigrating some dominated individuals to the elite population when necessary. The SGM can prevent the algorithm from searching around limited nondominated individuals and being trapped into the 'search stagnation' situation. In addition, several different kinds of crossover and mutation operator are used to generate offspring, which are benefits for the diversity property. Tests on 20 multiobjective optimization benchmark problems including five ZDT problems, five DTLZ problems and ten unconstrained CEC09 test problems show that NNCA is very competitive compared with seven the state-of-the-art multiobjective optimization algorithms.

关键词： Multiple objective programming Evolutionary computations coevolutionary algorithm Elite population

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Converging coevolutionary algorithm for two-person zero-sum discounted Markov games with perfect information

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2008年第2期53卷 596-601页

作者： Chang, Hyeong Soo Sogang Univ Dept Comp Sci & Engn Seoul 121742 South Korea

This note proposes a novel population-based coevolutionary algorithm, called the "local-equilibrium-based coevolutionary algorithm" (LECA), for solving infinite-horizon-discounted two-person zero-sum Markov games with perfect information. LECA "randomizes" a simplified variant of Raghavan and Syed's algorithm, which adapted Howard's policy improvement algorithm for solving Markov decision processes into a "negotiation process" algorithm between two players via a lexicographical search. LECA runs over relatively small sets of policies such that the negotiation process proceeds with the sets via "policy switching," rather than with the entire policy spaces of the players. The use of policy switching eliminates the action spaces manipulation in value iteration and policy iteration-type algorithms and derives a novel concept of "local equilibrium." With the concept incorporated into the LECA, a "local" equilibrium policy pair is identified as an "elitist," and kept in the next population, directing the LECA toward finding an equilibrium or a near-equilibrium policy pair. The algorithm is especially targeted to problems where the state space is small but the action spaces of the players are extremely large, and converges with probability one to an equilibrium policy pair for a given game.

关键词： coevolutionary algorithm Markov game perfect information policy switching stochastic game

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A coevolutionary algorithm for balancing and sequencing in mixed model assembly lines

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APPLIED INTELLIGENCE 2000年第3期13卷 247-258页

作者： Kim, YK Kim, JY Kim, Y Chonnam Natl Univ Dept Ind Engn Kwangju 500757 South Korea

A mixed model assembly line is a production line where a variety of product models are produced. Line balancing and model sequencing problems are important for an efficient use of such lines. Although the two problems are tightly interrelated with each other, prior researches have considered them separately or sequentially. This paper presents a new method using a coevolutionary algorithm that can solve the two problems at the same time. In the algorithm, it is important to promote population diversity and search efficiency. We adopt a localized interaction within and between populations, and develop methods of selecting symbiotic partners and evaluating fitness. Efficient genetic representations and operator schemes are also provided. When designing the schemes, we take into account the features specific to the problems. Also presented are the experimental results that demonstrate the proposed algorithm is superior to existing approaches.

关键词： coevolutionary algorithm genetic representation and operators line balancing model sequencing mixed model assembly lines

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An Implementation of Probabilistic Model-Building coevolutionary algorithm

An Implementation of Probabilistic Model-Building Coevolutio...

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16th International Symposium on Artificial Life and Robotics (AROB 16th '11)

作者： Otani, Takahiro Arita, Takaya Nagoya Univ Grad Sch Informat Sci Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan

ISBN: (纸本)9784990288051

We propose an extended coevolutionary algorithm (CA) with probabilistic model-building (CA-PMB) in order to improve search performance of CA. This paper specifically describes an implementation of CA-PMB called coevolutionary algorithm with population-based incremental learning (CA-PBIL), and analyzes behavior of the algorithm through computational experiments using intransitive numbers game as a benchmark problem. The experimental results show that desirable coevolution may be inhibited by the over-specialization effect, and that the algorithm shows complex dynamics caused by the game's intransitivity. However, further experiments show that the intransitivity encourages desirable coevolution when a different learning rate is set for each population.

关键词： coevolutionary algorithm Probabilistic model-building genetic algorithm Intransitive numbers game

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OPTIMIZATION OF STRUCTURE OF FUZZY-NEURAL SYSTEMS USING coevolutionary algorithm

OPTIMIZATION OF STRUCTURE OF FUZZY-NEURAL SYSTEMS USING COEV...

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International Conference on Evolutionary Computation Theory and Applications (ECTA 2011) / International Conference on Fuzzy Computation Theory and Applications (FCTA 2011)

作者： Avdagic, Zikrija Omanovic, Samir Buza, Emir Cardakovic, Belma Fac Elect Engn Sarajevo Dept Comp Sci & Informat Zmaja Bosne BbKampus Univ Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9789898425836

This paper is related to a research of modelling fuzzy-neural systems using the coevolutionary algorithm, and has the focus on advantages of using the coevolutionary algorithm for system structure optimization. In the context of this work, the term fuzzy-neural system defines the system that can be used as the fuzzy system with all its functionalities or as the neural network with all its functionalities. The hybridization of fuzzy logic, neural networks and coevolutionary algorithm and its architecture are presented in general, and the role of the coevolutionary algorithm in structure optimization is described in details. Results of testing with Iris Database, from UCI Machine Learning Repository are also presented. Tests performed during the research supports the conclusion that usage of the coevolutionary algorithm for the fuzzy-neural system's structure optimization is very efficient.

关键词： coevolutionary algorithm Fuzzy logic Neural network Mamdani fuzzy system Hybridization

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Michigan and Pittsburgh Methods Combination for Fuzzy Classifier Design with coevolutionary algorithm

Michigan and Pittsburgh Methods Combination for Fuzzy Classi...

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IEEE Congress on Evolutionary Computation

作者： Sergienko, Roman Semenkin, Eugene Siberian State Aerosp Univ Dept Syst Anal & Operat Res Krasnoyarsk Russia

ISBN: (纸本)9781479904549;9781479904532

A new method of Michigan and Pittsburgh approaches combination for fuzzy classifier design with evolutionary algorithms is presented. Fuzzy classifier design includes of four stages. The first stage is standard fuzzification. The second one is a special procedure of initial rules forming with a priori information from a learning sample. At the third stage Michigan method is applied and it provides fast search of fuzzy rules with the best grade of certainty values for different classes and smoothing of randomness at initial population forming. At the fourth stage Pittsburgh method provides rules subset search with the best performance and predefined number of the rules and doesn't require a lot of computational power. Besides, a self-tuning cooperative-competitive coevolutionary algorithm for strategy adaptation is applied at Michigan and Pittsburgh stages of the fuzzy classifier design. This algorithm automatically solves the problem of genetic algorithm parameters setting. Thereby the method allows getting a compact fuzzy rule set with appropriate classification performance and with high computation speed. Classification results for machine learning problems from UCI repository and comparison with different alternative classifiers are presented.

关键词： fuzzy classifier Michigan method Pittsburgh method coevolutionary algorithm

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Two Player Zero Sum Multi-Stage Game Analysis Using coevolutionary algorithm

Two Player Zero Sum Multi-Stage Game Analysis Using Coevolut...

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作者： Sumedh Sopan Nagrale Wright State University

学位级别：硕士

A New Two player zero sum multistage simultaneous Game has been developed from a real-life situation of dispute between two individual. Research identifies a multistage game as a multi-objective optimization problem and solves it using coevolutionary algorithm which converges to a solution from pareto optimal solution. A comparison is done between individual stage behaviour and multistage behaviour. Further, simulations over a range for Crossover rate, Mutation rate and Number of interaction is done to narrow down the range for a range with optimal computation speed. A relationship has been observed which identifies a relationship between population size, number of interactions, crossover rate, mutation rate and computational time. A point from the obtained range is then selected and applied to a new game to see if the point from the narrowed range works.

关键词： Electrical Engineering coevolutionary algorithm Parameters Game Theory Multistage Game computation speed population size Mutation rate Crossover rate Parameters relations equation

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Flexible Launch Vehicle Attitude Control Design Using coevolutionary algorithm

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IFAC Proceedings Volumes 2004年第6期37卷 979-984页

作者： Hyunjae Lee Chang-Su Park Min-Jea Tahk Hyochoong Bang Korea Advanced Institute of Science and Technology Division of Aerospace Engineering 373-1 Kusong-Dong Yusong-Gu Daejeon 305-701 Republic of Korea

Launch vehicles in general are slender and flexible. The flexibility of the launch vehicle easily induces instability to the total launch vehicle system. There is also a high rate of change in mass and aerodynamic conditions. These conditions require an attitude controller which is robust to various system parameter changes and disturbances. In this paper, the launch vehicle's attitude controller is optimized by the coevolutionary algorithm. The attitude controller structure must be determined and the performance index is set according to the user needs. The coevolutionary algorithm can find the gains of the controller which gives the best performance. The controller is robust over a wide range of the system uncertainties. Also these uncertainties are controlled by the adaptive control using neural networks.

关键词： Launch vehicle classical controller coevolutionary algorithm attitude control quadratic adaptive control

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