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Optimization of fuzzy CMAC using evolutionary Bayesian Ying-Yang learning

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Frontiers of Electrical and Electronic Engineering in China 2011年第2期6卷 208-214页

作者： Payam S.RAHMDEL Minh Nhut NGUYEN Liying ZHENG School of Engineering and Information Sciences Middlesex UniversityLondon NW44BTUnited Kingdom Institute for Infocomm Research Singapore 138632Singapore School of Computer Science and Techology Harbin Engineering UniversityHarbin 150001China

Cerebellar model articulation controller(CMAC)is a popular associative memory neural network that imitates human’s cerebellum,which allows it to learn fast and carry out local generalization efficiently.This research aims to integrate evolutionary computation into fuzzy CMAC Bayesian Ying-Yang(FCMACBYY)learning,which is referred to as FCMAC-EBYY,to achieve a synergetic development in the search for optimal fuzzy sets and connection weights.Traditional evolutionary approaches are limited to small populations of short binary string length and as such are not suitable for neural network training,which involves a large searching space due to complex connections as well as real values.The methodology employed by FCMACEBYY is coevolution,in which a complex solution is decomposed into some pieces to be optimized in different populations/species and then assembled.The developed FCMAC-EBYY is compared with various neuro-fuzzy systems using a real application of traffic flow prediction.

关键词： cerebellar model articulation controller(CMAC) Bayesian Ying-Yang(BYY)learning evolutionary computation

An evolutionary Algorithm to Optimization of Discrete Problem Based on Pheromone

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Wuhan University Journal of Natural Sciences 2001年第Z1期6卷 606-610页

作者： Huang Zhang can 1, Wu Fang cai 2, Chen Si duo 2 1.Department of Mathematics,Eastern Campus Wuhan University of Technology, Wuhan 430070, China 2.Department of Automation, Eastern Campus Wuhan University of Technology, Wuhan 430070,China Department of Mathematics Eastern Campus Wuhan University of Technology Wuhan China Department of Automation Eastern Campus Wuhan University of Technology Wuhan China

The pheromone based positive feedback approach of ant algorithm is introduced in evolutionary computation of discrete problem, so as to accomplish the optimization of each allele. It ensures stable converge of the algorithm into global optimum. The optimal cutting problem is studied as an example to analyze the performance of the algorithm. The experimental results show the novel performance of the algorithm in the optimization of discrete problem.

关键词： pheromone Ant System evolutionary computation

An evolutionary Real-Time 3D Route Planner for Aircraft

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Journal of Systems Engineering and Electronics 2003年第1期14卷 47-53页

作者：郑昌文丁明跃周成平 Institute of Pattern Recognition and Artificial Intelligence State Education Commission Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 P. R. China

A novel evolutionary route planner for aircraft is proposed in this paper. In the new planner, individual candidates are evaluated with respect to the workspace, thus the computation of the configuration space is not required. By using problem-specific chromosome structure and genetic operators, the routes are generated in real time, with different mission constraints such as minimum route leg length and flying altitude, maximum turning angle, maximum climbing/diving angle and route distance constraint taken into account.

关键词： evolutionary computation Route planning Route constraints Real time Aircraft.

computation model and improved ACO algorithm for p//T

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Journal of Systems Engineering and Electronics 2009年第6期20卷 1336-1343页

作者： Yi Yang Lai Jieling School of Information Science & Technology Sun Yat-sen Univ. Guangzhou 510275 P. R. China

Scheduling jobs on parallel machines to minimize the total tardiness（p//T） is proved to be NP hard.A new ant colony algorithm to deal with p//T（p//T ACO） is addressed, and the computing model of mapping p//T to the ant colony optimization environment is designed.Besides, based on the academic researches on p//T, some new properties used in the evolutionary computation are analyzed and proved.The theoretical analysis and comparative experiments demonstrate that p//T ACO has much better performance and can be used to solve practical large scale problems efficiently.

关键词： scheduling evolutionary computation ant colony optimization.

Exploiting Bivariate Dependencies to Speedup Structure Learning in Bayesian Optimization Algorithm

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Journal of Computer Science & Technology 2012年第5期27卷 1077-1090页

作者： Amin Nikanjam Adel Rahmani School of Computer Engineering Iran University of Science and Technology

Bayesian optimization algorithm （BOA） is one of the successful and widely used estimation of distribution algorithms （EDAs） which have been employed to solve different optimization problems. In EDAs, a model is learned from the selected population that encodes interactions among problem variables. New individuals are generated by sampling the model and incorporated into the population. Different probabilistic models have been used in EDAs to learn interactions. Bayesian network （BN） is a well-known graphical model which is used in BOA. Learning a propel model in EDAs and particularly in BOA is distinguished as a computationally expensive task. Different methods have been proposed in the literature to improve the complexity of model building in EDAs. This paper employs bivariate dependencies to learn accurate BNs in BOA efficiently. The proposed approach extracts the bivariate dependencies using an appropriate pairwise interaction-detection metric. Due to the static structure of the underlying problems, these dependencies are used in each generation of BOA to learn an accurate network. By using this approach, the computational cost of model building is reduced dramatically. Various optimization problems are selected to be solved by the algorithm. The experimental results show that the proposed approach successfully finds the optimum in problems with different types of interactions efficiently. Significant speedups are observed in the model building procedure as well.

关键词： evolutionary computation Bayesian optimization algorithm Bayesian network model building bivariate interaction

Zoning Search With Adaptive Resource Allocating Method for Balanced and Imbalanced Multimodal Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1163-1176页

作者： Qinqin Fan Okan K.Ersoy Logistics Research Center Shanghai Maritime UniversityShanghai 201306China School of Electronic and Computer Engineering Purdue UniversityWest LafayetteIN 47906 USA

Maintaining population diversity is an important task in the multimodal multi-objective optimization.Although the zoning search(ZS)can improve the diversity in the decision space,assigning the same computational costs to each search subspace may be wasteful when computational resources are limited,especially on imbalanced problems.To alleviate the above-mentioned issue,a zoning search with adaptive resource allocating(ZS-ARA)method is proposed in the current study.In the proposed ZS-ARA,the entire search space is divided into many subspaces to preserve the diversity in the decision space and to reduce the problem complexity.Moreover,the computational resources can be automatically allocated among all the subspaces.The ZS-ARA is compared with seven algorithms on two different types of multimodal multi-objective problems(MMOPs),namely,balanced and imbalanced MMOPs.The results indicate that,similarly to the ZS,the ZS-ARA achieves high performance with the balanced MMOPs.Also,it can greatly assist a“regular”algorithm in improving its performance on the imbalanced MMOPs,and is capable of allocating the limited computational resources dynamically.

关键词： computational resource allocation decision space decomposition evolutionary computation multimodal multi-objective optimization

Parallel exploration via negatively correlated search

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Frontiers of Computer Science 2021年第5期15卷 123-135页

作者： Peng YANG Qi YANG Ke TANG Xin YAO Guangdong Provincial Key Laboratory of Brain-inspired Intelligent computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

Effective exploration is key to a successful search process.The recently proposed negatively correlated search(NCS)tries to achieve this by coordinated parallel exploration,where a set of search processes are driven to be negatively correlated so that different promising areas of the search space can be visited simultaneously.Despite successful applications of NCS,the negatively correlated search behaviors were mostly devised by intuition,while deeper(e.g.,mathematical)understanding is missing.In this paper,a more principled NCS,namely NCNES,is presented,showing that the parallel exploration is equivalent to a process of seeking probabilistic models that both lead to solutions of high quality and are distant from previous obtained probabilistic models.Reinforcement learning,for which exploration is of particular importance,are considered for empirical assessment.The proposed NCNES is applied to directly train a deep convolution network with 1.7 million connection weights for playing Atari games.Empirical results show that the significant advantages of NCNES,especially on games with uncertain and delayed rewards,can be highly owed to the effective parallel exploration ability.

关键词： evolutionary computation reinforcement learning exploration

computational Intelligence in Remote Sensing Image Registration:A survey

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International Journal of Automation and computing 2021年第1期18卷 1-17页

作者： Yue Wu Jun-Wei Liu Chen-Zhuo Zhu Zhuang-Fei Bai Qi-Guang Miao Wen-Ping Ma Mao-Guo Gong Key Laboratory of Big Data and Intelligent Vision School of Computer Science and TechnologyXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial IntelligenceXidian UniversityXi′an 710071China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Electronic EngineeringXidian UniversityXi′an 710071China

In recent years,computational intelligence has been widely used in many fields and achieved remarkable performance.evolutionary computing and deep learning are important branches of computational intelligence.Many methods based on evolutionary computation and deep learning have achieved good performance in remote sensing image registration.This paper introduces the application of computational intelligence in remote sensing image registration from the two directions of evolutionary computing and deep learning.In the part of remote sensing image registration based on evolutionary calculation,the principles of evolutionary algorithms and swarm intelligence algorithms are elaborated and their application in remote sensing image registration is discussed.The application of deep learning in remote sensing image registration is also discussed.At the same time,the development status and future of remote sensing image registration are summarized and their prospects are examined.

关键词： computational intelligence evolutionary computation neural network deep learning remote sensing image registration

Enhancing the Performance of JADE Using Two-phase Parameter Control Scheme and Its Application

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International Journal of Automation and computing 2018年第4期15卷 462-473页

作者： Qin-Qin Fan Yi-Lian Zhang Xue-Feng Yan Zhi-Huan Wang Logistics Research Center Shanghai Maritime UniversityShanghai 201306China Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai 200237China Key Laboratory of Marine Technology and Control Engineering Ministry of Communications Shanghai Maritime UniversityShanghai 201306China

The search efficiency of differential evolution （DE） algorithm is greatly impacted by its control parameters. Although many adaptation/self-adaptation techniques can automatically find suitable control parameters for the DE, most techniques are based on pop- ulation information which may be misleading in solving complex optimization problems. Therefore, a self-adaptive DE （i.e., JADE） using two-phase parameter control scheme （TPC-JADE） is proposed to enhance the performance of DE in the current study. In the TPC-JADE, an adaptation technique is utilized to generate the control parameters in the early population evolution, and a well-known empirical guideline is used to update the control parameters in the later evolution stages. The TPC-JADE is compared with four state-of-the-art DE variants on two famous test suites （i.e., IEEE CEC2005 and IEEE CEC2015）. Results indicate that the overall performance of the TPC-JADE is better than that of the other compared algorithms. In addition, the proposed algorithm is utilized to obtain optimal nutrient and inducer feeding for the Lee-Ramirez bioreactor. Experimental results show that the TPC-JADE can perform well on an actual dynamic optimization problem.

关键词： Differential evolution（DE）algorithm evolutionary computation dynamic optimization control parameter adaptation chemical processes.