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Conference on Object Detection, Classification, and Tracking Technologies

作者： Zheng, H Wuhan Univ Sch Elect Informat Wuhan 430079 Peoples R China

ISBN: (纸本)0819442828

Image textures have been playing an important role in image recognition and interpretation. The selection of image texture features is the key part to classify image objects. An evolution approach for texture image feature selection is proposed in this paper. The approach uses evolution algorithms as the primary search component. Based on selected texture features, a fuzzy cluster method is used for texture image classification. Experimental results on color aerial images show the feasibility of the proposed method.

关键词： evolution algorithm texture classification feature selection

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Neuro-Fuzzy System Design Using Differential evolution with Local Information

Neuro-Fuzzy System Design Using Differential Evolution with ...

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IEEE International Conference on Fuzzy Systems (FUZZ 2011)

作者： Lin, Chin-Teng Han, Ming-Feng Lin, Yang-Yin Liao, Shih-Hui Chang, Jyh-Yeong Natl Chiao Tung Univ Dept Elect & Control Engn Hsinchu Taiwan

ISBN: (纸本)9781424473175

This paper proposes a differential evolution with local information for TSK-type neuro-fuzzy system optimization. The differential evolution with local information consider neighborhood between each individual to keep the diversity of population. An adaptive parameter tuning based on 1/5th rule is used to trade off between local search and global search. For structure learning algorithm, the on-line clustering algorithm is used for rule generation. The structure learning algorithm generates a new rule which compares the firing strength. Initially, there is no rule in neuro-fuzzy system model. The rules are automatically generated by fuzzy measure. For parameter learning, the parameters are optimized by differential evolution algorithm. Finally, the proposed neuro-fuzzy system with novel differential evolution model is applied in chaotic sequence prediction problem. Results of this paper demonstrate the effectiveness of the proposed model.

关键词： evolution algorithm Neuro-Fuzzy System Fuzzy System Differential evolution Optimization

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Feature based problem hardness understanding for requirements engineering

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Science China(Information Sciences) 2017年第3期60卷 88-107页

作者： Zhilei REN He JIANG Jifeng XUAN Shuwei ZHANG Zhongxuan LUO Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian University of Technology School of Software Dalian University of Technology State Key Laboratory of Software Engineering Wuhan University

Heuristics and metaheuristics have achieved great accomplishments in various fields, and the investigation of the relationship between these algorithms and the problem hardness has been a hot topic in the research field. Related research work has contributed much to the understanding of the underlying mechanisms of the algorithms for problem solving. However, most existing studies consider traditional combinatorial problems as their case studies. In this study, taking the Next Release Problem(NRP) from the requirements engineering as a case study, we investigate the relationship between software engineering problem instances and heuristics. We employ an evolutionary algorithm to evolve NRP instances, which are uniquely hard or easy for the target heuristic(Greedy Randomized Adaptive Search Procedure and Randomized Hill Climbing in this paper). Then, we use a feature-based method to estimate the hardness of the evolved instances, with respect to the target heuristic. Experimental results demonstrate that, evolutionary algorithm can be used to evolve NRP instances that are uniquely hard or easy to solve. Moreover, the features enable the estimation of the target heuristics’ performance.

关键词： problem hardness next release problem computational intelligence requirements engineering evolution algorithm

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Learning Gait of Quadruped Robot without Prior Knowledge of the Environment

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Chinese Journal of Mechanical Engineering 2012年第5期25卷 1068-1074页

作者： XU Tao CHEN Qijun College of Electronic and Information Engineering Tongji UniversityShanghai 201804China

Walking is the basic skill of a legged robot, and one of the promising ways to improve the walking performance and its adaptation to environment changes is to let the robot learn its walking by itself. Currently, most of the walking learning methods are based on robot vision system or some external sensing equipment to estimate the walking performance of certain walking parameters, and therefore are usually only applicable under laboratory condition, where environment can be pre-defined. Inspired by the rhythmic swing movement during walking of legged animals and the behavior of their adjusting their walking gait on different walking surfaces, a concept of walking rhythmic pattern（WRP） is proposed to evaluate the walking specialty of legged robot, which is just based on the walking dynamics of the robot. Based on the onboard acceleration sensor data, a method to calculate WRP using power spectrum in frequency domain and diverse smooth filters is also presented. Since the evaluation of WRP is only based on the walking dynamics data of the robot＇s body, the proposed method doesn＇t require prior knowledge of environment and thus can be applied in unknown environment. A gait learning approach of legged robots based on WRP and evolution algorithm（EA） is introduced. By using the proposed approach, a quadruped robot can learn its locomotion by its onboard sensing in an unknown environment, where the robot has no prior knowledge about this place. The experimental result proves proportional relationship exits between WRP match score and walking performance of legged robot, which can be used to evaluate the walking performance in walking optimization under unknown environment.

关键词： quadruped locomotion gait learning evolution algorithm

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A new sentence similarity measure and sentence based extractive technique for automatic text summarization

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第4期36卷 7764-7772页

作者： Aliguliyev, Ramiz M. Natl Acad Sci Azerbaijan Inst Informat Technol AZ-1141 Baku Azerbaijan

The technology of automatic document summarization is maturing and may provide a solution to the information overload problem. Nowadays, document summarization plays an important role in information retrieval. With a large volume of documents, presenting the user with a summary of each document greatly facilitates the task of finding the desired documents. Document summarization is a process of automatically creating a compressed version of a given document that provides useful information to users. and multi-document summarization is to produce a summary delivering the majority of information content from a set of documents about an explicit or implicit main topic. In our study we focus on sentence based extractive document summarization. We propose the generic document summarization method which is based on sentence clustering. The proposed approach is a continue sentence-clustering based extractive summarization methods, proposed in Alguliev [Alguliev, R. M., Aliguliyev. R. M., Bagirov, A. M. (2005). Global optimization in the summarization of text documents. Automatic Control and Computer Sciences 39, 42-47], Aliguliyev [Aliguliyev, R. M. (2006). A novel partitioning-based clustering method and generic document summarization. In Proceedings of the 2006 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT2006 Workshops) (WI-IATW'06),18-22 December (pp. 626-629) Hong Kong, China], Alguliev and Alyguliev [Alguliev, R. M., Alyguliev, R. M. (2007). Summarization of text-based documents with a determination of latent topical sections and information-rich sentences. Automatic Control and Computer Sciences 41, 132-140] Aliguliyev, [Aliguliyev, R. M. (2007). Automatic document summarization by sentence extraction. Journal of Computational Technologies 12, 5-15.]. The purpose of present paper to show, that summarization result not only depends on optimized function, and also depends on a similarity measure. The experimental re

关键词： Similarity measure Text mining Sentence clustering Summarization evolution algorithm Sentence extractive technique

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Evolving Deep Multiple Kernel Learning Networks Through Genetic algorithms

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第2期19卷 1569-1580页

作者： Shen, Wangbo Lin, Weiwei Wu, Yulei Shi, Fang Wu, Wentai Li, Keqin South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pengcheng Lab Shenzhen 518055 Peoples R China Univ Exeter Coll Engn Math & Phys Sci Dept Comp Sci Exeter EX4 4QF England SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Today's Industrial Internet of Things (IIoT) have achieved excellent manufacturing efficiency and automation results by leveraging machine learning (ML) and deep learning (DL). However, trustworthiness of ML/DL brings significant challenges to IIoT. This article proposes an evolving deep multiple kernel learning network through genetic algorithm (KNGA). Our KNGA method uses genetic algorithm (GA) to find the best deep multiple kernel learning structure, including the weights and the topology of the model. Compared with the current well-known models, KNGA has advantages in three aspects: 1) It can achieve good results without using many samples during model training;2) the model can evolve in the process of training, including self-growth, and self-pruning;and 3) its trustworthiness and reliability can be guaranteed. Moreover, the whole model ensures excellent performance and requires manual adjustment of only a few parameters. Extensive experiments on the UCI, KEEL, Caltech256, and MNIST datasets demonstrate the effectiveness and trustworthiness of the proposed method.

关键词： AutoML evolution algorithm kernel learning neural networks trustworthiness

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Memetic algorithm for Real-Time Combinatorial Stochastic Simulation Optimization Problems With Performance Analysis

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IEEE TRANSACTIONS ON CYBERNETICS 2013年第5期43卷 1495-1509页

作者： Horng, Shih-Cheng Lin, Shin-Yeu Lee, Hay Chen, Chun-Hung Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung 413 Taiwan Chang Gung Univ Dept Elect Engn Tao Yuan 333 Taiwan Chang Gung Univ Green Technol Res Ctr Tao Yuan 333 Taiwan Natl Univ Singapore Dept Ind & Syst Engn Singapore 119615 Singapore Shanghai Maritime Univ Logist Engn Coll Shanghai 200135 Peoples R China George Mason Univ Dept Syst Engn & Operat Res Fairfax VA 22030 USA Natl Taiwan Univ Dept Elect Engn Taipei 106 Japan Natl Taiwan Univ Inst Ind Engn Taipei 106 Japan

A three-phase memetic algorithm (MA) is proposed to find a suboptimal solution for real-time combinatorial stochastic simulation optimization (CSSO) problems with large discrete solution space. In phase 1, a genetic algorithm assisted by an offline global surrogate model is applied to find N good diversified solutions. In phase 2, a probabilistic local search method integrated with an online surrogate model is used to search for the approximate corresponding local optimum of each of the N solutions resulted from phase 1. In phase 3, the optimal computing budget allocation technique is employed to simulate and identify the best solution among the N local optima from phase 2. The proposed MA is applied to an assemble-to-order problem, which is a real-world CSSO problem. Extensive simulations were performed to demonstrate its superior performance, and results showed that the obtained solution is within 1% of the true optimum with a probability of 99%. We also provide a rigorous analysis to evaluate the performance of the proposed MA.

关键词： Artificial neural network assemble to order (ATO) combinatorial optimisation evolution algorithm memetic algorithm (MA) optimal computing budget allocation (OCBA) stochastic simulation surrogate model

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Optimal integration of MBESSs/SBESSs in distribution systems with renewables

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IET RENEWABLE POWER GENERATION 2018年第10期12卷 1172-1179页

作者： Zheng, Yu Meng, Ke Luo, Fengji Qiu, Jing Zhao, Junhua Changsha Univ Sci & Technol Sch Elect Engn & Informat Changsha Hunan Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China

The net demand of distribution systems of renewable energy shows strong daily and seasonal patterns that may cause a loss of operation and constriction. Battery energy storage systems (BESS) allow peak load shaving and are used to enhance the reliability of distribution systems. In addition to the problem of an unbalanced net demand, there can be significant net demand fluctuation for different times and locations. To address this, mobile BESS (MBESS) can offer advantages over static BESS (SBESS) in operation flexibility, though may require higher engineering costs. In this study, an operation model was proposed to coordinate static and MBESS to improve overall system economic efficiency and reliability. On the basis of this model, a framework was proposed to optimally allocate MBESS/SBESS in a distribution system based on cost-benefit analysis. Using this approach, the optimal operation schedules for MBESS and SBESS can be simultaneously obtained. The proposed optimisation problem uses a new evolution algorithm, a natural aggregation algorithm. A case study on the IEEE test system successfully verified the effectiveness of the proposed approach for the optimal allocation of MBESS/SBESS in distribution systems.

关键词： battery storage plants power distribution reliability distributed power generation optimisation evolutionary computation cost-benefit analysis power generation scheduling power generation economics MBESSs-SBESSs distribution system reliability renewable energy battery energy storage systems peak load shaving unbalanced net demand mobile BESS static BESS engineering costs operation model system economic efficiency cost-benefit analysis optimisation problem evolution algorithm natural aggregation algorithm IEEE test system optimal operation schedules

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The feature-preserving map of high-dimensional complex chemical objects using non-linear map integrated with correlative component analysis

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2005年第1期75卷 13-22页

作者： Yan, XF Yu, J Qian, F E China Univ Sci & Technol Automat Inst Shanghai 200237 Peoples R China

A novel procedure integrating non-linear map (NLM) with correlative component analysis (CCA) was proposed to obtain the two-dimensional feature map of high-dimensional complex chemical objects that can more concisely and efficiently represent the object classification. Firstly, CCA was employed to identify the most important correlative components (CCs) from the original high-dimensional complex chemical object. Then, NLM maps the first several CCs, which include the most useful classification information of the object, onto a two-dimensional plane, on which the object classification feature is concisely represented. To overcome the deficiencies of NLM, modified NLM (M-NLM) combining a new adaptive mapping error and a novel optimization approach named as select-best and prepotency evolution algorithm (SPEA) was proposed. The value of the weight factor of the adaptive mapping error is determined adaptively according to the relative deviation square between the two mapping points distance and the corresponding original objects distance. The larger the relative deviation square between two distances is, the larger the value of the corresponding weight factor is. The main operators of SPEA are the proposed select-best and prepotency operator (SPO) and the mutation operator. The comparison results demonstrated that the whole performance of SPEA is better than that of genetic algorithm (GA). Finally, a typical example of mapping two classes' natural spearmint essence was employed to verify the effectiveness of the new approach. The feature-preserving map obtained by NLM integrated with CCA can well represent the classification of the original objects and is much better than the map obtained by NLM alone. In addition, the feature-preserving map obtained by M-NLM integrated with CCA is better than that obtained by NLM integrated with CCA. (C) 2004 Elsevier B.V. All rights reserved.

关键词： non-linear map feature-preserving map topology-preserving map correlative component analysis evolution algorithm genetic algorithm

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A Novel Nonlinear Expanded Dominance Relation Based evolutionary algorithm for Many-Objective Optimization Problems

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IEEE ACCESS 2021年 9卷 17335-17349页

作者： Hu, Lingfeng Wei, Jingxuan Liu, Yang Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

Multi-objective optimization problems exist widely in scientific research and engineering applications. With the number of objectives increasing, the proportion of non-dominated individuals in the population of many-objective optimization problems increases sharply, resulting in a reduction of convergence pressure of the traditional multi-objective optimization algorithms. In some cases, the optimal solutions may be located in the special regions, such as many discrete regions and the regions with very few feasible solutions. In this case, the existing nonlinear expanded evolutionary algorithm can not find the true Pareto fronts. To address the limitation, a novel nonlinear expanded dominance relation based many-objective evolutionary algorithm is proposed to handle many-objective optimization problems. Experimental results show that compared with the state of art algorithms, the proposed algorithm is effective for DTLZs, in terms of IGD, PD and GD metrics.

关键词： Optimization Statistics Sociology evolutionary computation Convergence Linear programming Sorting Many-objective optimization nonlinear expanded dominance relation evolution algorithm

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