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31st Chinese Control Conference

作者： Zhou Wei Bu Yan-ping East China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Sch Technol Shanghai 201101 Peoples R China

ISBN: (纸本)9789881563811

A novel cultural algorithm based on particle swarm optimization (PSO) algorithm was proposed in this paper. After analyzing the partner selection problems of virtual enterprise, the CPSO algorithm was presented to solve enterprise alliance problem within reasonable time and cost. There are certain number partners of each sub-task in virtual enterprise environment. The objective is, by selecting the optimal combination of partners, to minimize project's completion time and project's total cost. We tested the CPSO algorithm against the PSO method. Simulation results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the exhaustion method to show the algorithm's efficiency.

关键词： cultural algorithm particle swarm optimization algorithm virtual enterprise partner selection

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AC transmission network expansion planning considering losses

AC transmission network expansion planning considering losse...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)

作者： Morquecho, Edgar G. Torres, Santiago P. Espinoza, Juan L. Lopez, Julio C. Quizhpe, Klever L. Sempertegui, Rodrigo E. Solano, Lizandro D. Univ Cuenca Dept Elect Elect & Telecommun Engn Cuenca Ecuador

ISBN: (纸本)9781538645055

This paper proposes to solve the transmission network expansion planning problem (TNEP) using the AC model formulated with full non-linear load flow equations, incorporating the cost of losses in the transmission network. Additionally, the decomposed formulation finds the location and amount of the reactive compensation needed in the system. A comparison between Evolutionary Programming (EP) and a variation of EP with a cultural algorithm (CEP) is presented to solve this very complex optimization problem. The results are obtained using Garver's 6-bus test system and IEEE 24-bus test system.

关键词： AC model cultural algorithm Evolutionary Programming Optimization Transmission network expansion planning

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A Hybrid of Differential Evolution and cultural algorithm for Recurrent Functional Neural Fuzzy Networks and Its Applications

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2012年第4期14卷 519-529页

作者： Lin, Hsueh-Yi Lin, Cheng-Jian Wu, Chi-Feng Chen, Cheng-Hung Natl Chin Yi Univ Technol Comp Sci & Informat Engn Dept Taichung 411 Taiwan Wenzao Ursuline Coll Languages Digital Content Applicat & Management Dept Kaohsiung 811 Taiwan Natl Formosa Univ Dept Elect Engn Yunlin 632 Taiwan

By applying the recurrent functional neural fuzzy network (RFNFN) and a novel evolutionary learning algorithm this study presents an evolutionary neural fuzzy network (NFN). The proposed new evolutionary learning algorithm is based on an effective combination of the modified differential evolution (MDE) and cultural algorithm, which is called the cultural-based MDE (CMDE) method. After the four individuals are randomly chosen from the population for mutation, the proposed CMDE method can search the whole solution space more efficiently. In addition, during the evolutionary process of the cultural algorithm, the performance enhancement is highly affected by the belief space extraction and use of the corresponding information. The proposed RFNFN model uses functional-link neural networks (FLNN) as the consequent part of the fuzzy rules, while in this study the orthogonal polynomials and linearly independent functions are adapted as in a functional expansion of the FLNNs. In summary, the RFNFN model is not only able to generate the consequent part as a nonlinear combination of input variables, but also capable of tackling complicated temporal problem through the added feedback connections. Experimental results in prediction and control applications have shown that the proposed RFNFN-CMDE mechanism outperforms other compared approaches, in terms of convergent speed and root-mean-squared error.

关键词： cultural algorithm differential evolution functional link neural network neural fuzzy networks control prediction

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A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problems

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NEUROCOMPUTING 2012年 98卷 76-89页

作者： Sun, Yang Zhang, Lingbo Gu, Xingsheng E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

Intelligent evolutionary algorithms have been widely used to solve large-scale, complex global optimization problems. Co-evolutionary algorithm (CEA), cultural algorithm (CA), and particle swarm optimization (PSO) are all promising methods in the field of intelligent computation. In this paper, a hybrid co-evolutionary cultural algorithm based on particle swarm optimization (CECBPSO) is proposed. In CECBPSO, a novel space called shared global belief space (SGBS) is introduced into the co-evolutionary mechanism, and a new co-evolutionary cultural framework is built. Through the synergistic mechanism, the algorithm has higher probability of avoiding local optima and the whole swarm can find global optima more quickly. Factorial Design (FD) approach is used in this paper in order to get a guideline on how to tune the designed parameters in CECBPSO. Extensive computational studies are also carried out to evaluate the performance of CECBPSO on thirteen benchmark functions and three real-life optimization problems. The results show that the proposed algorithm has superior performance to other compared algorithms in terms of accuracy and convergence speed, especially on high-dimensional problems. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Co-evolutionary algorithm cultural algorithm Particle swarm optimization Global optimization Factorial design Orthogonal test

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A multi-population cultural algorithm with adaptive diversity preservation and its application in ammonia synthesis process

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NEURAL COMPUTING & APPLICATIONS 2012年第6期21卷 1129-1140页

作者： Xu, Wei Wang, Raofen Zhang, Lingbo Gu, Xingsheng E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

A multi-population cultural differential evolution (MCDE) algorithm is proposed. Each of the populations is managed by its private cultural differential evolution algorithm, in which a center individual is introduced into the belief space and selection function follows a new method to select the offspring for the next generation. To accelerate the convergence speed, the populations exchange their knowledge with each other every given generations. An adaptive mechanism of population diversity preservation is put forward to prevent the populations from being trapped in local optima. In the adaptive mechanism, the idea of culture fusion between populations is used to know the convergence status, so that the diversity of populations is kept along the evolutionary process. The performance evaluation on MCDE using eleven constrained optimization problems shows that MCDE is a competitive approach. MCDE is further applied to a practical optimization problem in an ammonia synthesis system with the objective to maximize the net value of ammonia. The results achieved by MCDE are compared with those by two traditional differential evolution algorithms, which indicate that MCDE has more excellent performance and better effectiveness.

关键词： cultural algorithm Differential evolution Knowledge exchange Adaptive diversity preservation Ammonia synthesis

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Dominators VS Pure Dominators on the Accuracy of a Classifier with a Multi Objective cultural algorithm 12

Dominators VS Pure Dominators on the Accuracy of a Classifie...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Srinivasan, Sujatha Ramakrishnan, Sivakumar Cauvery Coll Women Dept Comp Sci Tiruchirappalli India AVVM Sri Pushpam Coll Dept Comp Sci Tanjore India

ISBN: (纸本)9781450311960

"If-Then" classification rules are one of many types of knowledge formats mined by data mining algorithms. These rules known as classifiers are evaluated based on objective and subjective metrics. Support, coverage, confidence, precision, recall, sensitivity and specificity are objective metrics which are used in measuring the accuracy of a classifier. Subjective measures like interest, surprise and comprehensibility are more user-oriented. Users prefer classifiers which are accurate, interesting and comprehensible. Hence mining classification rules with specific properties is considered as a multi objective optimization problem. Multi objective problems choose classifiers using optimization strategies which take the metric values as a vector or as a single functional value of the metrics taken together. Depending upon the values in the fitness vector the individuals may be dominators or pure dominators. In the current study an Extended Multi Objective cultural algorithm (EMOCA) framework is proposed for mining dominators and pure dominators taking coverage and confidence as optimization metrics and pure Pareto domination strategy to minimize the number of rules. The performance of dominators versus pure dominators in classifying unknown data instances and in producing a compact set of rules is compared using bench mark data set and the results reported.

关键词： Multi Objective Optimization Classification cultural algorithm Data mining

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Research of Web Search Based on cultural algorithm New Framework

Research of Web Search Based on Cultural Algorithm New Frame...

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International Workshop on Information and Electronics Engineering (IWIEE) / International Conference on Information, Computing and Telecommunications (ICICT)

作者： Zhang, Yu Harbin Univ Commerce Coll Comp & Informat Engn Harbin Peoples R China

With the development of information technology, especially the widespread use of Web, information on Web increases rapidly and becomes a huge information resource. In the meanwhile, such abundant information makes it an urgent problem: how to extract useful content rapidly and efficiently from information resources. This paper proposes a framework for evolutionary systems to search implicit knowledge on the web. Based on cultural algorithms ( CA), the web search process is supported by the domain knowledge objects in the belief space, and the optimization process is supported by evolutionary search in the population space. This framework in web search may help increase competition and diversity on the web. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Harbin University of Science and Technology

关键词： Web search cultural algorithm Data mining

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New Hybrids of ANFIS with Several Optimization algorithms for Flood Susceptibility Modeling

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WATER 2018年第9期10卷 1210-1210页

作者： Dieu Tien Bui Khosravi, Khabat Li, Shaojun Shahabi, Himan Panahi, Mahdi Singh, Vijay P. Chapi, Kamran Shirzadi, Ataollah Panahi, Somayeh Chen, Wei Bin Ahmad, Baharin Ton Duc Thang Univ Geog Informat Sci Res Grp Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Environm & Labour Safety Ho Chi Minh City Vietnam Sari Agr Sci & Nat Resources Univ Fac Nat Resources Dept Watershed Management Engn Sari *** Iran Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Hubei Peoples R China Univ Kurdistan Fac Nat Resources Dept Geomorphol Sanandaj *** Iran Islamic Azad Univ North Tehran Branch Young Researchers & Elites Club Tehran 19585466 Iran Texas A&M Univ Dept Biol & Agr Engn College Stn TX 77843 USA Texas A&M Univ Zachry Dept Civil Engn College Stn TX 77843 USA Univ Kurdistan Fac Nat Resources Dept Rangeland & Watershed Management Sanandaj *** Iran Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Shaanxi Peoples R China Univ Teknol Malaysia Fac Geoinformat & Real Estate Dept Geoinformat Skudai 81310 Malaysia

This study presents three new hybrid artificial intelligence optimization modelsnamely, adaptive neuro-fuzzy inference system (ANFIS) with cultural (ANFIS-CA), bees (ANFIS-BA), and invasive weed optimization (ANFIS-IWO) algorithmsfor flood susceptibility mapping (FSM) in the Haraz watershed, Iran. Ten continuous and categorical flood conditioning factors were chosen based on the 201 flood locations, including topographic wetness index (TWI), river density, stream power index (SPI), curvature, distance from river, lithology, elevation, ground slope, land use, and rainfall. The step-wise weight assessment ratio analysis (SWARA) model was adopted for the assessment of relationship between flood locations and conditioning factors. The ANFIS model, based on SWARA weights, was employed for providing FSMs with three optimization models to enhance the accuracy of prediction. To evaluate the model performance and prediction capability, root-mean-square error (RMSE) and receiver operating characteristic (ROC) curve (area under the ROC (AUROC)) were used. Results showed that ANFIS-IWO with lower RMSE (0.359) had a better performance, while ANFIS-BA with higher AUROC (94.4%) showed a better prediction capability, followed by ANFIS0-IWO (0.939) and ANFIS-CA (0.921). These models can be suggested for FSM in similar climatic and physiographic areas for developing measures to mitigate flood damages and to sustainably manage floodplains.

关键词： flood susceptibility modeling ANFIS cultural algorithm bees algorithm invasive weed optimization Haraz watershed

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Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

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WATER 2018年第11期10卷 1634页

作者： Sabzi, Sajad Abbaspour-Gilandeh, Yousef Garcia-Mateos, Gines Ruiz-Canales, Antonio Miguel Molina-Martinez, Jose Univ Mohaghegh Ardabili Coll Agr Dept Biosyst Engn Ardebil *** Iran Univ Murcia Comp Sci & Syst Dept E-30100 Murcia Spain Miguel Hernandez Univ Elche Engn Dept Orihuela 03312 Spain Tech Univ Cartagena Food Engn & Agr Equipment Dept Cartagena 30203 Spain

Due to the changes in the lighting intensity and conditions throughout the day, machine vision systems used in precision agriculture for irrigation management should be prepared for all possible conditions. For this purpose, a complete segmentation algorithm has been developed for a case study on apple fruit segmentation in outdoor conditions using aerial images. This algorithm has been trained and tested using videos with 16 different light intensities from apple orchards during the day. The proposed segmentation algorithm consists of five main steps: (1) transforming frames in RGB to CIE L*u*v* color space and applying thresholds on image pixels;(2) computing texture features of local standard deviation;(3) using intensity transformation to remove background pixels;(4) color segmentation applying different thresholds in RGB space;and (5) applying morphological operators to refine the results. During the training process of this algorithm, it was observed that frames in different light conditions had more than 58% color sharing. Results showed that the accuracy of the proposed segmentation algorithm is higher than 99.12%, outperforming other methods in the state of the art that were compared. The processed images are aerial photographs like those obtained from a camera installed in unmanned aerial vehicles (UAVs). This accurate result will enable more efficient support in the decision making for irrigation and harvesting strategies.

关键词： fruit segmentation video processing different light intensities artificial neural networks cultural algorithm irrigation management

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IIR Digital Filter Design Based on cultural Quantum-inspired Flower Pollination algorithm

IIR Digital Filter Design Based on Cultural Quantum-inspired...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Hongyuan Gao Yansong Liang Dandan Liu Ming Diao College of Information and Communication Engineering Harbin Engineering University

In order to resolve the multi-parameter optimization problem of infinite impulse response（IIR） digital filter design, a cultural quantum-inspired flower pollination algorithm（CQFPA） is proposed. The proposed CQFPA is a multidimensional search algorithm for optimization of real numbers,and uses mechanisms of the quantum and cultural evolution to update the quantum pollens, which may improve the capability of algorithm to find the optimal solution. Computer simulations have showed that IIR digital filters based on the CQFPA are superior to previous filters based on quantumbehaved particle swarm optimization（QPSO） and adaptive quantum-behaved particle swarm optimization（AQPSO） in the convergence speed and optimization results. The effectiveness and superiority of the CQFPA are also demonstrated by simulation experiments.

关键词： quantum flower pollination algorithm cultural algorithm IIR digital filter filter design

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