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A hybrid metaheuristic and kernel intuitionistic fuzzy c-means algorithm for cluster analysis

APPLIED SOFT COMPUTING 2018年 67卷 299-308页

作者： Kuo, R. J. Lin, T. C. Zulvia, F. E. Tsai, C. Y. Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Kee Lung Rd Taipei 10607 Taiwan Win Semicond 35Keji 7th RdHwaya Technol Pk Taoyuan 33383 Taiwan Univ Pertamina Dept Logist Engn Teuku Nyak Arief Rd Jakarta 12220 Indonesia Yuan Ze Univ Dept Ind Engn & Management 135 Yuan Tung Rd Taoyuan 32003 Taiwan

Cluster analysis is a very useful data mining approach. Although many clustering algorithms have been proposed, it is very difficult to find a clustering method which is suitable for all types of datasets. This study proposes an evolutionary-based clustering algorithm which combines a metaheuristic with a kernel intuitionistic fuzzy c-means (KIFCM) algorithm. The KIFCM algorithm improves the fuzzy c-means (FCM) algorithm by employing an intuitionistic fuzzy set and a kernel function. According to previous studies, the KIFCM algorithm is a promising algorithm. However, it still has a weakness due to its high sensitivity to initial centroids. Thus, this study overcomes this problem by using a metaheuristic algorithm to improve the KIFCM result. The metaheuristic can provide better initial centroids for the KIFCM algorithm. This study applies three metaheuristics, particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC) algorithms. Though the hybrid method is not new, this is the first paper to combine metaheuristics and KIFCM. The proposed algorithms, PSO-KIFCM, GA-KIFCM and ABC-KIFCM algorithms are evaluated using six benchmark datasets. The results are compared with some other clustering algorithms, namely K-means, FCM, Kernel fuzzy c-means (KFCM) and KIFCM algorithms. The results prove that the proposed algorithms achieve better accuracy. Furthermore, the proposed algorithms are applied to solve a case study on customer segmentation. This case study is taken from franchise stores selling women's clothing in Taiwan. For this case study, the proposed algorithms also exhibit better cluster construction than other tested algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Cluster analysis Metaheuristics Particle swarm optimization Genetic algorithm artificial bee colony algorithm Intuitionistic fuzzy set Kernel function Fuzzy c-meansa

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An Optimized artificial bee colony based Parameter Training Method for Belief Rule-Base

An Optimized Artificial Bee Colony based Parameter Training ...

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第六届中国计算机学会大数据学术会议

作者： Man-Na SU Zhi-Jian FANG Shao-Zhen YE Ying-Jie WU Yang-Geng FU College of Mathematics and Computer Science Fuzhou University

In order to solve the problems of poor portability,complex implemen-tation,and low efficiency in the traditional parameter training of the Belief rule-base,an artificial bee colony algorithm combined with Gaussian disturbance op-timization was introduced,and a novel Belief rule-base parameter training method was *** the light of the algorithm principle of the artificial bee colony,the honey bee colony search formula and the cross-border processing method were improved,and the Gaussian disturbance was employed to prevent the search from falling into a local *** parameter training was imple-mented in combination with the constraint conditions of the Belief *** fitting the multi-peak function and the leakage detection experiment of oil pipe-lines,the experimental error were compared with the traditional and existing pa-rameter training methods to verify its effectiveness.

关键词： Belief rule-base (BRB) artificial bee colony algorithm Swarm In-telligence Parameter optimization model

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A Speech Recognition System Based on Fuzzy Neural Network trained by artificial bee colony algorithm

A Speech Recognition System Based on Fuzzy Neural Network tr...

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IEEE International Conference on Electronics, Communications and Control (ICECC)

作者： Ning, Aiping Zhang, Xueying Taiyuan Univ Technol Coll Informat Engn Taiyuan Peoples R China

ISBN: (纸本)9781457703218

Training fuzzy neural network (FNN) is an optimization task which is desired to find optimal centers of the membership function and weights. Traditional training algorithms have some drawbacks such as getting stuck in local minima and computational complexity. This work presents FNN trained by artificial bee colony (ABC) optimization algorithm which has good exploration and exploitation capabilities. FNN trained by this algorithm is applied to speech recognition system and compares its performance with particle swarm optimization (PSO) algorithm and back-propagation (BP) algorithm. The experimental results prove that ABC algorithm has better recognition results and convergence speed than FNN trained by BP algorithm and has similar recognition results and convergence speed than FNN trained by PSO.

关键词： artificial bee colony algorithm particle swarm optimization algorithm Fuzzy Neural Network Speech Recognition

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Identification-based controller design using cloud model for course-keeping of ships in waves

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ENGINEERING APPLICATIONS OF artificial INTELLIGENCE 2018年 75卷 22-35页

作者： Zhu, Man Hahn, Axel Wen, Yuan-Qiao Carl von Ossietzky Univ Oldenburg Comp Sci D-26121 Oldenburg Germany Wuhan Univ Technol Sch Nav Wuhan 430063 Hubei Peoples R China Hubei Key Lab Inland Shipping Technol Wuhan 430063 Hubei Peoples R China

Course-keeping plays an important role in ensuring navigation safety of ships. This contribution proposed new approaches about system identification and controller design to facilitate the main goal which is to design a controller for course-keeping of ships based on the identified ship plant and the cloud model. The investigated plant as the control model was the first order linear Nomoto model. In order to estimate the parameters of this model, the support vector machines (SVM) optimized by artificial bee colony algorithm (ABC) was applied in combination with simulated data including the rudder and heading angles generated by the dynamic model of a Mariner class cargo ship. Based on the identified linear Nomoto model of Mariner class cargo ship, the cloud model was then applied to design the controller for course-keeping. To well demonstrate the feasibility and effectiveness of the proposed controller, a fuzzy logic PID controller, and a PID controller were also considered as comparison mechanisms. Aiming at validating the robustness of the proposed cloud model-based controller, it was used to compensate for the critical environmental disturbances induced by waves. Finally, simulation results indicate the desirable performance of ABC on optimizing parameters in SVM. Comparison with PID and fuzzy logic PID controllers demonstrates that the cloud model-based controller presents slightly preferable performance on course-keeping. This is due to that the flexible and intuitive modification of the digital characteristics of the cloud model and the structure of cloud inference engines makes the cloud model efficient to satisfy the required mapping between inputs and outputs for a controller.

关键词： Parameter identification Cloud model-based controller Ship course-keeping Support vector machines artificial bee colony algorithm

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A novel strategy for the combinatorial production planning problem using integer variables and performance evaluation of recent optimization algorithms

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 43卷 225-243页

作者： Chauhan, Sandeep Singh Sivadurgaprasad, Chinta Kadambur, Rajasekhar Kotecha, Prakash Indian Inst Technol Guwahati Dept Chem Engn Gauhati 781039 Assam India

In this article we propose a multi-unit production planning based optimization strategy that employs a set of integer and continuous variables to overcome many of the drawbacks of the formulation/strategies in literature [1,2] and helps in determining efficient production plans. Additionally, we have utilized an efficient strategy to handle the domain-hole constraints that does not rely on imposing penalties for violation. The benefits of the proposed strategy are demonstrated on eight cases, which have been previously discussed in literature to potentially guide the petrochemical industries, and provide an improvement of up to 34.66% in the profit. In addition to proposing an efficient strategy, this work also discusses the computational performance of five optimization algorithms viz., the recently proposed sanitized-teaching-learning-based optimization algorithm, multi-population based ensemble differential evolution, enhanced binary particle swarm optimization algorithm, artificial bee colony algorithm and the popular real coded genetic algorithm on this combinatorial optimization problem.

关键词： Sanitized teaching-learning-based optimization Genetic algorithm Multi-population based ensemble differential evolution algorithm Enhanced binary particle swarm optimization algorithm artificial bee colony algorithm Production planning Combinatorial optimization

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Multi-objective service composition model based on cost-effective optimization

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APPLIED INTELLIGENCE 2018年第3期48卷 651-669页

作者： Huo, Ying Qiu, Peng Zhai, Jiyou Fan, Dajuan Peng, Huanfeng Nanjing Inst Technol Dept Comp Engn Nanjing 211167 Jiangsu Peoples R China

The widespread application of cloud computing results in the exuberant growth of services with the same functionality. Quality of service (QoS) is mostly applied to represent nonfunctional properties of services, and has become an important basis for service selection. The object of most existing optimization methods is to maximize the QoS, which restricts the diversity of users' requirements. In this paper, instead of optimization for the single object, we take maximization of QoS and minimization of cost as two objects, and a novel multi-objective service composition model based on cost-effective optimization is designed according to the complicated QoS requirements of users. Furthermore, to solve this complex optimization problem, the Elite-guided Multi-objective artificial bee colony (EMOABC) algorithm is proposed from the addition of fast nondominated sorting method, population selection strategy, elite-guided discrete solution generation strategy and multi-objective fitness calculation method into the original ABC algorithm. The experiments on two datasets demonstrate that EMOABC has an advantage both on the quality of solution and efficiency as compared to other algorithms. Therefore, the proposed method can be better applicable to the cloud services selection and composition.

关键词： Quality of service Service composition Cost-effective Multi-objective optimization artificial bee colony algorithm

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An algorithm inspired by bee colonies coupled to an adaptive penalty method for truss structural optimization problems

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2019年第3期41卷 126-126页

作者： de Castro Lemonge, Afonso Celso Duarte, Grasiele Regina da Fonseca, Leonardo Goliatt Univ Fed Juiz de Fora Dept Appl & Computat Mech Juiz De Fora Brazil Univ Fed Juiz de Fora Grad Program Computat Modeling Juiz De Fora Brazil

The constrained optimization problems are very common in the engineering field. For instance, in civil, aeronautical, mechanical engineering and so on, this type of problem is largely used to find the best designs of structures leading to a structural optimization problem to be solved. Commonly, these problems consist in to find structures with the minimum weight, subject to a set of constraints such as allowable stress, displacements, natural frequencies of vibration and stability criteria. Besides the traditional optimization methods, consolidated through the decades, the evolutionary algorithms, in general inspired by natural phenomenona, have been playing an important role showing robustness to solve this kind of problem. In 2005, the artificial bee colony algorithm (ABC), inspired by the foraging of bee colonies, was proposed to solve multimodal and multidimensional optimization problems. This paper proposes, analyzes and discusses the coupling of ABC to variants of an adaptive penalty method, handling the constraints, to solve traditional problems of truss structural optimization. The results obtained are compared with the literature showing that the proposed strategy can be efficient and competitive.

关键词： Constrained optimization Truss structural optimization artificial bee colony algorithm Adaptive penalty method

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Interval quantile regression models based on swarm intelligence

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APPLIED SOFT COMPUTING 2018年 72卷 474-485页

作者： Soares, Yanne M. G. Fagundes, Roberta A. A. Univ Pernambuco UPE Dept Engn Comp Rua Benfica 455 BR-50720001 Recife PE Brazil

This paper presents quantile regression models for classical data and interval symbolic data using algorithms based on swarm intelligence to estimate the parameters aiming to improve the model performance. Also, these methods are compared with methods of estimation based on linear programming widely used in the literature. Applications using real and simulated data are considered. The prediction quality is assessed by the mean magnitude of relative error calculated from test data. The growth of symbolic data nature of alerts to the need of the new statistical methodologies development for the treatment of this type of information. The results show that the proposed models are effective alternatives for optimizing the choice of parameters of the quantile regression, providing greater precision and robustness than models based on linear programming. (C) 2018 Published by Elsevier B.V.

关键词： Quantile regression Swarm intelligence Symbolic data analysis Particle swarm optimization artificial bee colony algorithm

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Optimisation of droop coefficients of multiple distributed generators in a micro-grid

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第18期12卷 4108-4116页

作者： Manna, Dulal Goswami, Swapan K. Chattopadhyay, Pranab Kumar Jadavpur Univ Dept Elect Engn Kolkata 700032 India

y An optimisation technique employing artificial bee colony algorithm has been proposed for determining the droop parameters of the distributed generators in a micro-grid, operating in isolated mode. The proposed method minimises the active power loss and maintains a flat voltage profile. The bus interfacing the main and the micro-grid has been treated as a voltage regulated bus so as to facilitate seamless transfer from the autonomous to the grid-connected mode of operation and conversely.

关键词： voltage regulators optimisation voltage control power generation control distributed power generation droop coefficients multiple distributed generators microgrid optimisation technique artificial bee colony algorithm droop parameters isolated mode active power loss flat voltage profile

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Artifical bee colony algorithm using problem-specific neighborhood strategies for the tree t-spanner problem

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APPLIED SOFT COMPUTING 2018年 62卷 110-118页

作者： Singh, Kavita Sundar, Shyam Natl Inst Technol Raipur Dept Comp Applicat Raipur 492010 Madhya Pradesh India

A tree t-spanner is a spanning tree T in which the ratio of distance between every pair of vertices is at most t times their shortest distance in a connected graph, where t is a value called stretch factor of T. On a given connected, undirected, and weighted graph, this paper studies the tree t-spanner problem( Tree t-SP) that aims to find a spanning tree whose stretch factor is minimum amongst all spanning trees of the graph. Being a NP-Hard for any fixed t > 1, this problem is under-studied in the domain of metaheuristic techniques. In literature, only genetic algorithm has been proposed for this problem. This paper presents an artificial bee colony (ABC) algorithm for this problem, where ABC algorithm is a swarm intelligence technique inspired by intelligent foraging behavior of honey bees. Neighborhood strategies of ABC algorithm particularly employ problem-specific knowledge that makes ABC algorithm highly effective in searching high quality solutions in less computational time. Computational experiments on a large set of randomly generated graph instances exhibit superior performance of ABC algorithm over the existing genetic algorithm for the Tree t-SP. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Tree spanner Weighted graph Problem-specific knowledge Swarm intelligence artificial bee colony algorithm

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