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Empirical analysis of five nature-inspired algorithms on real parameter optimization problems

artificial INTELLIGENCE REVIEW 2018年第3期50卷 383-439页

作者： Agarwal, Parul Mehta, Shikha Jaypee Inst Informat Technol Dept Comp Sci & Informat Technol Sect 62 Noida 201301 India

In the past few years nature-inspired algorithms are seen as potential tools to solve computationally hard problems. Tremendous success of these algorithms in providing near optimal solutions has inspired the researchers to develop new algorithms. However, very limited efforts have been made to identify the best algorithms for diverse classes of problems. This work attempts to assess the efficacy of five contemporary nature-inspired algorithms i.e. bat algorithm (BA), artificial bee colony algorithm (ABC), cuckoo search algorithm (CS), firefly algorithm (FA) and flower pollination algorithm (FPA). The work evaluates the performance of these algorithms on CEC2014 30 benchmark functions which include unimodal, multimodal, hybrid and composite problems over 10, 30, 50 and 100 dimensions. Control parameters of all algorithms are self-adapted so as to obtain best results over benchmark functions. The algorithms have been evaluated along three perspectives (a) statistical significance using Wilcoxon rank sum test (b) computational time complexity (c) convergence rate of algorithms. Experimental results and analysis revealed that ABC algorithm perform best for majority of the problems on high dimension, while on small dimension, CS is the best choice. FPA attain the next best position follow by BA and FA for all kinds of functions. Self adaptation of above algorithms also revealed the best values of input parameters for various algorithms. This study may aid experts and scientists of computational intelligence to solve intricate optimization problems.

关键词： artificial bee colony algorithm Computational time complexity Wilcoxon rank sum test Convergence rate

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Optimization Of University Course Scheduling Problem With A Hybrid artificial bee colony algorithm

Optimization Of University Course Scheduling Problem With A ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Oner, Adalet Ozcan, Sel Dengi, Derya Yasar Univ Dept Ind Engn Izmir Turkey

ISBN: (纸本)9781424478354

Course scheduling problem (CSP) is concerned with developing a timetable that illustrates a number of courses assigned to the classrooms. In this study, a hybrid algorithm composed of a heuristic graph node coloring (GNC) algorithm and artificial bee colony (ABC) algorithm is proposed to solve CSP. The study is one of the few applications of ABC on discrete optimization problems and to our best knowledge it is the first application on CSP. A basic heuristic algorithm of node coloring problem takes part initially to develop some feasible solutions of CSP. Those feasible solutions correspond to the food sources in ABC algorithm. The ABC is then is used to improve the feasible solutions. The employed and onlooker bees are directed or controlled in a specific manner in order to avoid the conflicts in the course timetable. Proposed solution procedure is tested using real data from a university in Turkey. The experimental results demonstrate that the proposed hybrid algorithm yields efficient solutions.

关键词： course scheduling problem node coloring artificial bee colony algorithm

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Optimized support vector regression algorithm-based modeling of ship dynamics

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APPLIED OCEAN RESEARCH 2019年 90卷

作者： Zhu, Man Hahn, Axel Wen, Yuan-Qiao Sun, Wu-Qiang Wuhan Univ Technol Intelligent Transportat Syst Res Ctr Wuhan 430063 Hubei Peoples R China Carl von Ossietzky Univ Oldenburg Comp Sci D-26121 Oldenburg Germany Natl Engn Res Ctr Water Transport Safety Wuhan 430063 Hubei Peoples R China Hubei Key Lab Inland Shipping Technol Wuhan 430063 Hubei Peoples R China Sunwin Intelligent Co Ltd Hefei 230000 Anhui Peoples R China

This study contributes to solving the problem of how to derive a simplistic model feasible for describing dynamics of different types of ships for maneuvering simulation employed to study maritime traffic and furthermore to provide ship models for simulation-based engineering test-beds. The problem is first addressed with the modification and simplification of a complicated and nonlinearly coupling vectorial representation in 6 degrees of freedom (DOF) to a 3 DOF model in a simple form for simultaneously capturing surge motions and steering motions based on several pieces of reasonable assumptions. The created simple dynamic model is aiming to be useful for different types of ships only with minor modifications on the experiment setup. Another issue concerning the proposed problem is the estimation of parameters in the model through a suitable technique, which is investigated by using the system identification in combination with full-scale ship trail tests, e.g., standard zigzag maneuvers. To improve the global optimization ability of support vector regression algorithm (SVR) based identification method, the artificial bee colony algorithm (ABC) presenting superior optimization performance with the advantage of few control parameters is used to optimize and assign the particular settings for structural parameters of SVR. Afterward, the simulation study on identifying a simplified dynamic model for a large container ship verifies the effectiveness of the optimized identification method at the same time inspires special considerations on further simplification of the initially simplified dynamic model. Finally, the further simplified dynamic model is validated through not only the simulation study on a container ship but also the experimental study on an unmanned surface vessel so-called I-Nav-II vessel. Either simulation study results or experimental study results demonstrate a valid model in a simple form for describing the dynamics of different types' ships and al

关键词： Parameter estimation Simplified ship dynamic model Different types of ships Support vector regression algorithm artificial bee colony algorithm Maritime traffic simulation

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Effective de-noising of ECG by optimised adaptive thresholding on noisy modes

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2018年第5期12卷 640-644页

作者： Jain, Shweta Bajaj, Varun Kumar, Anil PDPM Indian Inst Informat Technol Design & Mfg Elect & Commun Engn Jabalpur India

Electrocardiograph (ECG) denoising is a preliminary step in QRS complex detection and ECG beat classification process. An ECG denoising methodology is proposed that uses ensemble empirical mode decomposition (EEMD) for decomposing input ECG to its intrinsic mode functions (IMFs). Then noisy-IMFs are identified by the proposed method that uses squared distance similarity measure. On selected noisy-IMFs, hard thresholding, by the evolutionary optimisation algorithms (EOAs)-based thresholds, is applied. For selecting the best performing EOA, algorithms namely artificial bee colony, particle swarm optimisation (PSO), and cuckoo search (CS) optimisation are implemented. The proposed methodology is tested with the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) arrhythmia database and is evaluated in terms of signal-to-noise ratio (SNR) and mean-square error (MSE). In comparison with state-of-the-art methods, the proposed method is found to be the best attaining maximum SNR and MSE for PSO and CS algorithm optimised thresholding.

关键词： electrocardiography Hilbert transforms evolutionary computation artificial bee colony algorithm particle swarm optimisation search problems mean square error methods medical signal processing adaptive noise thresholding electrocardiograph denoising QRS complex detection ECG beat classification process ECG denoising methodology ensemble empirical mode decomposition EEMD intrinsic mode function IMF evolutionary optimisation algorithm EOA artificial bee colony algorithm particle swarm optimisation PSO cuckoo search optimisation CS optimisation MIT-BIH arrhythmia database signal-to-noise ratio SNR mean-square error method MSE method

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Improved artificial bee colony algorithm with Chaos

Improved Artificial Bee Colony Algorithm with Chaos

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International Workshop on Computer Science for Environmental Engineering and EcoInformatics (CSEEE 2011)

作者： Wu, Bin Fan, Shu-hai Nanjing Univ Technol Coll Econ & Mangement Nanjing 210009 Jiangsu Peoples R China

ISBN: (纸本)9783642226939

artificial bee colony (ABC) algorithm is the one of the newest nature inspired heuristics for optimization problem. Like the chaos in real bee colony behavior, this paper proposes a Chaotic ABC (CABC) that use chaotic searching behavior for candidate food position produced in order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions. To show the performance of our proposed CABC, a number of experiments are carried out on a set of well-known benchmark continuous optimization problems. Simulation results and comparisons with the standard ABC and several meta-heuristics show that the CABC can effectively enhance the searching efficiency and greatly improve the searching quality.

关键词： artificial bee colony algorithm Chaos Global Numerical Optimization

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A Discrete artificial bee colony algorithm For the Economic Lot Scheduling Problem

A Discrete Artificial Bee Colony Algorithm For the Economic ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Tasgetiren, M. Fatih Bulut, Onder Fadiloglu, M. Murat Yasar Univ Dept Ind Engn Izmir Turkey Izmir Econ Univ Ind Engn Dept Izmir Turkey

ISBN: (纸本)9781424478354

In this study we present a discrete artificial bee colony (DABC) algorithm to solve the economic lot scheduling problem (ELSP) under extended basic period (EBP) approach and power-of-two (PoT) policy. In specific, our algorithm provides a cyclic production schedule of n items to be produced on a single machine such that the production cycle of each item is an integer multiple of a fundamental cycle. All the integer multipliers are in the form of power-of-two, and under EBP approach feasibility is guaranteed with a constraint that checks if the items assigned in each period can be produced within the length of the period. For this problem, which is NP-hard, our DABC algorithm employs a multi-chromosome solution representation to encode power-of-two multipliers and the production positions separately. Both feasible and infeasible solutions are maintained in the population through the use of some sophisticated constraint handling methods. A variable neighborhood search (VNS) algorithm is also fused into DABC algorithm to further enhance the solution quality. The experimental results show that the proposed algorithm is very competitive to the best performing algorithms from the existing literature under the EBP and PoT policy.

关键词： economic lot scheduling problem extended basic period power-of-two policy artificial bee colony algorithm

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

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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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Two swarm intelligence approaches for tuning extreme learning machine

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2018年第8期9卷 1271-1283页

作者： Alshamiri, Abobakr Khalil Singh, Alok Surampudi, Bapi Raju Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 India Int Inst Informat Technol Cognit Sci Lab Hyderabad 500032 India

Extreme learning machine (ELM) is a new algorithm for training single-hidden layer feedforward neural networks which provides good performance as well as fast learning speed. ELM tends to produce good generalization performance with large number of hidden neurons as the input weights and hidden neurons biases are randomly initialized and remain unchanged during the learning process, and the output weights are analytically determined. In this paper, two swarm intelligence based metaheuristic techniques, viz. artificial bee colony (ABC) and Invasive Weed Optimization (IWO) are proposed for tuning the input weights and hidden biases. The proposed approaches are called ABC-ELM and IWO-ELM in which the input weights and hidden biases are selected using ABC and IWO respectively and the output weights are computed using the Moore-Penrose (MP) generalized inverse. The proposed approaches are tested on different benchmark classification data sets and simulations show that the proposed approaches obtain good generalization performance in comparison to the other techniques available in the literature.

关键词： artificial bee colony algorithm Classification Extreme learning machine Invasive weed optimization Swarm intelligence

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An Improved Fuzzy C-Means Clustering algorithm Based on Multi-chain Quantum bee colony Optimization

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WIRELESS PERSONAL COMMUNICATIONS 2018年第2期102卷 1421-1441页

作者： Feng, Yufang Lu, Houqing Xie, Wenbin Yin, Hong Bai, Jingbo Army Engn Univ PLA Nanjing Jiangsu Peoples R China

The fuzzy c-means (FCM) algorithm is the most popular clustering method. Many studies of FCM had been done. However, the FCM algorithm and its studies are usually affected by the selection of initial values and noise data, and can easily fall into local optimal value. To overcome these drawbacks of FCM, this paper proposed the algorithm of FCM based on multi-chain quantum bee colony algorithm (MQBC-FCM). In MQBC-FCM, first, the multiple chains encoding method is introduced to the artificial bee colony algorithm to propose the MQBC algorithm. Then MQBC is used to search for the optimal initial clustering centers. The proposed algorithm is used on artificial data sets and image segmentations, and its performance is contrasted with several algorithms. The experimental results have indicated that the proposed MQBC-FCM has efficiently improved the performance of the clustering algorithm.

关键词： Fuzzy c-means artificial bee colony algorithm Expansion of multi-chain coding Gene chain Quantum bee colony algorithm

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Evaluating the capability of distribution networks to maintain power quality and voltage level, case study: Alborz Electric Power Distribution Company

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第16期12卷 3883-3891页

作者： Jamshidieini, Bahman Rezaie, Kamran Firuzabad, Mahmud Fotuhi Univ Tehran Fac Engn Tehran Iran Sharif Univ Technol Fac Elect Engn Tehran Iran

Distribution system operators that seek operational excellence are supposed to design and implement capable processes throughout their value stream and assess how well their real services match the design specifications. Process capability analysis is a critical stage in systematic and sophisticated quality engineering approaches such as six sigma, which are based on quantitative studies. The capability of a distribution network to maintain the power quality and voltage level within an acceptable range is one of the most important aspects of design and utilisation of electricity distribution network. Capability analysis has been successfully used in the previous studies to assess the quality of conformance in the power distribution network. However, these studies were limited to a voltage level and other aspects of quality such as total harmonic distortion were neglected. In addition, parametric methods were not available to overcome the problem of multimodality in voltage distribution, especially in the presence of distributed generation. In the current study, for the first time, a parametric approach based on decomposing complex distributions to a few Gaussian distributions by artificial bee colony optimisation were used for evaluating the capability of the distribution network to maintain voltage level and power quality indices in predefined ranges.

关键词： distribution networks power supply quality artificial bee colony algorithm six sigma (quality) Gaussian distribution electricity distribution network utilisation power quality indices voltage level Alborz Electric Power Distribution Company distribution system operators value stream process capability analysis quality engineering approaches six sigma artificial bee colony optimisation Gaussian distributions complex distributions distributed generation

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