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Gaussian mutational chaotic fruit fly-built optimization and feature selection

EXPERT SYSTEMS WITH APPLICATIONS 2020年 141卷 112976-000页

作者： Zhang, Xiang Xu, Yueting Yu, Caiyang Heidari, Ali Asghar Li, Shimin Chen, Huiling Li, Chengye Wenzhou Mingcheng Construct Investment Grp Co Ltd Wenzhou 325000 Zhejiang Peoples R China Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Wenzhou Med Univ Dept Pulm & Crit Care Med Affiliated Hosp 1 Wenzhou 325000 Peoples R China

To cope with the potential shortcomings of classical fruit fly optimization algorithm (FOA), a new version of FOA with Gaussian mutation operator and the chaotic local search strategy (MCFOA) is proposed in this research. First, the Gaussian mutation operator is introduced into the basic FOA to avoid premature convergence and improve the exploitative tendencies in the algorithm (MFOA). Then, chaotic local search method is adopted for enhancing the local searching ability of the swarm of agents (CFOA). To substantiate the efficiency of three proposed methods, a comprehensive comparison has been completed using 23 benchmark functions with different characteristics. The best version of FOA among them is the MCFOA, which is extensively compared with the notable swarm-intelligence algorithms like bat algorithm (BA), particle swarm optimization algorithm (PSO), and several advanced FOA-based methods such as chaotic FOA (CIFOA), improved FOA (IFOA), multi-swarm FOA (swarm_MFOA) and differential evolution based FOA (DFOA). Numerical results show that two embedded strategies will effectively boost the performance of FOA for optimization tasks. In addition, MCFOA is also applied to feature selection problems. The results also prove that MCFOA can obtain the optimal classification accuracy. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： fruit fly optimization algorithm Chaotic local search Gaussian mutation Feature selection Global optimization

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STOCHASTIC FRACTAL BASED MULTIOBJECTIVE fruit fly optimization

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2017年第2期27卷 417-433页

作者： Zuo, Cili Wu, Lianghong Zeng, Zhao-Fu Wei, Hua-Liang Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Hunan Peoples R China Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance.

关键词： multiobjective optimization fruit fly optimization algorithm stochastic fractal

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The optimized evidence k-Nearest Neighbor based on FOA under the hesitant fuzzy environment and its application in classification

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第1期39卷 1119-1129页

作者： Song, Chenyang Xu, Zeshui Zhang, Yixin Army Engn Univ PLA Command & Control Engn Coll Nanjing Peoples R China Sichuan Univ Business Sch Chengdu Peoples R China

The k-Nearest Neighbor (k-NN) is one of the simplest intelligent algorithms in the field of pattern recognition and classification. The increasing complexity of practical applications brings more uncertainty and fuzziness. In this paper, we take advantage of the Dempster-Shafer evidence theory (D-S evidence theory) and the hesitant fuzzy set (HFS) in depicting uncertain preference and information, and develop the evidence k-Nearest Neighbor (Ek-NN) under the hesitant fuzzy environment. The fruit fly optimization algorithm (FOA) is adopted to determine the most appropriate value of k in Ek-NN, and a specific implementation process of the optimized Ek-NN based on FOA is also provided. Moreover, two numerical examples about classification problems are presented to evaluate the performance of the proposed method. Comparative analysis and sensitivity analysis are further conducted to illustrate the advantages of the optimized Ek-NN based on FOA under the hesitant fuzzy environment.

关键词： k-Nearest neighbor dempster-shafer evidence theory hesitant fuzzy set fruit fly optimization algorithm

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Parameter identification and sensitivity analysis of an improved LuGre friction model for magnetorheological elastomer base isolator

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MECCANICA 2015年第11期50卷 2691-2707页

作者： Yu, Yang Li, Yancheng Li, Jianchun Univ Technol Sydney Sch Civil & Environm Engn Fac Engn & Informat Technol Ctr Built Infrastruct Res Sydney NSW 2007 Australia

The recently-developed magnetorheological elastomer (MRE) base isolator can provide an instant change in the shear modulus and damping property under applied magnetic field, which makes it as an ideal device for the semi-active control in buildings and bridges. Previous studies show that this new device is featured with its nonlinear and hysteretic responses, and it is necessary to sufficiently understand its behaviour when adopting this device in control system. Although there are several models presented to predict the hysteresis of MRE base isolator, they are always suffered from some application limitations, e.g. high computation demand or complex model. To better interpret this complicated feature of the device, this work presents an improved LuGre friction model, which has been successfully used in modelling other magnetorheological device i.e. MR damper. In addition, an improved fruit fly optimization algorithm (IFFOA) is also proposed to identify the model parameters. In the improved algorithm, a transfer factor based on a self-adaptive step is added together with a three-dimensional searching space. This improvement can enhance the convergence rate of the algorithm and avoid the local optimum. Furthermore, to reduce the complexity of the model, the local and global parameter sensitivity analyses are conducted for model simplification. Eventually, the experimental measurements of device displacement, velocity and shear force are used to evaluate the performance of the proposed model and IFFOA.

关键词： Magnetorheological elastomer base isolator LuGre friction model Parameter identification Sensitivity analysis fruit fly optimization algorithm

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Robust aerodynamic shape optimization using a novel multi-objective evolutionary algorithm coupled with surrogate model

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2020年第4期62卷 1969-1987页

作者： Tian, Xu Li, Jie Northwestern Polytech Univ Sch Aeronaut Dept Fluid Mech Xian 710072 Peoples R China

The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm that has exhibited enormous values in engineering optimization. In this study, an effective multi-objective fruit fly optimization algorithm (MOFOA) incorporated with the Pareto dominance is investigated and applied to robust aerodynamic shape optimization considering uncertainties in the design process. In the MOFOA, the fruit flies are required to perform solution explorations within an adaptive search scope by flying around the Pareto non-dominated solutions. An enhanced clustering evolution mechanism of combining the cooperative local search and differential evolution operator is elaborately designed to enrich the exploratory capacities for complex optimization. Paired with the search strategy, the non-dominated sorting technique considering the quality as well as uniform distribution of Pareto solutions is adopted to deal with the multiple objectives. To generate high-quality and uniformly spread-out Pareto non-dominated solutions, the roulette wheel selection (RWS) operator based on crowding distance is employed to guide the selection processes of individuals throughout various stages of the MOFOA. The effectiveness and superiority of MOFOA are demonstrated by the comparative studies on several benchmark functions. Finally, a robust aerodynamic design system for combining the MOFOA optimizer with a Kriging-based surrogate model and full Navier-Stokes computation is advantageously implemented to perform shape optimization on a transonic airfoil using the Taguchi robust design methodology that emphasizes the inherent variability while improving engineering productivity. The design results show that the robust design, compared with the single-point design, is capable of producing a set of robust solutions that are significantly less sensitive to input variations by capturing the Pareto-optimal front with respect to the criteria of aerodynamic performance and its stability.

关键词： fruit fly optimization algorithm Multi-objective optimization Cooperative local search Differential evolution operator Pareto-optimal solutions Robust aerodynamic shape optimization

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Research on intelligent slice planning method for free-form surfaces of shaped workpieces

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第12期37卷 11214-11232页

作者： Du, Yuxiao Chen, Yihang Cai, Shuting Chen, Kongyang Li, Xianghuan Guangdong Univ Technol Sch Automation Guangzhou Peoples R China Guangzhou Univ Inst Artificial Intelligence & Blockchain Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China Jiangnan Univ Jiangsu Key Lab Media Design & Software Technol Wuxi Peoples R China Guangdong Univ Technol Guangzhou Peoples R China

A surface slicing planning method based on the K-means clustering algorithm and improved fruit fly optimization algorithm (FOA) is proposed to address the efficiency problem in the surface processing of special shaped workpieces. The NURBS surface reconstruction is performed on the complex surface of the selected workpiece, and the K-means clustering algorithm with the curvature-distance factor is used to subdivide the surface, and the FOA with the hopping strategy is used to plan the optimal connection path for each subdivision with different starting points. This improves the local search capability of the FOA by improving the mixing of different classes that occurs when the surface is subdivided. Finally, the proposed method is demonstrated to be effective for surface slicing by simulating the slicing plan on the constructed heterogeneous workpiece surface, and the shortest connectivity paths are obtained for different starting points.

关键词： curved surface slice fruit fly optimization algorithm K-means clustering traveling salesman problem

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Prediction and determination of mildew grade in grain storage based on FOA-SVM algorithm

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Food Quality and Safety 2023年第1期7卷 145-151页

作者： Jianghao Yuan Fang Tang Zhihui Qi Huiyi Zhao Academy of National Food and Strategic Reserves Administration BeijingChina Hebei Agricultural University BaodingChina National Engineering Research Center of Grain Storage and Logistics BiingChina

Grain mildew is a significant hazard that causes food loss and poses a serious threat to human health when ***,effective prediction and determination of mildew grade is essential for the prevention and control of mildew and global food *** the present study,a model for predicting and determining the mildew grade of rice was constructed using logistic regression,back propagation neural network and GS-SVM(a grid search-based support vector machine algorithm)based on laboratory culture data and actual data from a granary,*** results show that the GS-SVM model has a better prediction effect,but the model cannot automatically adjust the parameters and is more subjective,and the accuracy may decrease when the data set ***,this paper establishes a new model for a support vector machine based on a fruit fly optimization algorithm(FOA-SVM),which can achieve automatic parameter search and automatically adjust its parameters to find the best result when the data set changes,with a strong ability of self-adjustment of *** addition,the FOA-SVM converges quickly and the model is *** results of this study provide a technical method for early identification of mildew grade during grain storage,which is beneficial for the prevention and control of rice mildew during grain storage.

关键词： Identification of grain mildew support vector machine fruit fly optimization algorithm grain security

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Analysis and optimization of Cache-Enabled Fog Radio Access Networks: Successful Transmission Probability, Fractional Offloaded Traffic and Delay

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第5期69卷 5219-5231页

作者： Jiang, Yanxiang Peng, Aoao Wan, Chaoyi Cui, Ying You, Xiaohu Zheng, Fu-Chun Jin, Shi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Key Lab Wireless Sensor Network & Commun Shanghai 200050 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Shenzhen 518055 Peoples R China

In this paper, the edge caching optimization problem in fog radio access networks (F-RANs) is investigated. Considering that fog access points (F-APs) can offer cooperation gain by jointly transmitting the same file or content diversity gain by concurrently transmitting the coded subfiles, we propose to use both joint transmission (JT) strategy and parallel transmission (PT) strategy to serve users. Using tools from stochastic geometry, we first derive the expressions of successful transmission probability (STP) under different transmission strategies, and then derive the expressions of fractional offloaded traffic (FOT) and delay. We further formulate the corresponding edge caching optimization problem to maximize the STP, FOT or minimize the delay. Considering the NP-hard property of the above optimization problem, we propose an improved fruit fly optimization (FFO) based edge caching scheme, which utilizes the dynamic search step to avoid pre-mature convergence. Furthermore, we theoretically analyze the convergence and global optimality of our proposed scheme. Numerical results show that a significant performance gain is achieved by using our proposed edge caching scheme compared with the baseline schemes.

关键词： Fog radio access networks edge caching stochastic geometry successful transmission probability fractional offloaded traffic delay fruit fly optimization algorithm

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A novel fruit fly framework for multi-objective shape design of tubular linear synchronous motor

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JOURNAL OF SUPERCOMPUTING 2017年第3期73卷 1235-1256页

作者： Rizk-Allah, Rizk M. El-Sehiemy, Ragab A. Deb, Suash Wang, Gai-Ge Univ Minoufia Dept Basic Engn Sci Shibin Al Kawm Egypt Kafrelsheikh Univ Dept Elect Engn Fac Engn Kafr Al Sheikh Egypt Jiangsu Normal Univ Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada Northeast Normal Univ Sch Comp Sci & Informat Technol Changchun 130117 Peoples R China Northeast Normal Univ Inst Algorithm & Big Data Anal Changchun 130117 Peoples R China

This paper addresses a novel multi-objective fruit fly optimization algorithm (MOFOA) for solving multi-objective optimization problems. The essence of MOFOA lies in its having two characteristic features. For the first feature, a population of random fruit flies initializes the algorithm. During this initialization phase, the dominated fruit fly is replaced by the nearest non-dominated one. Subsequently, the fruit flies undergo evolution by flying randomly around the non-dominated solution or around the reference point, i.e., the best location of the individual objectives. Afterwards, the fruit flies are updated according to the nearest location whether from the reference point or the previous non-dominated location. For the second feature, the weighted sum method is incorporated to update the previous best locations of fruit flies and the reference point to emphasize the convergence of the non-dominated solutions. To prove the capability of the proposed MOFOA, two standard benchmark problems in addition to the real world application, namely, multi-objective shape design of tubular linear synchronous motor (TLSM) are checked. The corresponding TLSM objective functions aims to maximize operating force and to minimize the flux saturation. The outcomes clearly demonstrate the effectiveness of the proposed algorithm for finding the non-dominated solutions.

关键词： fruit fly optimization algorithm Multi-objective optimization problem Pareto-optimal solutions Reference point Tubular linear synchronous motor

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A hybrid fruit fly algorithm for solving flexible job-shop scheduling to reduce manufacturing carbon footprint

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JOURNAL OF CLEANER PRODUCTION 2017年 168卷 668-678页

作者： Liu, Qiong Zhan, Mengmeng Chekem, Freddy O. Shao, Xinyu Ying, Baosheng Sutherland, John W. Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Wuhan Univ Sci & Technol Sch Automobile & Traff Engn Wuhan 430081 Hubei Peoples R China Purdue Univ Environm & Ecol Engn W Lafayette IN 47906 USA

In order to help manufacturing companies to quantify product carbon footprints and provide carbon emission data in a job shop for future life cycle product carbon labelling, a calculating method of product carbon footprint in a job shop is proposed. The carbon emissions in a job shop are allocated to each product based on mapping relations between resources and products consuming the resources. To reduce product carbon footprints in a job shop, a multi-objective optimization model aimed at minimizing carbon footprints of all products and makespan is proposed. In order to solve the proposed model and explore the performance of fruit fly optimization algorithm (FOA) in multi-objective scheduling optimization problems, a Hybrid fruit fly optimization algorithm (HFOA) is designed. The proposed model and algorithm are verified by a case study. It is to provide decision makers awareness of carbon footprints for each product in a job shop and the product carbon footprint in job shops can be further used to calculate life cycle product carbon labelling. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Green manufacturing Flexible job-shop scheduling Carbon emission Carbon footprint fruit fly optimization algorithm

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