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TWIN SUPPORT VECTOR REGRESSION BASED ON fruit fly optimization algorithm

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2019年第5期15卷 1851-1864页

作者： Fang, Jianwen Pan, Feng Gu, Binjie Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ 1800 Lihu Rd Wuxi 214122 Jiangsu Peoples R China

Recently, Twin Support Vector Regression (TSVR), which determines a pair of epsilon-insensitive lower and upper bound functions by solving two related SVR-type problems, has become a new hot topic in machine learning field. However, at least four parameters should be appropriately specified in TSVR. In this paper, in order to obtain the optimal parameters of TSVR, we proposed a twin support vector regression based on fruit fly optimization algorithm. First, we represented the parameters to be optimized in TSVR by the locations of the fruit flies. Then, we used fitting regression precision as fitness function, and let fruit flies fly randomly to avoid trapping into local minimum. Finally, we could find the highest regression accuracy corresponding to the final position of the fruit flies within finite iterations. The experimental results on benchmark datasets and glutamic acid fed-batch fermentation process show that the proposed algorithm can be used to find suitable parameters for TSVR. Furthermore, our algorithm costs less optimization time than other state-of-the-art algorithms.

关键词： Twin support vector regression fruit fly optimization algorithm Fitness function Parameter selection

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Wind Power Day-ahead Prediction Based on LSSVM With fruit fly optimization algorithm

Wind Power Day-ahead Prediction Based on LSSVM With Fruit Fl...

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International Conference on Power System Technology (POWERCON)

作者： Wang, Lijie Wang, Zheng Wang, Bo Beijing Informat Sci & Technol Univ Beijing 100192 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China

ISBN: (纸本)9781538664612

The accuracy of least squares support vector machine (LSSVM) for wind power prediction is greatly affected by its parameters. To solve the problem of the man-made choice of the parameter values, a model for day-ahead wind power prediction based on fruit fly optimization algorithm (FOA) is proposed in the paper. For day-ahead prediction, numerical weather prediction (NWP) including wind speed, wind direction, temperature and atmospheric pressure has great influence on wind power. LSSVM is adopted to model the non-linear relationship in the study. FOA is employed to search for the optimal parameters of LSSVM. The simulation show that the new method based on FOA has better prediction properties than the model based on particle swarm optimization.

关键词： Wind power day-ahead prediction numerical weather prediction least squares support vector machine fruit fly optimization algorithm

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Improved fruit fly optimization algorithm for Traveling Salesman Problem 33

Improved Fruit Fly Optimization Algorithm for Traveling Sale...

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33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Pan, Zixiao Chen, Yang Cheng, Wei Guo, Dongyu Wuhan Univ Technol Sch Automat Wuhan Hubei Peoples R China Wuhan Univ Technol Dept Math Wuhan Hubei Peoples R China

ISBN: (纸本)9781538672556

An improved fruit fly optimization algorithm (FOA) is proposed to solve the traveling salesman problem. In this paper, in order to use FOA to solve the discrete problems, a new method of Visual Search is presented. The fruit fly movement process is proposed to avoid the fruit fly optimization algorithm fall into the local optimal solution. A novel strategy is used to enhance the good individuals in the fruit fly population. The proposed algorithm is evaluated on the TSP test problems. The experimental results show that this algorithm presents extreme fast convergence speed and accuracy.

关键词： traveling salesman problem fruit fly optimization algorithm visual search fruit fly movement

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An Enhanced fruit fly optimization algorithm Based on Elitist Learning and Differential Perturbation Strategy 11

An Enhanced Fruit Fly Optimization Algorithm Based on Elitis...

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11th International Symposium on Computational Intelligence and Design (ISCID)

作者： Li, Gang Tian, Tian Chen, Jicheng Wang, Xiang Xian Technol Univ Sch Mechatron Engn Xian 710032 Shaanxi Peoples R China AVIC Xian Aircraft Ind Grp Co LTD Xian 710089 Shaanxi Peoples R China AVIC Xian Aviat Inst Comp Technol Xian 710068 Shaanxi Peoples R China

ISBN: (纸本)9781538685273

Aim at the problem that original fruit fly optimization algorithm (FOA) has some disadvantages of low convergence precision, slow convergence rate and easily relapsing into local extremum, an enhanced fruit fly optimization algorithm based on elitist learning and differential perturbation strategy is put forward. In this study, a smell concentration based subgroup collaboration strategy is utilized, where the fruit fly swarm can be divided into excellent subgroup and general subgroup which have quite different evolutionary routes. Additionally, in order to enhance the search efficiency and keep the diversity of solutions, the elitist learning and differential perturbation strategy are adopted to coordinate the exploitation ability and the exploration ability of algorithm. Experimental results on six typical benchmark function optimization problems demonstrate the effectiveness of the algorithm as an optimization technique. Compared with original fruit fly optimization algorithm for optimization task, the presented algorithm has the ability to search better solutions and its optimization performance is clearly better than that of the original algorithm.

关键词： fruit fly optimization algorithm elitist learning differential perturbation function optimization

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An Application of Improved fruit fly optimization algorithm for Vibration Isolation System 11

An Application of Improved Fruit Fly Optimization Algorithm ...

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11th International Symposium on Computational Intelligence and Design (ISCID)

ISBN: (纸本)9781538685273

Vibration isolation system is widely used in various engineering fields. The efficiency of vibration isolation depends on its structural parameters. In this study, an improved fruit fly optimization algorithm is put forward for the parametric optimal design of vibration isolation system. With the isolator parameters as optimal variables, the encoding scheme of fruit fly individuals, smell concentration judgment value function are introduced. In order to enhance the search efficiency and keep the diversity of solutions, adaptive smell-based search and vision-based search with random perturbation strategy are employed to coordinate the exploitation ability and the exploration ability of algorithm. Experimental results on optimal parameters design of two-stage vibration isolation system demonstrate the effectiveness of the algorithm. As an optimization technique, the method can be used for vibration isolation system design effort.

关键词： fruit fly optimization algorithm vibration isolation adaptive search random perturbation parametric optimal design

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An Improved fruit fly optimization algorithm for Solving Multidimensional Knapsack Problem 37

An Improved Fruit Fly Optimization Algorithm for Solving Mul...

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37th Chinese Control Conference (CCC)

作者： Qian, Hao Zhang, Qing-yong Wuhan Univ Technol Wuhan 430070 Hubei Peoples R China

ISBN: (纸本)9789881563958

In this paper, a novel improved fruit fly optimization algorithm (IFOA) is proposed for solving the multidimensional knapsack problem (MKP), which is characterized as high dimension and strong constraint. Initial swarms are generated according to the probability vector respectively. After the smell-based searching accomplishing, a repair operator granded on the pseudo-utility ratio, which is calculated by solving the dual problem of linear programming relaxion of MKP, is applied to guarantee the feasibility and enhance the quality of solutions. A swarm reduction strategy is used to balance the searching ability and convergence speed. Numerous tests and comparison with other algorithms based on two sets of benchmark problems demonstrate that IFOA is an efficient algorithm to solve MKP.

关键词： Multidimensional knapsack problem fruit fly optimization algorithm Swarm reduction

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An improved fruit fly optimization algorithm for solving high dimensional problems 37

An improved fruit fly optimization algorithm for solving hig...

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37th Chinese Control Conference (CCC)

作者： Cheng, Xiaohong Chen, Liding Xu, Bugong South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Guangdong Peoples R China

ISBN: (纸本)9789881563958

As a new optimization algorithm, fruit fly optimization algorithm (FOA) attracts a lot of attentions. By analyzing the probability of FOA jumping out of the local optimal range, we verified that FOA is ineffective in solving complex optimization problems whose optimal solution is nonzero. In order to improve the performance of FOA, a Modified Global fruit fly optimization algorithm (MGFOA) is introduced in this paper. In MGFOA, a uniform mechanism to produce the candidate solution is used to improve the global searching ability, a self-adaptive way to control the flight range is adapted to increase the optimize accuracy, and a ladder growth way of population is introduced to imitate the detection behavior of fruit fly. The experiment on 12 benchmark functions shows that MGFOA is more effective and robust than basic FOA, Global Particle Swarm optimization algorithm (GPSO) and another improved FOA (LGMS-FOA).

关键词： Meta-heuristic algorithms fruit fly optimization algorithm Global search

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An improved fruit fly optimization algorithm for solving traveling salesman problem

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Frontiers of Information Technology & Electronic Engineering 2017年第10期18卷 1525-1533页

作者： Lan HUANG Gui-chao WANG Tian BAI Zhe WANG College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering (Jilin University) Ministry of Education Changchun 130012 China

The traveling salesman problem(TSP), a typical non-deterministic polynomial(NP) hard problem, has been used in many engineering applications. As a new swarm-intelligence optimization algorithm, the fruit fly optimization algorithm(FOA) is used to solve TSP, since it has the advantages of being easy to understand and having a simple implementation. However, it has problems, including a slow convergence rate for the algorithm, easily falling into the local optimum, and an insufficient optimization precision. To address TSP effectively, three improvements are proposed in this paper to improve FOA. First, the vision search process is reinforced in the foraging behavior of fruit flies to improve the convergence rate of FOA. Second, an elimination mechanism is added to FOA to increase the diversity. Third, a reverse operator and a multiplication operator are proposed. They are performed on the solution sequence in the fruit fly's smell search and vision search processes, respectively. In the experiment, 10 benchmarks selected from TSPLIB are tested. The results show that the improved FOA outperforms other alternatives in terms of the convergence rate and precision.

关键词： Traveling salesman problem fruit fly optimization algorithm Elimination mechanism Vision search Operator

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A bilevel improved fruit fly optimization algorithm for the nonlinear bilevel programming problem

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KNOWLEDGE-BASED SYSTEMS 2017年第Dec.15期138卷 113-123页

作者： Wang, Guangmin Ma, Linmao Chen, Jiawei China Univ Geosci Sch Econ & Management Wuhan 430074 Peoples R China Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China

This paper proposes a bilevel improved fruit fly optimization algorithm (BIFOA) to address the nonlinear bilevel programming problem (NBLPP). Considering the hierarchical nature of the problem, this algorithm is constructed by combining two sole improved fruit fly optimization algorithms. In the proposed algorithm, the lower level problem is treated as a common nonlinear programming problem rather than being transformed into the constraints of the upper level problem. Eventually, 10 test problems are selected involving low-dimensional and high-dimensional problems to evaluate the performance of BIFOA from the aspects of the accuracy and stability of the solutions. The results of extensive numerical experiments and comparisons reveal that the proposed algorithm outperforms the compared algorithms and is significantly better than the methods presented in the literature;the proposed algorithm is an effective and comparable algorithm for NBLPP. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Bilevel programming problem Nonlinear bilevel programming problem fruit fly optimization algorithm Improved fruit fly optimization algorithm

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Power load forecasting by wavelet least squares support vector machine with improved fruit fly optimization algorithm

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JOURNAL OF COMBINATORIAL optimization 2017年第3期33卷 1122-1143页

作者： Niu Dongxiao Ma Tiannan Liu Bingyi North China Elect Power Univ Dept Business Adm Beijing 102206 Peoples R China

As is affected by many factors, mid-long term power load forecasting has become the nonlinear and multi-dimension complex problem, and its accuracy affects the decision and layout of power generation sector. In order to improve the accuracy and convergence ability of the single least square support vector machine (LSSVM), this paper proposes the improved fruit fly optimization algorithm applied to wavelet least square support vector machine (IFOA-w-LSSVM). Firstly, the Gaussian kernel function of LSSVM is replaced by the wavelet kernel function and wavelet least square support vector machine (w-LSSVM) is built. Secondly, the ordinary fruit fly optimization algorithm (FOA) is improved from three aspects: (1) dividing fruit fly group into two parts: (2) improving the taste detection function;(3) using Cauchy mutation process to make fruit fly individuals variant. Finally, w-LSSVM is optimized by IFOA for seeking the optimal parameters and achieving the forecasting accuracy. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in mid-long term power load forecasting.

关键词： Power load forecasting fruit fly optimization algorithm Least square support vector machine Wavelet kernel function

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