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Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting

ENERGY 2016年第Part1期115卷 734-745页

作者： Cao, Guohua Wu, Lijuan Chongqing Univ Sch Econ & Business Adm Chongqing 400030 Peoples R China

Accurate monthly electricity consumption forecasting can provide the reliable guidance for better energy planning and administration. However, it has been found that the monthly electricity consumption demonstrates a complex nonlinear characteristic and an obvious seasonal tendency. Support vector regression has been widely applied to handle nonlinear time series prediction, but it suffers from the key parameters selection and the influence of seasonal tendency. This paper proposes a novel approach, which hybridizes support vector regression model with fruit fly optimization algorithm and the seasonal index adjustment to forecast monthly electricity consumption. Besides, in order to comprehensively evaluate the forecasting performance of the hybrid model, a small sample of monthly electricity consumption of China and a large sample of monthly electricity retail sales of the United States were employed to demonstrate the forecasting performance. The results show that the proposed hybrid approach is a viable option for the electricity consumption forecasting applications. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Support vector regression fruit fly optimization algorithm Electricity consumption forecasting Seasonal mechanism

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Chaotic fruit fly optimization algorithm

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KNOWLEDGE-BASED SYSTEMS 2015年 89卷 446-458页

作者： Mitic, Marko Vukovic, Najdan Petrovic, Milica Miljkovic, Zoran Univ Belgrade Fac Mech Engn Dept Prod Engn Belgrade 11120 35 Serbia Univ Belgrade Fac Mech Engn Innovat Ctr Belgrade 11120 35 Serbia

fruit fly optimization algorithm (FOA) is recently presented metaheuristic technique that is inspired by the behavior of fruit flies. This paper improves the standard FOA by introducing the novel parameter integrated with chaos. The performance of developed chaotic fruit fly algorithm (CFOA) is investigated in details on ten well known benchmark problems using fourteen different chaotic maps. Moreover, we performed comparison studies with basic FOA, FOA with Levy flight distribution, and other recently published chaotic algorithms. Statistical results on every optimization task indicate that the chaotic fruit fly algorithm (CFOA) has a very fast convergence rate. In addition, CFOA is compared with recently developed chaos enhanced algorithms such as chaotic bat algorithm, chaotic accelerated particle swarm optimization, chaotic firefly algorithm, chaotic artificial bee colony algorithm, and chaotic cuckoo search. Overall research findings show that FOA with Chebyshev map show superiority in terms of reliability of global optimality and algorithm success rate. (C) 2015 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Chaos Metaheuristic technique optimization

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An ensemble fruit fly optimization algorithm for solving range image registration to improve quality inspection of free-form surface parts

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INFORMATION SCIENCES 2016年 367卷 953-974页

作者： Li, Taifeng Gao, Liang Li, Peigen Pan, Quanke Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

Free-form surface part inspection can be conducted by comparing an ideal design model with a real measurement model. Because these two models are in different coordinate systems, the measurement model, represented by a set of 3D points, should be consistently registered to the design model. The final aim of the registration is to determine the optimal transformation matrix. In this research area, the iterative closest point (ICP) method is the best-known algorithm for the registration of two point sets. However, the ICP method needs a good initial parameter to obtain the global optimum transformation matrix, which is difficult to guarantee in the actual inspection process. To improve the precision and robustness of complex parts quality inspection, an ensemble parameters fruit fly optimization (EFFO) algorithm is proposed in this study. This paper provides a parameter pool using the smell-based search process of a fruit fly swarm and sorts the individuals based on the fitness to identify the leaders in a vision-based search process. Additionally, an ICP-based initialization strategy is introduced into the EFFO. We compared the EFFO algorithm with other registration methods and some variants of FFO. The proposed algorithm is superior to other algorithms in terms of accuracy and robustness, and the experimental results show that the proposed algorithm is effective. (C) 2016 Elsevier Inc. All rights reserved.

关键词： fruit fly optimization algorithm Free-form surface parts inspection Range image registration Iterative closest point

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A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm

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KNOWLEDGE-BASED SYSTEMS 2013年第Jan.期37卷 378-387页

作者： Li, Hong-ze Guo, Sen Li, Chun-jie Sun, Jing-qi N China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China

Accurate annual power load forecasting can provide reliable guidance for power grid operation and power construction planning, which is also important for the sustainable development of electric power industry. The annual power load forecasting is a non-linear problem because the load curve shows a non-linear characteristic. Generalized regression neural network (GRNN) has been proven to be effective in dealing with the non-linear problems, but it is very regretfully finds that the GRNN have rarely been applied to the annual power load forecasting. Therefore, the GRNN was used for annual power load forecasting in this paper. However, how to determine the appropriate spread parameter in using the GRNN for power load forecasting is a key point. In this paper, a hybrid annual power load forecasting model combining fruit fly optimization algorithm (FOA) and generalized regression neural network was proposed to solve this problem, where the FOA was used to automatically select the appropriate spread parameter value for the GRNN power load forecasting model. The effectiveness of this proposed hybrid model was proved by two experiment simulations, which both show that the proposed hybrid model outperforms the GRNN model with default parameter, GRNN model with particle swarm optimization (PSOGRNN), least squares support vector machine with simulated annealing algorithm (SALSSVM), and the ordinary least squares linear regression (OLS_LR) forecasting models in the annual power load forecasting. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Annual power load forecasting Generalized regression neural network fruit fly optimization algorithm optimization problem Parameter selection

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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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Construct the prediction model for China agricultural output value based on the optimization neural network of fruit fly optimization algorithm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 86卷 663-669页

作者： Han, Shi-Zhuan Pan, Wen-Tsao Zhou, Ying-Ying Liu, Zong-Li East China Jiaotong Univ Sch Econ & Management Nanchang Jiangxi Peoples R China Guangdong Univ Foreign Studies Sch Business Guangzhou Guangdong Peoples R China

Since agriculture is the foundation of a country and the industry that people depend on for life, it is particularly important for the development of national economy, and it has a higher output value than forestry, fishery and animal husbandry, so it occupies a very important position in the economic development of a country. The aim of this paper is to strengthen the capacity of prediction mode for total agricultural output value. This paper provides relevant government departments a reference and solves the problem of the lack of predictive ability of prediction mode for total agricultural output value in previous study. Different from previous literature, this paper adopts the new CFOA to optimize the parameters of GRNN, which contains innovative and reference value in some degree. Besides the way to validate this new model is to take the agricultural output value of the past years as a research sample and test it repeatedly. The study results have indicated that the total agricultural production value accounts for a higher proportion of agriculture, forestry, fishery and animal husbandry and the proportion tends to decline year by year;it can be found through 4 evaluation indexes that the prediction model that optimizes the smoothing parameters of GRNN through CFOA has a better predictive ability than the other two prediction models. (C) 2018 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Improved fruit fly optimization algorithm Chaos fruit fly optimization algorithm General Regression Neural Network Total agricultural production value

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An effective fruit fly optimization algorithm with hybrid information exchange and its applications

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2018年第10期9卷 1623-1648页

作者： Lv, Sheng-Xiang Zeng, Yu-Rong Wang, Lin Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China Hubei Univ Econ Sch Informat Engn Wuhan 430205 Hubei Peoples R China

As a newly proposed algorithm, fruit fly optimization algorithm (FOA) has been shown to have a strong capacity for solving numerical optimization problems. However, the basic FOA is faced with the challenges of poor diversity of the swarm and weak local search ability because of the improper osphresis operation and vision operation. To overcome these limitations synthetically, we propose an improved FOA based on hybrid location information exchange mechanism (HFOA) aiming at improving the swarm diversity in a more efficient way and well balance the global search and local search abilities. First, the proposed HFOA enables flies to communicate with each other and conduct local search in a swarm based approach. Second, osphresis operation is conducted in probability to balance the global search and local search processes. Finally, a mutation strategy called cataclysm policy is designed to help the flies jump out of the local extreme points. 18 complex continuous benchmark functions are used to test the performance of HFOA. Numerical experiments results indicate that HFOA outperforms main state-of-the-art algorithms. A classical non-deterministic polynomial hard problema widely-researched joint replenishment and delivery scheduling problem with resource restrictions is also used to further verify the ability of HFOA in solving practical operation management problems. Results show that HFOA can obtain lower operation cost than other widely used methods, demonstrating its ability to solve various complex optimization problems.

关键词： fruit fly optimization algorithm Information exchange Hybrid exchange Function optimization Joint replenishment and delivery scheduling

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Inverse estimation of the particle size distribution using the fruit fly optimization algorithm

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APPLIED THERMAL ENGINEERING 2015年 88卷 306-314页

作者： He, Zhenzong Qi, Hong Yao, Yuchen Ruan, Liming Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Peoples R China

The fruit fly optimization algorithm (FOA) is applied to retrieve the particle size distribution (PSD) for the first time. The direct problems are solved by the modified Anomalous Diffraction Approximation (ADA) and the Lambert Beer Law. Firstly, three commonly used monomodal PSDs, i.e. the Rosin -Rammer (R-R) distribution, the normal (N-N) distribution and the logarithmic normal (L-N) distribution, and the bimodal Rosin-Rammer distribution function are estimated in the dependent model. All the results show that the FOA can be used as an effective technique to estimate the PSDs under the dependent model. Then, an optimal wavelength selection technique is proposed to improve the retrieval results of bimodal PSD. Finally, combined with two general functions, i.e. the Johnson's S-B (J-S-B)) function and the modified beta (M-beta) function, the FOA is employed to recover actual measurement aerosol PSDs over Beijing and Hangzhou obtained from the aerosol robotic network (AERONET). All the numerical simulations and experiment results demonstrate that the FOA can be used to retrieve actual measurement PSDs, and more reliable and accurate results can be obtained, if the J-S-B function is employed. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： fruit fly optimization algorithm Aerosol Particle size distribution Inverse problem

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A knowledge-guided multi-objective fruit fly optimization algorithm for the multi-skill resource constrained project scheduling problem

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SWARM AND EVOLUTIONARY COMPUTATION 2018年 38卷 54-63页

作者： Wang, Ling Zheng, Xiao-long Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

In this paper, a knowledge-guided multi-objective fruit fly optimization algorithm (MOFOA) is proposed for the multi-skill resource-constrained project scheduling problem (MSRCPSP) with the criteria of minimizing the makespan and the total cost simultaneously. First, a solution is represented by two lists, i.e. resource list and task list. Second, the minimum total cost rule is designed for the initialization according to the property of the problem. Third, the smell-based search is implemented via the neighborhood based search operators that are specially designed for the MSRCPSP, while the vision-based search adopts the technique for the order preference by similarity to an ideal solution (TOPSIS) and the non-dominated sorting collaboratively to complete the multi-objective evaluation. In addition, a knowledge-guided search procedure is introduced to enhance the exploration of the FOA. Finally, the design-of-experiment (DOE) method is used to investigate the effect of parameter setting, and numerical tests based on benchmark instances are carried out. The results compared to other algorithms demonstrate the effectiveness of the MOFOA with knowledge-guided search in solving the multi-objective MSRCPSP.

关键词： fruit fly optimization algorithm Multi-skill resource constrained project scheduling problem Knowledge Multi-objective optimization

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RWFOA: a random walk-based fruit fly optimization algorithm

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SOFT COMPUTING 2020年第16期24卷 12681-12690页

作者： Chen, Chong Shandong Polytech Jinan Peoples R China

As one kind of the classic swarm intelligence (SI) algorithms, fruit fly optimization algorithm has been widely used in many aspects, such as multiple objective optimization and service computing. However, due to the limitation that introduced by the initial population location, its global optimization ability is limited. Therefore, we propose a random walk-based fruit fly optimization algorithm, namely RWFOA, to enhance its global optimization ability. RWFOA employs the random walk mechanism to dynamically adjust the position of the fruit fly population, which can reduce the impact of the initial population location and thus enhance the global optimization ability. We conduct a comprehensive experimental evaluation of RWFOA by comparing it with three representative algorithms, i.e., the OFOA, CFOA and IFFO, over 29 widely used benchmark functions published in CEC 2015. Experimental results demonstrate that RWFOA can find better solutions in most of the selected benchmark functions.

关键词： fruit fly optimization algorithm Random work Probability graph Roulette scheme Swarm intelligence algorithm

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