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A fruit fly optimization algorithm with a traction mechanism and its applications

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2017年第11期13卷 1-1页

作者： Guo, Xing Zhang, Jian Li, Wei Zhang, Yiwen Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China Anhui Univ Key Lab Intelligent Comp & Signal Proc Hefei Anhui Peoples R China

The original fruit fly optimization algorithm, as well as some of its improved versions, may fail to find the function extremum when it falls far from the origin point or in the negative range. To address this problem, in this article, we propose a new fruit fly optimization algorithm, named as the traction fruit fly optimization algorithm, which is mainly based on the combination of traction population and dynamic search radius. In traction fruit fly optimization algorithm, traction population consists of the worst individual recorded in the iterative process, the individual in the center of the interval, and the best fruit flies individual through different transformations, which is used to avoid the algorithm stopping at a local optimal. Moreover, our dynamic search radius strategy will ensure a wide search range in the early stage and enhance the local search capability in the latter part of the algorithm. Extensive experiment results show that traction fruit fly optimization algorithm is superior to fruit fly optimization algorithm and its other improved versions in the optimization of extreme values of continuous functions. In addition, through solving the service composition optimization problem, we prove that traction fruit fly optimization algorithm can also obtain a better performance in the discrete environment.

关键词： fruit fly optimization algorithm traction mechanism service composition function extremum swarm intelligence

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A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm

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Neurocomputing 2017年 221卷 24-31页

作者： Rui Hu Shiping Wen Zhigang Zeng Tingwen Huang School of Automation Huazhong University of Science and Technology Wuhan PR China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan PR China Texas A & M University at Qatar Doha 5825 Qatar

Short term power load forecasting plays an important role in the security of power system. In the past few years, application of artificial neural network (ANN) for short-term load forecasting (STLF) has become a research hotspots. Generalized regression neural network (GRNN) has been proved to be suitable for solving the non-linear problems. And according to the historical load curve, it can be known that STLF is a non-linear problem. Thus, the GRNN was used for STLF in this paper. However, the value of spread parameter σ determines the performance of the GRNN. The fruit fly optimization algorithm with decreasing step size (SFOA) is introduced to select an appropriate spread parameter σ . Combined with the weather factors and the periodicity of short-term load, an effective STLF model based on the GRNN with decreasing step FOA was proposed. Performance of the proposed SFOA-GRNN model is compared with other ANN on the basis of prediction error.

关键词： Short term power load forecasting Generalized regression neural network fruit fly optimization algorithm Decreasing step size

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Solving 2D strip packing problem using fruit fly optimization algorithm

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Procedia Computer Science 2017年 111卷 52-57页

作者： İsmail Babaoğlu Selçuk University Department of Computer Engineering Konya 42250 Turkey

Two dimensional strip-packing problem (2DSPP) consists of packing a set of rectangular items on one strip with a restriction of a maximal width and height. Because the conventional algorithms are still sub-optimal, the researchers tend towards searching for more successful alternative algorithms to solve 2DSPP. The fruit fly optimization algorithm (FOA), which is one of the recently proposed meta-heuristic algorithms, has been successfully applied on many engineering and mathematical problems. This study presents an implementation of FOA for solving non-oriented 2DSPP. The aim of the study is to find the optimal sequence of the rectangles in a strip, and then to place the rectangles by bottom left fill approach to have the optimal height within a fixed width box. The experiments are concluded on online available set of 2DSPP test problems. The preliminary results of the study are compared with the results of some conventional or heuristic approaches which use the same problem set. The experimental results show the promising results are obtained by FOA on solving 2DSPPs.

关键词： fruit fly optimization algorithm two dimensional strip packing problem swarm intelligence heuristic

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An application of fruit fly optimization algorithm for traveling salesman problem

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Procedia Computer Science 2017年 111卷 58-63页

作者： Hazim Iscan Mesut Gunduz Computer Engineering Department Selcuk University Konya Turkey

In this study, an application of fruit fly optimization algorithm (FOA) is presented. FOA is one of the recently proposed swarm intelligence optimization algorithms used to solve continuous complex optimization problems. FOA has been invented by Pan in 2011 and it is based on the food search behavior of fruit flies. The FOA has a simple framework and it is easy to implement for solving optimization problem with different characteristics. The FOA is also a robust and fast algorithm and some researchers used FOA to solve discrete optimization problems. In this study, a new modified FOA is proposed for solving the well-known traveling salesman problem (TSP) which is one of the most studied discrete optimization problems. In basic FOA, there are two basic phases, one of them is osphresis phase and the other is vision phase. In the modified version of FOA the ospherisis phases kept as it is and for vision phase two different methods developed. In vision phase, the first half of the city arrangement matrix is updated according to first %30 part of best solutions of the ospheresis phase. The other half of the city arrangement matrix is randomly reproduced because of the possibility that initial solutions are far from the optimum. According to the results, travelling salesman problem can be solved with FOA as an alternative method. For big scale problems, it needs some improvements.

关键词： fruit fly optimization algorithm metaheuristic FOA FFOA traveling salesman problem discrete optimization problem

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Identification of dynamic protein complexes based on fruit fly optimization algorithm

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KNOWLEDGE-BASED SYSTEMS 2016年第Aug.期105卷 270-277页

作者： Lei, Xiujuan Ding, Yulian Fujita, Hamido Zhang, Aidong Shaanxi Normal Univ Sch Comp Sci Xian 710062 Peoples R China Iwate Prefectural Univ Fac Software & Informat Sci 152-52 Sugo Takizawa Iwate 0200693 Japan SUNY Buffalo Dept Comp Sci & Engn Buffalo NY USA

Protein complexes play a significant role in understanding cellular life in postgenomic era. Yet up to now, the existing protein complex detection algorithms are mostly applied to static PPI networks and their performance is not very ideal for the deficiency of low efficiency and sensitive to noisy data. In this paper, a novel algorithm named fruit fly optimization Clustering algorithm (FOCA), is proposed to identify dynamic protein complexes by combining fruit fly optimization algorithm (FOA) and gene expression profiles. Particularly, we first find the always active proteins by the stable interactions of the dynamic PPI network and detect protein complex cores from those always active proteins. Then, FOA is used to merge of the rest proteins in every dynamic sub-network to their corresponding protein complex cores. The experimental results on DIP dataset demonstrate that FOCA is very effective in detecting protein complexes than the state-of-the-art complex detection techniques. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Dynamic protein-protein interaction network (DPIN) fruit fly optimization algorithm Gene expression Protein complex

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A novel 3D fruit fly optimization algorithm and its applications in economics

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NEURAL COMPUTING & APPLICATIONS 2016年第5期27卷 1391-1413页

作者： Lin, Wei-Yuan Soochow Univ Dept Econ Taipei Taiwan

fruit fly optimization algorithm (FOA) is a method that we have previously developed from the food-finding behavior of fruit flies to solve optimization problems. The advantage of FOA is simple and easy to understand compared to traditional stochastic algorithms. In this paper, we propose a modified algorithm called novel 3D-FOA. The performances of the 3D-FOA are far better than those of the original FOA. We select more than thirty different nonlinear functions as test vehicles to show that the search efficiency and/or quality of the 3D-FOA is superior to that of the genetic algorithm and particle swarm optimization algorithm. We also apply the 3D-FOA on some economics topics, two theoretic examples and a case study. Our results strongly suggest that the 3D-FOA can enhance capabilities in a variety of fields and future experiments.

关键词： optimization problems fruit fly optimization algorithm Economic models General regression neural network Financial distress prediction

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A novel multi-scale cooperative mutation fruit fly optimization algorithm

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KNOWLEDGE-BASED SYSTEMS 2016年 114卷 24-35页

作者： Zhang, Yiwen Cui, Guangming Wu, Jintao Pan, Wen-Tsao He, Qiang Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230031 Anhui Peoples R China Oriental Inst Technol Dept Informat Management New Taipei Taiwan Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia

The fruit fly optimization algorithm (FOA) is a widely used intelligent evolutionary algorithm with a simple structure that requires only simple parameters. However, its limited search space and the swarm diversity weaken its global search ability. To tackle this limitation, this paper proposes a novel Multi Scale cooperative mutation fruit fly optimization algorithm (MSFOA). First, we analyze the convergence of FOA theoretically and demonstrate that its convergence depends on the initial location of the swarm. Second, a Multi-Scale Cooperative Mutation (MSCM) mechanism is introduced that tackles the limitation of local optimum. Finally, the effectiveness of MSFOA is evaluated based on 29 benchmark functions. The experimental results show that MSFOA significantly outperforms the improved versions of FOA presented in recent literature, including IFFO, CFOA, and CMFOA, on most benchmark functions. (C) 2016 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Multi-scale cooperative mutation Global optimization Convergence

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

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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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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 two-stage adaptive fruit fly optimization algorithm for unrelated parallel machine scheduling problem with additional resource constraints

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 65卷 28-39页

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

In this paper, an unrelated parallel machine scheduling problem with additional resource constraints (UPMSP_RC) from the real world manufacturing systems is studied. With the objective of minimizing the makespan, a mixed integer linear programming model is presented and several properties are analyzed. Furthermore, a two-stage adaptive fruit fly optimization algorithm (TAFOA) is proposed to solve the UPMSP_RC. At the first stage, a heuristic is proposed to generate an initial solution with high quality. At the second stage, the initial solution is adopted as the initial swarm center for further evolution. During the evolution, the search manners are selected adaptively with the guidance of the problem-specific knowledge, which is a sufficient condition of the best schedule under a given job-to-machine assignment. Moreover, the effect of parameters on the performance of the TAFOA is investigated by using the two factor analysis of variance (ANOVA). Finally, extensive numerical comparisons are carried out to show the effectiveness of the TAFOA in solving the UPMSP_RC. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： fruit fly optimization algorithm Unrelated parallel machine scheduling Resource constraint Knowledge

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