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A novel multi-scale cooperative mutation fruit fly optimization algorithm

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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Optimal Gating System Design of Steel Casting by fruit fly optimization algorithm Based on Casting Simulation Technology

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INTERNATIONAL JOURNAL OF METALCASTING 2019年第3期13卷 561-570页

作者： Wang, Tong Shen, Xu Zhou, Jianxin Yin, Yajun Ji, Xiaoyuan Zhou, Qin Huazhong Univ Sci & Technol State Key Lab Mat Proc & Die & Mould Technol Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Engn Training Ctr Wuhan Hubei Peoples R China

Casting simulation technology is an effective method to provide the predicted information on defects such as shrinkage, gas entrapment, cold shut, misrun and inclusions. To extend the analysis of casting simulation technology, a new optimal method for gating system design by fruit fly optimization algorithm (FOA) is proposed in this paper. First, according to the filling principles of steel casting, the gating system geometry mathematical model, which includes objective function and constraint conditions, is established. Second, in order to obtain optimal solution, fruit fly optimization algorithm is introduced to solve the above model. Finally, numerical simulation software is used to verify the validity of the proposed optimal method. Taking an upper center plate casting as an example, the results indicate that the proposed optimal method could provide practical and useful suggestions for the designer to obtain higher-quality and lower-resource-cost designs in the gating system design process.

关键词： gating system design steel casting fruit fly optimization algorithm casting simulation technology

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New fruit fly optimization algorithm with joint search strategies for function optimization problems

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KNOWLEDGE-BASED SYSTEMS 2019年 176卷 77-96页

作者： Wang, Lin Xiong, Yingnan Li, Shuwen Zeng, Yu-Rong Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Hubei Peoples R China China Univ Geosci Wuhan Sch Econ & Management Wuhan 430074 Hubei Peoples R China Hubei Univ Econ Wuhan 430205 Hubei Peoples R China

An enhanced fruit fly optimization algorithm (FOA) with joint search strategies named JS-FOA is proposed to optimize continuous function problems. First, a collaborative group search, which includes a new parameter, is conducted to obtain the critical value. Second, a new search strategy similar to biological memory, namely, memory move direction, is proposed to improve solution accuracy. Third, a gradient descent search is used in the collaborative group search to ensure that it does not fall into a local optimum. Finally, a new function, which is similar to the excitation function in a neural network, is proposed to combine the three search strategies. To test the robustness and convergence of the proposed JS-FOA, we used 29 complex continuous benchmark functions. Results show that the proposed JS-FOA outperforms other heuristic algorithms for most functions. The performance of JS-FOA is also evaluated for different parameter values and the results show that parameter values affect convergence speed within a certain range, but do not change the convergence accuracy for the continuous benchmark functions. The proposed JS-FOA may potentially solve high-dimensional optimization problems. (C) 2019 Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm Group collaboration Memory move direction Gradient descent search

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An improved evolution fruit fly optimization algorithm and its application

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NEURAL COMPUTING & APPLICATIONS 2020年第14期32卷 9897-9914页

作者： Yang, Xuan Li, Weide Su, Lili Wang, Yaling Yang, Ailing Lanzhou Univ Sch Math & Stat 222 TianShui Rd South Lanzhou 730000 Gansu Peoples R China

fruit fly optimization algorithm (FOA) is a kind of swarm intelligence optimization algorithm, which has been widely applied in science and engineering fields. The aim of this study is to design an improved FOA, namely evolution FOA (EFOA), which can overcome some shortcomings of basic FOA, including difficulty in local optimization, slow convergence speed, and lack of robustness. EFOA applies a few new strategies which adaptively control the search steps and swarm numbers of the fruit flies. The evolution mechanism used in EFOA can preserve dominant swarms and remove inferior swarms. Comprehensive comparison experiments are performed to compare EFOA with other swarm intelligence algorithms through 14 benchmark functions and a constrained engineering problem. Experimental results suggest that EFOA performs well both in global search ability and in robustness, and it can improve convergence speed.

关键词： fruit fly optimization algorithm Evolution mechanism Swarm intelligence

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Object Pose Estimation in Accommodation Space using an Improved fruit fly optimization algorithm

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2019年第2期95卷 405-417页

作者： Guo, Qingda Quan, Yanming Jiang, Changcheng South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Guangdong Peoples R China

The accommodation space changes as flexible products are packed into it. In order to improve the automatic loading of containers, it is necessary to solve the problem of object pose estimation in accommodation space. The goal of this study is to establish a method for pose estimation of a target object in the accommodation space. Firstly, the paper introduces basic algorithms and concepts, including the quick hull (Qhull) algorithm, oriented bounding box (OBB) algorithm, and fruit fly optimization algorithm (FOA). Secondly, the constraint conditions and the objective function of pose estimation are set up according to the pose variables in three-dimensional (3D) space, and a solution method for pose estimation is established using an improved FOA. Then, the algorithms with different population parameters are simulated, and the optimal parameters are obtained. The bounding box algorithm is used for system optimization, whereas a convex hull is used to simplify the target object significantly, reducing the corresponding running time. Finally, the hardware platform of the industrial robot is established, the initial and final poses of the end-effector are obtained using the proposed method, and tests are performed for different cases. The results show that the application of convex hull algorithm can significantly simplify a target object reducing the running time, and half individuals of the population guide the entire population to search for an optimal pose (6 degrees of freedom) in accommodation space.

关键词： Pose estimation fruit fly optimization algorithm Quick hull Oriented Bounding Box Accommodation space

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Comment and improvement on "A new fruit fly optimization algorithm: Taking the financial distress model as an example"

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KNOWLEDGE-BASED SYSTEMS 2014年 59卷 159-160页

作者： Dai, Hongde Zhao, Guorong Lu, Jianhua Dai, Shaowu Naval Aeronaut & Astronaut Univ Dept Control Engn Yantai 264001 Peoples R China

A new fruit fly optimization algorithm (FOA) has been introduced in a recent paper published in Knowledge-Based Systems by Pan (2012) [1], which is much simpler and more robust compared with the normal optimization algorithm such as genetic algorithm, ant colony optimization and particle swarm optimization. However, it is found that a improvement is required, the smell concentration judgment value S is non-negative in Ref. [1], which will restrict the application of FOA in some problem, an improvement is proposed in this letter, comparison between the traditional FOA and the improved FOA have been done by simulation, results show the effectiveness of the improved algorithm. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.

关键词： fruit fly optimization algorithm optimization problem Genetic algorithm Ant colony optimization Particle swarm optimization

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optimization about the layout of IMUs in large ship based on fruit fly optimization algorithm

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OPTIK 2015年第4期126卷 490-493页

作者： Dai, Hongde Liu, Aili Lu, Jianhua Dai, Shaowu Wu, Xiaonan Sun, Yuyu Naval Aeronaut & Astronaut Univ Dept Control Engn Yantai 2640017 Japan

Accurate attitude information is needed in the deck of large ship, but it is influenced by the deformation of the deck, the deformation will degrade the performance of the ship-board weapons and equipments. Aim at this problem, IMUs which are contain laser gyros and accelerators, are installed in the key battle paint of the deck, applying for the estimation of the deformation, but how many IMUs are needed, and where they should be arranged, is a difficult problem for mankind to solve by hand. In this paper, a new proposed optimization algorithm named fruit fly optimization algorithm (FOA) is utilized to determine the layout of the IMUs, fitness function for the FOA algorithm is organized by the ship's Modal Assurance Criterion (MAC) matrix. Experimental results show that the FOA algorithm can give an optimal layout of the IMUs for detecting the deformation of the deck. (C) 2014 Elsevier GmbH. All rights reserved.

关键词： optimization problem fruit fly optimization algorithm IMU Deformation detection

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Improved fruit fly optimization algorithm Incorporating Tabu Search for Optimizing the Selection of Elements in Trusses

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KSCE JOURNAL OF CIVIL ENGINEERING 2018年第12期22卷 4940-4954页

作者： Li, Yancang Lian, Sida Hebei Univ Engn Coll Water Conservancy & Hydroelect Power Handan 056038 Hebei Peoples R China Hebei Univ Engn Coll Civil Engn Handan 056038 Hebei Peoples R China

In order to find a more effective method for the structural optimization, an improved fruit fly optimization algorithm was proposed. The dynamic adjustment search, the inertia weight function and the tabu search theory were employed to overcome the premature flaw of the basic algorithm. Then, the improved algorithm was introduced to the structural optimization of the tube- type trestle. After the setup of the optimization model, the improved algorithm was used. optimization results and comparison with other algorithms show that the stability of improved fruit fly optimization algorithm is apparently improved and the efficiency is obviously remarkable. This study provides a more effective solution to structural optimization problems.

关键词： structural optimization tube-type trestle fruit fly optimization algorithm self-adaptive tabu search

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Effective long short-term memory with fruit fly optimization algorithm for time series forecasting

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SOFT COMPUTING 2020年第19期24卷 15059-15079页

作者： Peng, Lu Zhu, Qing Lv, Sheng-Xiang Wang, Lin Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Shaanxi Normal Univ Int Business Sch Xian 710000 Peoples R China

A number of recent studies have adopted long short-term memory (LSTM) in extensive applications, such as handwriting recognition and time series prediction, with considerable success. However, the parameters of LSTM have greatly influenced its accuracy and performance. In this study, LSTM with fruit fly optimization algorithm (FOA), called FOA-LSTM, is designed to solve time series problems. As a novel intelligent algorithm, FOA is applied to decide on the optimal hyper-parameter of LSTM. Experiments under the NN3 time series, three comparative experiments and the monthly energy consumption of the USA are conducted to verify the effectiveness of the FOA-LSTM model. The results indicate that the symmetric mean absolute percentage error (SMAPE) is reduced by up to 11.44% in the last 11 monthly series in the NN3 dataset. Four comparative experiments and the real-life series verify further that the FOA-LSTM model obtains a better result compared with other forecasting models.

关键词： Long short-term memory fruit fly optimization algorithm Time series forecasting

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A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem

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KNOWLEDGE-BASED SYSTEMS 2013年 48卷 17-23页

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

In this paper, a novel binary fruit fly optimization algorithm (bFOA) is proposed to solve the multidimensional knapsack problem (MKP). In the bFOA, binary string is used to represent the solution of the MKP, and three main search processes are designed to perform evolutionary search, including smell-based search process, local vision-based search process and global vision-based search process. In particular, a group generating probability vector is designed for producing new solutions. To enhance the exploration ability, a global vision mechanism based on differential information among fruit flies is proposed to update the probability vector. Meanwhile, two repair operators are employed to guarantee the feasibility of solutions. The influence of the parameter setting is investigated based on the Taguchi method of design of experiment. Extensive numerical testing results based on benchmark instances are provided. And the comparisons to the existing algorithms demonstrate the effectiveness of the proposed bFOA in solving the MKP, especially for the large-scale problems. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Multidimensional knapsack problem fruit fly optimization algorithm Binary algorithm Smell-based search Vision-based search

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