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An Adaptive fruit fly optimization algorithm for optimization Problems

Journal of Applied Mathematics and Physics 2023年第11期11卷 3641-3650页

作者： L. Q. Zhang J. Xiong J. K. Liu College of Computers and Engineering Chongqing Three Gorges University Chongqing China College of Mathematics and Statistics Chongqing Three Gorges University Chongqing China

In this paper, we present a new fruit fly optimization algorithm with the adaptive step for solving unconstrained optimization problems, which is able to avoid the slow convergence and the tendency to fall into local optimum of the standard fruit fly optimization algorithm. By using the information of the iteration number and the maximum iteration number, the proposed algorithm uses the floor function to ensure that the fruit fly swarms adopt the large step search during the olfactory search stage which improves the search speed;in the visual search stage, the small step is used to effectively avoid local optimum. Finally, using commonly used benchmark testing functions, the proposed algorithm is compared with the standard fruit fly optimization algorithm with some fixed steps. The simulation experiment results show that the proposed algorithm can quickly approach the optimal solution in the olfactory search stage and accurately search in the visual search stage, demonstrating more effective performance.

关键词： Swarm Intelligent optimization algorithm fruit fly optimization algorithm Adaptive Step Local Optimum Convergence Speed

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Improved fruit fly optimization algorithm Based on Simulated Annealing in Neural Network

Improved Fruit Fly Optimization Algorithm Based on Simulated...

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Asia-Pacific-Signal-and-Information-Processing-Association Annual Summit and Conference (APSIPA ASC)

作者： Wu, Jin Dai, Wei Wang, Yu Zhao, Bo Xian Univ Posts & Telecommun Sch Elect Engn Xian Peoples R China

ISBN: (纸本)9789881476890

Aiming at the problem that the fruit fly optimization algorithm (FOA) has weak local optimization ability and difficulty in convergence, this paper combines the improved FOA (IFOA) and simulated annealing algorithm (SA) to propose a new optimization algorithm called IFOA-SA algorithm. The IFOA-SA algorithm first modifies the optimization step size of FOA, increases the escape coefficient, and improves the generation mechanism of candidate solutions. At the same time, the concept of three-dimensional search is introduced to expand the search range of fruit flies, and then the FOA solution acceptance mechanism is modified through SA. Finally, the fusion algorithm IFOA-SA is used to optimize the Generalized Regression Neural Network (GRNN) and Back Propagation (BP) Neural Network for freight volume forecasting and nonlinear function fitting. The experimental results show that the total error of the BP neural network optimized by the IFOA-SA algorithm is 37.4903, which achieves better optimization results than other algorithms. In the end, the effectiveness of the algorithm is proved through experiments.

关键词： fruit fly optimization algorithm Freight volume forecast Function fitting Neural network Simulated annealing algorithm

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A novel fruit fly optimization algorithm with quasi-affine transformation evolutionary for numerical optimization and application

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2022年第2期18卷

作者： Wang, Ru-Yu Hu, Pei Hu, Chia-Cheng Pan, Jeng-Shyang Shandong Univ Sci & Technol Coll Comp Sci & Engn 579 Qianwangang RdXinan St Qingdao 266590 Shandong Peoples R China Nanyang Inst Technol Sch Software Nanyang Peoples R China Yango Univ Coll Artificial Intelligence Fuzhou Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan

The fruit fly optimization algorithm is a swarm intelligence algorithm with strong versatility and high computational efficiency. However, when faced with complex multi-peak problems, fruit fly optimization algorithm tends to converge prematurely. In response to this situation, this article proposes a new optimized structure-Quasi-affine Transformation evolutionary for the fruit fly optimization algorithm. The new algorithm uses the evolution matrix in QUasi-Affine TRansformation Evolution algorithm to update the position coordinates of particles. This strategy makes the movement of particles more scientific and the search space broader. In order to prove its effectiveness, we compare Quasi-affine Transformation evolutionary for the fruit fly optimization algorithm with five other mature intelligent algorithms, and test them on 22 different types of benchmark functions. In order to observe the multi-faceted performance of Quasi-affine Transformation evolutionary for the fruit fly optimization algorithm more intuitively, we also conduct experiments on algorithm convergence analysis, the Friedman test, the Wilcoxon signed-rank test, and running time comparison. Through the above several comparative experiments, Quasi-affine Transformation evolutionary for the fruit fly optimization algorithm has indeed demonstrated its strong competitiveness. Finally, we apply it to Capacitated Vehicle Routing Problem. Through comparing with the contrast algorithms, it is confirmed that Quasi-affine Transformation evolutionary for the fruit fly optimization algorithm can achieve better vehicle routes planning.

关键词： Intelligence algorithm fruit fly optimization algorithm evolution matrix Quasi-affine Transformation Evolution algorithm Capacitated Vehicle Routing Problem

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A novel fruit fly optimization algorithm with Levi flight and challenge probability 10

A novel fruit fly optimization algorithm with Levi flight an...

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10th Annual International Conference of Information and Communication Technology (ICICT)

作者： Wang, Tianlei Xu, Jinzhao Luo, Wenhui Yu, Yanjiang Huang, Zundi Wuyi Univ Fac Intelligent Mfg Jiangmeng 529000 Guangdong Peoples R China Wuyi Univ Sch Math & Computat Sci Jiangmen 529000 Guangdong Peoples R China Wuyi Univ Jiangmen Sch Rail Transportat Jiangmen 529000 Guangdong Peoples R China

Aiming at the problem of low convergence precision and premature convergence of fruit fly optimization algorithm (FOA), this paper proposes a novel fruit fly optimization algorithm with Levi flight and challenge probability (LCFOA). We introduce the Levi flight mechanism and challenge adjustment process to coordinate the global exploration and local exploitation capabilities of the algorithm. Then, we designed an adaptive challenge factor in the challenge adjustment process to improve the convergence precision of the algorithm. The convergence performance of the algorithm is verified by 6 different benchmark functions. The numerical experimental results comfirmed that the proposed LCFOA has better search precision and convergence speed. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： fruit fly optimization algorithm metaheuristic optimization swarm intelligence Levy flight challengeprobability

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A Novel fruit fly optimization algorithm with Vision Scanning Search and Extensive Learning Mechanism 24

A Novel Fruit Fly Optimization Algorithm with Vision Scannin...

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24th IEEE International Conference on Computer Supported Cooperative Work in Design (IEEE CSCWD)

作者： Zhao, Fuqing Ding, Ruiqing Tang, Jianxin Liu, Huan Lanzhou Univ Technol Sch Comp & Commun Technol Lanzhou 730050 Peoples R China

ISBN: (纸本)9781728165974

The fruit fly optimization algorithm has drawn various attention to researchers and engineers, due to the simple theory and flexible frame. For solving the complex continuous optimization problems, an improved fruit fly algorithm based on vision scanning search and extensive learning mechanism is proposed in this paper. The vision scanning search strategy is used to scan the potential area by changing the search angle of swarm center. This strategy is utilized to guide the population to jump out the local trap. The extensive learning is using the knowledge of neighboring structure to increase the diversity of population for solving the non-separable issues. Furthermore, a new mutation strategy based on difference vector is proposed to improve the search efficiency of VLFOA. Testified in CEC 2017 benchmark problems, the results show that the VLFOA has a superior performance compared with the original FOA and the state of art variants of the FOA.

关键词： fruit fly optimization algorithm vision scanning-search extensive learning

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Hybrid collaborative multi-objective fruit fly optimization algorithm for scheduling workflow in cloud environment

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SWARM AND EVOLUTIONARY COMPUTATION 2022年第0期68卷 101008-101008页

作者： Qin, Shuo Pi, Dechang Shao, Zhongshi Xu, Yue Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China

Scheduling the complex workflows in cloud environment have drawn enormous attentions because the distinct features of the cloud resources. Most of the previous approaches ignored the multiple conflicting objectives of workflow scheduling and resources provisioning. In this paper, a novel hybrid collaborative multi-objective fruit fly optimization algorithm (HCMFOA) is developed to optimize both the execution time and cost. In the proposed HCMFOA, a reference points-based cluster strategy is introduced to dynamic divide the swarm into multiple subswarms. Moreover, a hybrid initial strategy is designed based on non-linear weight vector and two assignment rules of tasks to initialize the location of all the fruit flies in the problem space. In the collaborative smell-based foraging, three effective problem-specific neighborhood operators are employed to collaborative explore the global scope. In multi-objective vision-based foraging, the sub-swarms based crossover operator is designed to perform exploitation in local region. Finally, an extensive computational experiment is conducted to validate the performance of HCMFOA. The statistical results reveal that HCMFOA significantly outperforms the existing state-of-the-art approaches.

关键词： Cloud computing Workflow scheduling Multi-objective optimization fruit fly optimization algorithm Non-linear weight vector

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Adaptive co-simulation method and platform application of drive mechanism based on fruit fly optimization algorithm

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PROGRESS IN NUCLEAR ENERGY 2022年 153卷

作者： Sun, Hongbiao Li, Wenqiang Zheng, Lanjiang Ling, Sitong Fu, Wanchang Sichuan Univ Sch Mech Engn Chengdu 610065 Peoples R China Innovat Method & Creat Design Key Lab Sichuan Prov Chengdu 610065 Peoples R China Nucl Power Inst China Chengdu 610041 Peoples R China

The simulation of the control rod drive mechanism is a co-simulation process of multi-physics coupling. It has the problem of conflict between simulation accuracy and efficiency in the simulation process based on the centralized simulation idea. Therefore, a multidisciplinary co-simulation method and simulation platform with adaptive step size are proposed in this paper. According to the characteristics of the simulation nodes, the fruit fly optimization method is used to determine the adaptive step size of different simulation stages. A multi-disciplinary co-simulation platform is constructed based on the Isight which can integrate co-simulation tool. The co-simulation process of the control rod drive mechanism is completed with the method and platform. The results show that the simulation accuracy of the platform built in this paper is increased by 23%, and the simulation efficiency is increased by 64%, which verifies that the proposed method has good compatibility in terms of simulation accuracy and efficiency.

关键词： Multi-disciplinary Co-simulation fruit fly optimization algorithm Adaptive step size Isight platform Driving mechanism

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Solving interval many-objective optimization problems by combination of NSGA-III and a local fruit fly optimization algorithm

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APPLIED SOFT COMPUTING 2022年第0期114卷 108096-108096页

作者： Ge, Fawei Li, Kun Han, Ying Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Liaoning Peoples R China

Interval many-objective optimization problems (IMaOPS) are ubiquitous in practical applications. Therefore, it is of great significance to study the solving method for IMaOPS. However, there are fewer solving methods due to the uncertain interval of the objective function. In this paper, an improved NSGA-III algorithm (named LFOA-NSGA-III) is proposed to effectively solve these problems. Due to the uncertain interval in the IMaOPs, the original NSGA-III algorithm can ineffectively evaluate the relationship between the interval solution set and the reference point. So the matter-element extension model is introduced, which can make the optimized solution set close to the Pareto optimal solution. Furthermore, in order to improve the optimization performance and population diversity of the improved algorithm, the K-mean algorithm is used to solve the initial solution set, as well as a local fruit fly optimization algorithm (FOA) is combined with the genetic algorithm (GA). Finally, the LFOA-NSGA-III algorithm is empirically evaluated on eleven interval benchmark test problems and an unmanned aerial vehicles (UAVs) path planning problem. Through simulation comparisons with other different algorithms, it is concluded that the hyper-volume value, the imprecision value and the IGD value indicators are significantly better than other comparison algorithms. In addition, from a simulation experiment in application of the multi-UAVs path planning problem, it can be seen that the LFOA-NSGA-III algorithm is more effective and applicative in the IMaOPs. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Many-objective optimization fruit fly optimization algorithm NSGA-III algorithm Matter-element extension model Interval

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A short-term traffic forecasting model based on echo state network optimized by improved fruit fly optimization algorithm

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NEUROCOMPUTING 2020年 416卷 117-124页

作者： Zhang, Qingyong Qian, Hao Chen, Yuepeng Lei, Deming Wuhan Univ Technol Sch Automat Wuhan Peoples R China

Short-term traffic forecasting is an important part of contemporary intelligent transportation systems. In this paper, based on echo state network optimized by improved fruit fly optimization algorithm (ESN-IFOA), a model is proposed to provide a five-minute forecast of traffic volume. In IFOA, parallel searching strategy and uniform crossover operator are applied to enhance searching range and communication between swarms. Simultaneously, the source allocation strategy is proposed to calculate the number of fruit flies generated by each swarm in the next iteration. The five main parameters of ESN are optimized by the proposed IFOA. Massive of prediction results and algorithm comparisons demonstrates that ESN-IFOA has very good forecasting ability for the five-minutes forecast of traffic volume. (C) 2019 Published by Elsevier B.V.

关键词： Short-term traffic forecasting Echo state network fruit fly optimization algorithm Rource allocation Parallel searching Uniform crossover operator

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Quantum Behavior-Based Enhanced fruit fly optimization algorithm with Application to UAV Path Planning

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 1315-1331页

作者： Zhang, Xiangyin Xia, Shuang Li, Xiuzhi Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China Beijing Lab Urban Mass Transit Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

As a newly developed simple and effective optimization technology, the fruit fly optimization algorithm (FOA) has been successfully applied in many fields. To accelerate the algorithm convergence and avoid the local optimum, the enhanced FOA based on quantum theory called QFOA is proposed in this paper. When establishing the quantum Delta potential well around the location of fruit fly swarm, QFOA introduces the quantum behavior-based searching mechanism into the original osphresis-based search procedure of FOA. In the process that fruit flies find and move toward the food source, fruit flies follow the wave function property of the Delta potential well rather than the Newtonian mechanics. Taking advantage of the probability and uncertainty of quantum theory, the proposed QFOA can effectively overcome the weakness in premature convergence and easy trapping into local optimum. Since there are two popular models of the basic FOA, this paper also develops two corresponding QFOAs. Experimental results on various benchmark functions show that both the two QFOA models has overall better performance compared with the basic FOA as well as other FOA variants and other well-known optimization algorithms. In addition, the proposed QFOAs are also applied to unmanned aerial vehicle (UAV) path planning problem in the three-dimensional environment, and comparative results about the obtained optimal flight path and population convergence process show the effectiveness of QFOAs. (C) 2020 The Authors. Published by Atlantis Press B.V.

关键词： fruit fly optimization algorithm Continuous function optimization Delta potential well Quantum behavior Path planning

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