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检索条件"主题词=Fruit fly optimization algorithm"

共 248 条记录，以下是91-100 订阅

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A Modified fruit fly optimization algorithm and Its Application to Control of Robot Manipulators 5

A Modified Fruit Fly Optimization Algorithm and Its Applicat...

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5th International Conference on Control, Instrumentation, and Automation (ICCIA)

作者： Mehdifar, Farhad Gholami, Hesam Seyed Kharrati, Hamed Menhaj, Mohammad Bagher Univ Tabriz Fac Elect & Comp Engn Tabriz Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781538621349

This paper presents a new Modified fruit fly optimization algorithm (MFOA) which is used to find the optimal PID controllers parameters applied to control a two-link robotic manipulator. The proposed new distribution law in MFOA for some of the fruit flies improves searching diversity in earlier iterations and increases solution precession in last iterations. In order to apply the PID controllers to the robot manipulator, a nonlinear feedback linearization control technique is employed which can fully linearize and decouple nonlinear robot's dynamics. Simulation results confirm that the MFOA-PID controller can achieve better closed-loop system responses with respect to the original FOA-PID controller.

关键词： PID Controller tuning fruit fly optimization algorithm Evolutionary algorithms Feedback Linearization Robot Manipulator

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An Improved fruit fly optimization algorithm Incorporating Average Learning and Step Changing

An Improved Fruit Fly Optimization Algorithm Incorporating A...

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International Conference on Computer Systems, Electronics and Control (ICCSEC)

作者： Yuan, Panpan Zhou, Yan Wang, Dongli Xiangtan Univ Coll Informat Engn Xiangtan Peoples R China

ISBN: (纸本)9781538635735

To overcome the disadvantages of slow convergence speed, easily relapsing into local extremum and poor stability of traditional fruit fly optimization algorithm (FOA), an improved FOA incorporating Average Learning and Step Changing into the evolutionary process (AL-SC-FOA) is proposed. The combination of strategies of average learning and step changing can balance the ability of global search and local search of the algorithm, accelerate the convergence speed, improve the accuracy and enhance the stability of the algorithm. The proposed AL-SC-FOA is applied to six standard examples of the optimization test. The experimental results show that the AL-SC-FOA can avoid falling into the local optimum, which has higher precision and faster convergence speed, as well as better stability.

关键词： fruit fly optimization algorithm local extremum precision convergence speed stability

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PID control parameters optimize based on an immune fruit fly optimization algorithm 28

PID control parameters optimize based on an immune fruit fly...

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28th Chinese Control and Decision Conference

作者： Song, Juan Pan, Huan Ningxia Univ Sch Phys & Elect Informat Sci Ningxia Key Lab Intelligent Sensing Desert Inform Yinchuan 750021 Peoples R China

ISBN: (纸本)9781467397148

The traditional Ziegler-Nichols (Z-N) method usually fails to achieve the best control performance for tuning PID parameters. Thus, this paper proposed an immune fly fruit optimization algorithm (IFOA) with the error performance criterion of ITAE as fitness function for the PID parameters optimized. Firstly, the proposed algorithm selected the best fruit flies for immune vaccines in the osphresis search mode. Then, it introduced the immune vaccination and immune selection mechanism in the visual search mode, so as to avoid fly fruit optimization algorithm (FOA) falling into premature, and to overcome the artificial immune algorithm (AIA) shortcomings in the cumbersome and inefficient calculations. Finally, test the performance of the hybrid algorithm with four benchmarks, and apply it in PID parameters tuning. Simulation results show that the IFOA has fast convergence, good stability and higher precision, and also prove the feasibility and effectiveness in PID control parameter optimization.

关键词： fruit fly optimization algorithm Artificial immune algorithm PID controller Parameters optimization

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A Binary fruit fly optimization algorithm to Solve the Set Covering Problem 15th

A Binary Fruit Fly Optimization Algorithm to Solve the Set C...

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15th International Conference on Computational Science and Its Applications (ICCSA)

作者： Crawford, Broderick Soto, Ricardo Torres-Rojas, Claudio Pena, Cristian Riquelme-Leiva, Marco Misra, Sanjay Johnson, Franklin Paredes, Fernando Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ San Sebastian Santiago Chile Univ Cent Chile Santiago Chile Univ Autonoma Chile Santiago Chile Univ Cient Sur Lima Peru Covenant Univ Ota Nigeria Univ Playa Ancha Valparaiso Chile Univ Diego Portales Escuela Ingn Ind Santiago Chile

ISBN: (纸本)9783319214108;9783319214092

The Set Covering Problem (SCP) is a well known NP-hard problem with many practical applications. In this work binary fruit fly optimization algorithms (bFFOA) were used to solve this problem using different binarization methods. The bFFOA is based on the food finding behavior of the fruit flies using osphresis and vision. The experimental results show the effectiveness of our algorithms producing competitive results when solve the benchmarks of SCP from the OR-Library.

关键词： Set Covering Problem fruit fly optimization algorithm Metaheuristics Combinatorial optimization 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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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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Solving 2D strip packing problem using fruit fly optimization algorithm 8th

Solving 2D strip packing problem using fruit fly optimizatio...

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8th International Conference on Advances in Information Technology (IAIT)

作者： Babaoglu, Ismail Selcuk Univ Dept Comp Engn TR-42250 Konya 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. (c) 2017 The Authors. Published by Elsevier B.V.

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

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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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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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A Self-Adaptive Modified fruit fly optimization algorithm 36

A Self-Adaptive Modified Fruit Fly Optimization Algorithm

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第36届中国控制会议

作者： Yingtong Tan Mei Zhang Jinhui Zhu Haiming Liu School of Automation Science and Control Engineering South China University of Technology Engineering Research Center for Precision Electronic Manufacturing Equipment of Ministry of Education South China University of Technology School of Software Engineering South China University of Technology

ISBN: (纸本)9781538629185

fruit fly optimization algorithm(FOA) is inspired by imitating the foraging activity of fruit flies. Aiming at its inability to search the entire solution space, a Self-Adaptive Modified fruit fly optimization algorithm(SAMFOA) is proposed. Firstly, a new calculation formula of the smell concentration judgment value is designed. With the use of the new formula, the smell concentration judgment value is no longer restricted to be non-negative value so the algorithm is able to search both the positive and negative part of the solution space. Secondly, a self-adaptive osphresis foraging radius is introduced to enhance the ability to break away from local optimum. Experiments on 20 numerical benchmark functions show that the algorithm has good performance in terms of global searching ability, optimize accuracy and stability.

关键词： Swarm intelligence optimization fruit fly optimization algorithm Numerical optimization

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