检索结果-内蒙古大学图书馆

Chaotic flower pollination algorithm for scheduling tardiness-constrained flow shop with simultaneously loaded stations

引用

NEURAL COMPUTING & APPLICATIONS 2022年第2期37卷 579-596页

作者： Davendra, Donald Herrmann, Frank Bialic-Davendra, Magdalena Cent Washington Univ Dept Comp Sci 400 E Univ Way Ellensburg WA 98926 USA Ostbayer TH Regensburg Fac Comp Sci & Math POB 120327 D-93025 Regensburg Germany Cent Washington Univ Dept Econ & Finance 400 E Univ Way Ellensburg WA 98926 USA

This paper introduces a novel chaotic flower pollination algorithm (CFPA) to solve a tardiness-constrained flow-shop scheduling problem with simultaneously loaded stations. This industrial manufacturing problem is modeled from a filter basket production line in Germany and has been generally solved using standard deterministic algorithms. This research develops a metaheuristic approach based on the highly efficient flower pollination algorithm coupled with different chaos maps for stochasticity. The objective function targeted is the tardiness constraint of the due dates. Fifteen different experiments with thirty scenarios are generated to mimic industrial conditions. The results are compared with the genetic algorithm and with the four standard benchmark priority rule-based deterministic algorithms of First In First Out, Raghu and Rajendran, Shortest Processing Time and Slack. From the obtained results and analysis of the relative difference, percentage relative difference and t tests, CFPA was found to be significantly better performing than the deterministic heuristics and the GA algorithm.

关键词： flower pollination algorithm Chaos maps Flow shop with tardiness

来源：评论

学校读者我要写书评

暂无评论

Combined economic and emission dispatch solution using flower pollination algorithm

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年第0期80卷 264-274页

作者： Abdelaziz, A. Y. Ali, E. S. Abd Elazim, S. M. Ain Shams Univ Fac Engn Elect Power & Machine Dept Cairo Egypt Zagazig Univ Fac Engn Elect Power & Machine Dept Zagazig Egypt

Economic Load Dispatch (ELD) is the process of allocating the required load between the available generation units such that the cost of operation is minimized. The ELD problem is formulated as a nonlinear constrained optimization problem with both equality and inequality constraints. The dual-objective Combined Economic Emission Dispatch (CEED) problem is considering the environmental impacts that accumulated from emission of gaseous pollutants of fossil-fueled power plants. In this paper, an implementation of flower pollination algorithm (FPA) to solve ELD and CEED problems in power systems is discussed. A comparison of the simulated results using the proposed FPA is carried out to confirm its effectiveness against other swarm intelligent algorithms for six various power systems. The superiority of the proposed FPA compared with other algorithms is demonstrated even for large scale power system considering valve point loading effect. (c) 2015 Elsevier Ltd. All rights reserved.

关键词： flower pollination algorithm Economic load dispatch Combined economic emission dispatch Emission constraints Valve point loading effect Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

FPA clust: evaluation of the flower pollination algorithm for data clustering

引用

EVOLUTIONARY INTELLIGENCE 2021年第3期14卷 1189-1199页

作者： Senthilnath, J. Kulkarni, Sushant Suresh, S. Yang, X. S. Benediktsson, J. A. ASTAR Inst Infocomm Res Singapore 138632 Singapore Tiger Analyt Chennai 600096 Tamil Nadu India Indian Inst Sci Dept Aerosp Engn Bangalore 560012 Karnataka India Middlesex Univ Sch Sci & Technol London NW4 4BT England Univ Iceland Fac Elect & Comp Engn Saemundargata 2 IS-101 Reykjavik Iceland

In this work, a standalone approach based on the flower pollination algorithm (FPA) is proposed for solving data clustering problems. The FPA is a nature-inspired algorithm simulating the behavior of flower pollination. The proposed approach is used to extract key information in terms of optimal cluster centers that are derived from training samples of the selected databases. These extracted cluster centers are then validated on test samples. Three datasets from the UCI machine learning data repository and an additional multi-spectral, real-time satellite image are chosen to illustrate the effectiveness and diversity of the proposed technique. The FPA performance is compared with the k-means, a popular clustering algorithm and metaheuristic algorithms, namely, the Genetic algorithm, Particle Swarm Optimization, Cuckoo Search, Spider Monkey Optimization, Grey Wolf Optimization, Differential Evolution, Harmony Search and Bat algorithm. The results are evaluated based on classification error percentage (CEP), time complexity and statistical significance. FPA has the lowest CEP for all four datasets and an average CEP of 28%, which is 5.5% lower than next best algorithm in that sense. The FPA is the second quickest algorithm to converge after HS algorithm. FPA also shows a higher level of statistical significance. Therefore, the obtained results show that the FPA efficiently clusters the data and performs better than the state-of-the-art methods.

关键词： Data clustering Metaheuristic algorithm flower pollination algorithm Multispectral dataset

来源：评论

学校读者我要写书评

暂无评论

Parallel Scheduling of Machines, Tool Transporter and Tools in a Multi Machine FMS with Alternative Routing Using flower pollination algorithm

引用

JOURNAL OF ADVANCED MANUFACTURING SYSTEMS 2023年第3期22卷 477-512页

作者： Reddy, N. Sivarami Lalitha, M. Padma Ramamurthy, D., V Rao, K. Prahlada Annamacharya Inst Technol Sci Mech Engn Dept Rajampet Andhra Pradesh India Annamacharya Inst Technol Sci Elect & Elect Engn Dept Rajampet Andhra Pradesh India Satya Inst Technol & Management Vizianagarm Andhra Pradesh India JNTUA Coll Engn Mech Engn Dept Ananthapuram Andhra Pradesh India

Scheduling jobs and tools is a significant problem for manufacturing systems. Inefficient job scheduling and tool loading planning may result in the underutilization of capital-intensive machines and a high machine idle time. Tools are transferred by a tool transporter (TT) between machines. Therefore, efficient scheduling of jobs, TT, and the tools with alternate machines enables a manufacturing system to increase machines' utilization and decrease their idle times. This paper addresses machines, TT, and tools concurrent scheduling with alternate machines and only one copy of every tool variety is made available where the tools are costly, in a multi-machine flexible manufacturing system, taking into account tool transfer times for makespan (MS) minimization. The tools are placed in a central tool magazine, which shares and serves them to many machines to cut down the price of duplicating the tools in each machine. The problem is to assign machines from alternate machines, tools, and corresponding TT trips, including the deadheading trip and loaded trip, to job operations for MS minimization. This paper uses a nonlinear mixed-integer programming framework to present the problem, and a flower pollination algorithm (FPA) is employed to solve it. The results show that FPA outperforms the Jaya algorithm, and the usage of alternate machines for the operations reduces the MS. Reduction in MS indicates an improvement in utilization of resources.

关键词： Flexible manufacturing system machines machines tool transporter and tools scheduling flower pollination algorithm makespan metaheuristics

来源：评论

学校读者我要写书评

暂无评论

An Extended Version of flower pollination algorithm

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第12期43卷 7573-7603页

作者： Singh, Deepika Singh, Urvinder Salgotra, Rohit Thapar Univ Dept ECE Patiala Punjab India

flower pollination algorithm (FPA) is a novel meta-heuristic algorithm inspired from the pollination of flowers. It has been applied to various fields of research and has proved its worth. But the algorithm suffers from certain limitations which confines its scope to different field of applications. In the present work, four modified versions of FPA have been proposed to improve exploration, searching accuracy, convergence speed and to maintain good balance between intensification and diversification. Sine Cosine operator has been added instead of Levy flight for improving the exploration and searching accuracy. Secondly, an improved probability switching has been used to balance the exploration and exploitation. The last modification uses improved distribution parameter for enhancing the local search phase. The results of proposed versions are tested on nineteen benchmark functions for different population sizes. The best modified version of algorithm has been named as linear-exponential flower pollination algorithm (LEFPA). The results of LEFPA are then compared with firefly algorithm, differential evolution, bat algorithm, bat flower pollination algorithm, Sine Cosine algorithm, particle swarm optimization, genetic algorithm, cuckoo search and FPA. Experimental results show that LEFPA algorithm is better when compared to other algorithms and can be considered as the potential candidate to become state of the art. Further, statistical testing has been done to prove the significance of the proposed approach.

关键词： Natural exponential function Linearly decreasing function Sine Cosine operator flower pollination algorithm Benchmark functions

来源：评论

学校读者我要写书评

暂无评论

An Improved flower pollination algorithm Based on Muti-population Co-evolutionary Strategy to Solve Function Optimization Problems

引用

ENGINEERING LETTERS 2020年第4期28卷 1182-1190页

作者： Guo, Meng-Wei Wei, Dong Wang, Jie-Sheng Ma, Xiao-Xu Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan Peoples R China

flower pollination algorithm is an algorithm that simulates the behavior of flower pollination in the biological world. In order to refine the accuracy of the FPA algorithm and effectively improve the convergence speed of the original algorithm FPA, two improved flower pollination algorithms based on clonal operator and bacterial foraging strategy are proposed. Then, based on the synergistic strategy between organisms, the muti-population co-evolutionary flower pollination algorithm (CFPA) was proposed and applied to the proposed two improved FPAs. Finally, this article designs a simulation experiment and uses six typical functions to carry out. According to the experimental data, the improved algorithm proposed in this paper has significantly improved the convergence speed of the algorithm and the optimization accuracy of the experimental results.

关键词： flower pollination algorithm Co-evolutionary Muti-population Function optimization

来源：评论

学校读者我要写书评

暂无评论

A flower pollination algorithm based Chebyshev polynomial neural network for net asset value prediction

引用

EVOLUTIONARY INTELLIGENCE 2023年第1期16卷 115-131页

作者： Mohanty, Smita Dash, Rajashree Siksha O Anusandhan Deemed Univ Dept Comp Sci & Engn Bhubaneswar Odisha India

Deriving relevant features from historic financial data and forecasting it accurately and impartially is an emerging and predominant field of research in the vast domain of finance. Mutual fund is a structured investment instrument in economic market and the measuring instrument required to price it is called as Net Asset Value (NAV). For NAV prediction, Artificial Neural Network has been immensely utilized in the past due to its adaptive learning, robustness and great ability to identify and handle hidden nonlinear patterns. In this study, a Chebyshev Polynomial Neural Network (CPNN) has been proposed for one day ahead prediction of three different NAV data set belonging to three leading Indian financial houses. The controlling parameters of the network are estimated by a computationally intelligent meta-heuristic algorithm known as flower pollination algorithm (FPA). It is a nature inspired algorithm, motivated by the pollination process of flowering plants with very few control parameters and rapid convergence rate. The efficacy of the proposed forecasting model is also examined by a comparative analysis of training of CPNN with other optimizing algorithms like Differential Evolution and Particle Swarm Optimization (PSO) on the same set of data collected over the same period of time. The convergence plots obtained during the training of the models and the different error metrics values calculated during their testing phase of the study showcase that the proposed CPNN-FPA model clearly outperforms the other two experimented predictive models.

关键词： Data mining Financial prediction Artificial neural network Chebyshev polynomial neural network flower pollination algorithm

来源：评论

学校读者我要写书评

暂无评论

VNE strategy based on chaos hybrid flower pollination algorithm considering multi-criteria decision making

引用

NEURAL COMPUTING & APPLICATIONS 2021年第17期33卷 10673-10684页

作者： Zhang, Peiying Liu, Fanglin Aujia, Gagangeet Singh Vashisht, Sahil China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England Chandigarh Univ Comp Sci & Engn Dept Mohali Punjab India CGC Coll Engn Res & Dev Dept Mohali 140307 Punjab India

With the development of science and technology and the need for multi-criteria decision making (MCDM), the optimization problem to be solved becomes extremely complex. The theoretically accurate and optimal solutions are often difficult to obtain. Therefore, meta-heuristic algorithms based on multi-point search have received extensive attention. The flower pollination algorithm (FPA) is a new swarm intelligence meta-heuristic algorithm, which can effectively control the balance between global search and local search through a handover probability, and gradually attracts the attention of researchers. However, the algorithm still has problems that are common to optimization algorithms. For example, the global search operation guided by the optimal solution is easy to lead the algorithm into local optimum, and the vector-guided search process is not suitable for solving some problems in discrete space. Moreover, the algorithm does not consider dynamic multi-criteria decision problems well. Aiming at these problems, the design strategy of hybrid flower pollination algorithm for virtual network embedding problem is discussed. Combining the advantages of the genetic algorithm and FPA, the algorithm is optimized for the characteristics of discrete optimization problems. The cross-operation is used to replace the cross-pollination operation to complete the global search and replace the mutation operation with self-pollination operation to enhance the ability of local search. Moreover, a life cycle mechanism is introduced as a complement to the traditional fitness-based selection strategy to avoid premature convergence. A chaos optimization strategy is introduced to replace the random sequence-guided crossover process to strengthen the global search capability and reduce the probability of producing invalid individuals. In addition, a two-layer BP neural network is introduced to replace the traditional objective function to strengthen the dynamic MCDM ability. Simulation res

关键词： Virtual network embedding Genetic algorithm flower pollination algorithm BP neural network Chaos optimization strategy

来源：评论

学校读者我要写书评

暂无评论

EEG Feature Extraction for Person Identification Using Wavelet Decomposition and Multi-Objective flower pollination algorithm

引用

IEEE ACCESS 2018年 6卷 76007-76024页

作者： Alyasseri, Zaid Abdi Alkareem Khader, Ahamad Tajudin Al-Betar, Mohammed Azmi Papa, Joao P. Alomari, Osama Ahmad Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Univ Kufa ECE Dept Fac Engn Najaf Iraq Al Balqa Appl Univ Dept Informat Technol Al Huson Univ Coll Irbid Jordan Sao Paulo State Univ Dept Comp BR-13084971 Bauru Brazil

In the modern life, the authentication technique for any system is considered as one of the most important and challenging tasks. Therefore, many researchers have developed traditional authentication systems to deal with our digital society. Recently, several studies showed that the brain electrical activity or electroencephalogram (EEG) signals could provide robust and unique features that can be considered as a new biometric authentication technique, given that accurate methods to decompose the signals must also be considered. This paper proposes a novel method for extracting EEG features using multi-objective flower pollination algorithm and the wavelet transform. The proposed method was applied in two scenarios for EEG signal decomposition to extract unique features from the original signals. Moreover, the proposed method is compared with the state-of-the-art techniques using different criteria with promising results.

关键词： Biometric authentication EEG wavelet decomposition feature extraction flower pollination algorithm multi-objective

来源：评论

学校读者我要写书评

暂无评论

Improved flower pollination algorithm and Its Application in User Identification Across Social Networks

引用

IEEE ACCESS 2019年 7卷 44359-44371页

作者： Li, Wenjing He, Zhiming Zheng, Jian Hu, Zhongdong Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China

Aiming at the shortages of basic flower pollination algorithm (FPA) with slow convergence speed, low search precision, and easy to fall into local optimum, a new adaptive FPA based on opposition-based learning and t-distribution (OTAFPA) was proposed and be applied to the social networks. First, the opposition-based learning strategy is utilized to increase the diversity and quality of the initial population. Then, the adaptive dynamic switching probability is introduced, which can effectively balance the global and local search according to the current number of iterations. Finally, the t-distribution variation is used to increase the population diversity and to help the algorithm jump out of the local optimum. The simulation experiments on eight classical test functions show that OTAFPA has better global optimization ability, which improves the convergence speed and the solution accuracy of the algorithm. The OTAFPA also shows superior performance in practical applications of user identification across social networks.

关键词： flower pollination algorithm opposition-based learning strategy dynamic switching probability t-distribution variation user identification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：