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Design of a superachromatic quarter-wave retarder for near-infrared region using flower pollination algorithm

OPTICAL ENGINEERING 2019年第9期58卷 095101-1-095101-6页

作者： Mukhopadhyay, Nilanjan Mandal, Sudip Bhattacharya, Kallol Saha, Arijit Global Inst Management & Technol Dept Elect & Commun Engn Krishnagar Nadia India Jalpaiguri Govt Engn Coll Dept Elect & Commun Engn Jalpaiguri W Bengal India Univ Calcutta Dept Appl Opt & Photon Kolkata India BP Poddar Inst Management & Technol Dept Elect & Commun Engn Kolkata India

Phase retarders normally exhibit strong wavelength dependence. However, for use with polychromatic light, achromatic retarders, which exhibit ideally nearly identical characteristics over a wide wavelength region, are in demand. Designing such a superachromatic retarder is a challenging task for an optical system designer where retardation will be almost constant within a wide range of wavelengths. The present investigation considers a system consisting of wave plates made of different materials, which focus on near-infrared wavelength range, viz. 800 to 2000 nm. Here, a flower pollination algorithm has been used for this nonlinear optimization problem. With the help of the mentioned optimization technique, the optimal values of thicknesses of wave plates are calculated for which the system act as a quarter-wave plate. The obtained result shows a significant improvement in terms of maximum deviation of retardation for the above-mentioned wavelength range. The proposed methodology holds promise for optimizing the design of optical systems. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： superachromatic retardation near-infrared optimization flower pollination algorithm

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DOA Estimation of Far-Field Sources by Exploiting Second Order Statistics of Bi-level Nested Arrays Using Biological flower pollination algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2024年第2期138卷 769-798页

作者： Hameed, Khurram Ahmed, Nauman Khan, Wasim Ahmed, Muneeb Farooq, Salma Zainab Ramzan, Muhammad Rashid Ramzan, Muhammad Int Islamic Univ Dept Elect Engn Islamabad Pakistan Northwestern Polytech Univ Sch Marine Sci & Technol Xian Peoples R China Natl Aerosp Sci & Technol Pk Aerosp Design & Innovat Ctr Kamra Pakistan Inst Space & Technol Islamabad Dept Space Sci Islamabad 44000 Pakistan Inst Space Technol Islamabad Dept Elect Engn Islamabad Pakistan Dept Elect & Comp Engn CUI Wah Campus Wah Cantt Pakistan

The immense degree of freedom (DOF), high array aperture, non-uniform linear arrays, and reduced mutual coupling have developed interest in the estimations of the direction of arrival (DOA). Due to complex previous structures, this paper investigates the bi-level sparse linear nested array (SNA) concepts to discuss element spacing and different ranges on uniform DOF. Then features of flower pollination algorithm is applied to the proposed two-level SNA to generalize and enhance the proposed structure further. In order to boost DOF, it is also investigated local and global minima of highly non-linear functions. The proposed technique for quantifying the DOA is reviewed analytically using evaluation parameters like cumulative distributive function, accuracy, root mean square error, and robustness against noise and snapshots. The simulation findings prove its validation with the analytical model and target the accuracy with fewer separations and the minimum number of physical sensors in relation to particle swarm optimization. Moreover, the strength of the proposed study further validated by comparing with Cramer Rao Bound for minimum variance which shows that the FPA outperforms.

关键词： Direction of arrival MUSIC Particle swarm optimization flower pollination algorithm

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A Novel Feature Selection Method Based on MRMR and Enhanced flower pollination algorithm for High Dimensional Biomedical Data

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CURRENT BIOINFORMATICS 2022年第2期17卷 133-149页

作者： Yan, Chaokun Li, Mengyuan Ma, Jingjing Liao, Yi Luo, Huimin Wang, Jianlin Luo, Junwei Henan Univ Sch Comp & Informat Engn Kaifeng Peoples R China Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo Henan Peoples R China Tsinghua Univ Acad Arts & Design Beijing Peoples R China China Mobile Online Serv Co Ltd Hong Kong Peoples R China

Background: The massive amount of biomedical data accumulated in the past decades can be utilized for diagnosing disease. Objective: However, the high dimensionality, small sample sizes, and irrelevant features of data often have a negative influence on the accuracy and speed of disease prediction. Some existing machine learning models cannot capture the patterns on these datasets accurately without utilizing feature selection. Methods: Filter and wrapper are two prevailing feature selection methods. The filter method is fast but has low prediction accuracy, while the latter can obtain high accuracy but has a formidable computation cost. Given the drawbacks of using filter or wrapper individually, a novel feature selection method, called MRMR-EFPATS, is proposed, which hybridizes filter method Minimum Redundancy Maximum Relevance (MRMR) and wrapper method based on an improved flower pollination algorithm (FPA). First, MRMR is employed to rank and screen out some important features quickly. These features are further chosen for individual populations following the wrapper method for faster convergence and less computational time. Then, due to its efficiency and flexibility, FPA is adopted to further discover an optimal feature subset. Results: FPA still has some drawbacks, such as slow convergence rate, inadequacy in terms of searching new solutions, and tends to be trapped in local optima. In our work, an elite strategy is adopted to improve the convergence speed of the FPA. Tabu search and Adaptive Gaussian Mutation are employed to improve the search capability of FPA and escape from local optima. Here, the KNN classifier with the 5-fold-CV is utilized to evaluate the classification accuracy. Conclusion: Extensive experimental results on six public high dimensional biomedical datasets show that the proposed MRMR-EFPATS has achieved superior performance compared to other state-of-theart methods.

关键词： Feature selection flower pollination algorithm MRMR elite strategy adaptive gaussian mutation tabu search

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Island flower pollination algorithm for global optimization

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JOURNAL OF SUPERCOMPUTING 2019年第8期75卷 5280-5323页

作者： Al-Betar, Mohammed Azmi Awadallah, Mohammed A. Abu Doush, Iyad Hammouri, Abdelaziz I. Mafarja, Majdi Alyasseri, Zaid Abdi Alkareem Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Irbid Jordan Al Aqsa Univ Dept Comp Sci POB 4051 Gaza Palestine Yarmouk Univ Dept Comp Sci Irbid Jordan Amer Univ Kuwait Comp Sci & Informat Syst Dept Salmiya Kuwait Al Balqa Appl Univ Dept Comp Informat Syst Al Salt 19117 Jordan Birzeit Univ Dept Comp Sci POB 14 West Bank Palestine Univ Kufa ECE Dept Fac Engn Najaf Iraq

flower pollination algorithm (FPA) is a recent swarm-based evolutionary algorithm that was inspired by the biological evolution of pollination of the flowers. It deals with a panmictic population of pollens (or solutions) at each generation, using global and local pollination operators, to improve the whole population at once. Like other evolutionary algorithms, FPA has a chronic shortcoming that lies in its inability to maturely converge. This is conventionally known as a premature convergence where the diversity of the population is loosed and thus the search is stagnated. Island model is one of the successful structured population techniques that were utilized in the theoretical characteristics of several evolutionary-based algorithms. In this model, the population is divided into a set of islands. The knowledge is distributed among those islands using a migration process that is controlled by migration rate, topology, frequency, and policy. In this paper, the island model is utilized in the evolution process of FPA to control diversity. The proposed approach is called IsFPA. The ability of IsFPA in maintaining the diversity during the search process, and in producing impressive results, can be interpreted by utilizing the island model in the FPA optimization framework. To assess the efficiency of IsFPA, 23 benchmark functions with various sizes and complexities were used. The best parameter configurations of IsFPA were investigated and analyzed. Comparing the results of IsFPA with those of state-of-the-art methods which are FPA, genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), multi-verse optimizer (MVO), island bat algorithm (iBA), and island harmony search (iHS), the comparison results show that the IsFPA is able to control the diversity and improves the outcomes where IsFPA is ranked first followed by FPA, iBA, iHS, GSA, MVO, GA, PSO, respectively, based on the Friedman test with Holm and Hochberg as post hoc st

关键词： flower pollination algorithm Island model Structured population Optimization

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Evaluating chaotic functions with flower pollination algorithm for modelling an optimized low complexity neural network based NAV predictor model

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SOFT COMPUTING 2022年第18期26卷 9395-9417页

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

Obtaining accurate and impartial forecasts from historical financial data is an emerging and dominant research area in the vast sphere of finance. An investment instrument in the market for structured investments is referred to as a mutual fund, and the tool to measure its price is the Net Asset Value (NAV). Investment managers and investors rely on accurate estimates of NAV of different investment funds to make informed decisions. However, predicting such complex financial series is challenging due to uncertainty and influence of economic and political factors. In this study, a NAV predictor model is designed using a low-complexity neural network, called Legendre Polynomial Neural Network (LPNN). Additionally, a Chaotic flower pollination algorithm (CHFPA) was proposed in the learning phase of the network for tuning its unknown parameters. A series of ten chaotic maps has been applied to the classical FPA through three stagesto achieve improvement in the diversity of the initial population as well as to avoid local optima by carrying out unexpected swaps between global and local search processes. With an aim of accelerating global convergence speed, the selected chaotic variants have been thoroughly investigated on ten well known standard benchmark functions to find the most efficient one. The proposed LPNN-CHFPA NAV predictor model is tested on three separate data sets of reputed Indian financial firms for one day-ahead forecasting of NAV. In comparison with the other prediction models, based on the convergence curve, scatter plots, and various error metrics, the experimental results clearly show that the proposed framework performs well.

关键词： Chaos theory Meta-heuristic algorithms flower pollination algorithm Legendre polynomial neural network Chaotic flower pollination algorithm

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A flower pollination algorithm for the double-floor corridor allocation problem^†

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2019年第20期57卷 6506-6527页

作者： Guan, Chao Zhang, Zeqiang Li, Yunpeng Southwest Jiaotong Univ Sch Mech Engn Chengdu Sichuan Peoples R China

This research explores the double-floor corridor allocation problem (DFCAP), which deals with the optimal arrangement of departments over two floors and then place them along both sides against a corridor. This problem is a natural extension of the corridor allocation problem (CAP) to additional floors;the layout of each floor can be regarded as an approximately independent CAP. The DFCAP is commonly observed in manufacturing and service buildings. In this study, a mixed-integer programming formulation for the DFCAP is developed, and it is able to reduce to the classical CAP model. Then a novel flower pollination algorithm is provided, which is discretised using swap pair set approach to solve the considered DFCAP. In addition, to ameliorate the algorithm, three constructive heuristic rules are developed to produce a reasonably good initial population;meanwhile, a variable neighbourhood search structure is presented to prevent prematurity in arrival at a poor local solution. Finally, several instances for the DFCAP with a size of 9 <= n <= 80 are employed in the algorithms, as well as in mixed-integer non-linear programming (MINLP) formulations, which are solved with GUROBI 7.0.1. Moreover, the above-mentioned instances are utilized to show that the proposed algorithm performs better in comparison to the state-of-the-art optimization algorithms.

关键词： Facility design double-floor corridor allocation problem flower pollination algorithm combinatorial optimisation

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A new environment-aware scheduling method for remanufacturing system with non-dedicated reprocessing lines using improved flower pollination algorithm

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JOURNAL OF MANUFACTURING SYSTEMS 2020年 57卷 94-108页

作者： Shi, Jiaxuan Zhang, Wenyu Zhang, Shuai Wang, Weirui Lin, Jian Feng, Ruijun Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310027 Peoples R China Zhejiang Univ Sch Mech Engn Hangzhou 310027 Peoples R China

As the resource crisis and environmental pollution become increasingly prominent, remanufacturing is currently a popular research field for addressing these issues. However, scheduling for remanufacturing is more difficult than that for traditional manufacturing because the entire remanufacturing system (RMS) involves three cooperative subsystems: disassembly, reprocessing, and reassembly. Few studies have focused on the scheduling of the entire RMS and addressed the quality differences of defective components using non-dedicated reprocessing lines. Thus, this paper proposes a new environment-aware scheduling model for RMS, which considers not only three subsystems simultaneously, but also the non-dedicated reprocessing lines related to the recycled quality of defective components. The proposed model also integrates environmental factors by considering the carbon emissions of machines to take advantage of the environmental benefits in remanufacturing. To solve the proposed model, an improved flower pollination algorithm with new two-dimensional representation scheme is employed, which not only utilizes the self-adaptive parameter but also integrates path relinking technique, local search strategy, and elite replacement strategy. Experiments are performed to illustrate the effectiveness of the proposed algorithm for solving the remanufacturing scheduling problem by comparing it with six baseline algorithms.

关键词： Remanufacturing system Remanufacturing scheduling Environmental protection Carbon emission Non-dedicated reprocessing lines flower pollination algorithm

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PI controller Optimization for a Heat Exchanger Through Metaheuristic Bat algorithm, Particle Swarm Optimization, flower pollination algorithm and Cuckoo Search algorithm

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IEEE LATIN AMERICA TRANSACTIONS 2017年第9期15卷 1801-1807页

作者： Damasceno, N. C. Filho, O. G. Univ Potiguar Engn Automacao Proc Gas Nat Natal RN Brazil Univ Fed Rio Grande do Norte Natal RN Brazil

This paper presents the modeling and computer simulation of a control system for a shell and tube heat exchanger, using Bat algorithms, Particle Swarm Optimization, flower pollination algorithm and Cuckoo Search algorithm. To evaluate the performance of different methods of tuning, we compared the values of the transient of the response to step in eight mesh settings generated. It has also established a comparison between these two types of mesh using the performance indices proposed in the literature, with optimized system by Bat algorithms got the best values of transient in relation to the Particle Swarm Optimization, Cuckoo Search algorithm and flower pollination algorithm. Performance indices FPA and PSO obtained better results.

关键词： PI controller Bat algorithm PSO flower pollination algorithm Heat Exchanger Optimization

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Synthesis of linear antenna array using flower pollination algorithm

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NEURAL COMPUTING & APPLICATIONS 2018年第2期29卷 435-445页

作者： Singh, Urvinder Salgotra, Rohit Thapar Univ Patiala Punjab India Chandigarh Univ Mohali Punjab India

Linear antenna array (LAA) design is a classical electromagnetic problem. It has been extensively dealt by number of researchers in the past, and different optimization algorithms have been applied for the synthesis of LAA. This paper presents a relatively new optimization technique, namely flower pollination algorithm (FPA) for the design of LAA for reducing the maximum side lobe level (SLL) and null control. The desired antenna is achieved by controlling only amplitudes or positions of the array elements. FPA is a novel meta-heuristic optimization method based on the process of pollination of flowers. The effectiveness and capability of FPA have been proved by taking difficult instances of antenna array design with single and multiple objectives. It is found that FPA is able to provide SLL reduction and steering the nulls in the undesired interference directions. Numerical results of FPA are also compared with the available results in the literature of state-of-the-art algorithms like genetic algorithm, particle swarm optimization, cuckoo search, tabu search, biogeography based optimization (BBO) and others which also proves the better performance of the proposed method. Moreover, FPA is more consistent in giving optimum results as compared to BBO method reported recently in the literature.

关键词： Antenna Linear antenna array Optimization flower pollination algorithm Evolutionary computing

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An improved flower pollination algorithm for solving a Type-II U-shaped assembly line balancing problem with energy consideration

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ASSEMBLY AUTOMATION 2020年第6期40卷 847-856页

作者： Zhang, Beikun Xu, Liyun Tongji Univ Sch Mech Engn Shanghai Peoples R China

Purpose The increasing energy shortage leads to worldwide attentions. This paper aims to develop a mathematical model and optimization algorithm to solve the energy-oriented U-shaped assembly line balancing problem. Different from most existing works, the energy consumption is set as a major objective. Design/methodology/approach An improved flower pollination algorithm (IFPA) is designed to solve the problem. The random key encoding mechanism is used to map the continuous algorithm into discrete problem. The pollination rules are modified to enhance the information exchange between individuals. Variable neighborhood search (VNS) is used to improve the algorithm performance. Findings The experimental results show that the two objectives are in conflict with each other. The proposed methodology can help manager obtain the counterbalance between them, for the larger size balancing problems, and the reduction in objectives is even more significant. Besides, the experiment results also show the high efficiency of the proposed IFPA and VNS. Originality/value The main contributions of this work are twofold. First, a mathematical model for the U-shaped assembly line balancing problem is developed and the model is dual foci including minimized SI and energy consumption. Second, an IFPA is proposed to solve the problem.

关键词： Energy consumption Balancing problem flower pollination algorithm U-shaped assembly line

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