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A new hybrid model based on relevance vector machine with flower pollination algorithm for phycocyanin pigment concentration estimation

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第25期28卷 32564-32579页

作者： Pham, Quoc Bao Sammen, Saad Sh. Abba, Sani Isa Mohammadi, Babak Shahid, Shamsuddin Abdulkadir, Rabiu Aliyu Duy Tan Univ Inst Res & Dev Danang 550000 Vietnam Duy Tan Univ Fac Environm & Chem Engn Danang 550000 Vietnam Univ Diyala Dept Civil Engn Coll Engn Baqubah Diyala Governor Iraq Baze Univ Dept Civil Engn Fac Engn Abuja Nigeria Lund Univ Dept Phys Geog & Ecosyst Sci Solvegatan 12 SE-22362 Lund Sweden Univ Teknol Malaysia UTM Sch Civil Engn Fac Engn Skudai 81310 Johor Malaysia Kano Univ Sci & Technol Dept Elect & Elect Wudil Nigeria Kano Univ Sci & Technol Dept Comp Sci Wudil Nigeria

Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measurement of the cyanobacteria are tedious, cumbersome, and often lead to inaccurate results. The quantity of phycocyanin (PC) pigment is considered more appropriate for cyanobacteria monitoring. Traditional approaches for PC estimation are time-consuming, expensive, and require high expertise. Recently, some studies have proposed the application of artificial intelligence (AI) techniques to predict the amount of PC concentration. Nonetheless, most of these researches are limited to standalone modeling schemas such as artificial neural network (ANN), multilayer perceptron (MLP), and support vector machine (SVM). The independent schema provides imprecise results when faced with highly nonlinear systems and data uncertainties resulting from environmental disturbances. To alleviate the limitations of the existing models, this study proposes the first application of a hybrid AI model that integrates the potentials of relevance vector machine (RVM) and flower pollination algorithm (RVM-FPA) to predict the PC concentration in water resources. The performance of the hybrid model is compared with the standalone RVM model. The prediction performance of the proposed models was evaluated at two stations (stations 508 and 478) using different statistical and graphical performance evaluation methods. The results showed that the hybrid models exhibited higher performance at both stations compared to the standalone RVM model. The proposed hybrid RVM-FPA can therefore serve as a reliable predictive tool for PC concentration in water resources.

关键词： Phycocyanin pigment concentration Prediction models Hybrid models Relevance vector machine flower pollination algorithm

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Quality of Service Enhancement in Wireless Sensor Network Using flower pollination algorithm

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PROGRAMMING AND COMPUTER SOFTWARE 2018年第6期44卷 398-406页

作者： Muruganantham, R. Ganeshkumar, P. MAM Sch Engn Dept Comp Sci & Engn Tiruchirappalli Tamil Nadu India Anna Univ Dept Informat Technol Reg Campus Coimbatore Tamil Nadu India

One of the renowned and drastically rising research works is developing and deploying emerging applications under wireless sensor networks. Surveillance monitoring, military and health care industries are some examples of emerging applications. Generally, sensor nodes are deployed randomly and distributed within the network region. In Wireless sensor network, it is hard to increase the network lifetime and to maximize the coverage for interest points under certain constraints. A sensor node can able to sense only the environmental information within its sensing region;hence the deployment of the node must cover the points of interests by considering energy of the nodes. To do that, we propose a flower pollination algorithm (FPA) to optimize the locations of the sensor nodes and balance the residual energy of the nodes. The FP algorithm adjusts the sensor nodes and schedules the node condition (Sleep/Active) to maintain target coverage for long period of time. The proposed FPA is experimented using a simulation tool and the obtained results are verified. From the results it is noticed that the target coverage time is increased by 30% and also the performance is evaluated by comparing the obtained results with the existing IWD and ACO algorithms. Finally, it is confirmed that the FP algorithm outperforms than the IWD and ACO by increasing the energy efficiency 40% than the existing approaches.

关键词： Wireless Sensor Networks target coverage Sensor Node Deployment quality of service optimization flower pollination algorithm Artificial Bee Colony particle swarm optimization

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Color image quantization using flower pollination algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第43-44期79卷 32151-32168页

作者： Lei, Mengyi Zhou, Yongquan Luo, Qifang Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi High Sch Complex Syst & Computat Intellig Key Labs Nanning 530006 Guangxi Peoples R China

flower pollination algorithm (FPA) is a swarm-based optimization technique that has attracted the attention of many researchers in several optimization fields due to its impressive characteristics. This paper proposes a new application for FPA in the field of image processing to solve the color quantization problem, which is use the mean square error is selected as the objective function of the optimization color quantization problem to be solved. By comparing with theK-means and other swarm intelligence techniques, the proposed FPA for Color Image Quantization algorithm is verified. Computational results show that the proposed method can generate a quantized image with low computational cost. Moreover, the quality of the image generated is better than that of the images obtained by six well-known color quantization methods.

关键词： flower pollination algorithm Color image quantization Metaheuristic algorithm

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Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems

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ENERGY 2016年 101卷 506-518页

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

ELD (Economic Load Dispatch) 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 CEED (Combined Economic Emission Dispatch) problem is considering the environmental impacts that accumulated from emission of gaseous pollutants of fossil-fueled power plants. In this paper, an implementation of FPA (flower pollination algorithm) to solve ELD and CEED problems in power systems is discussed. Results obtained by the proposed FPA are compared with other optimization algorithms for various power systems. The results introduced in this paper show that the proposed FPA outlasts other techniques even for large scale power system considering valve point effect in terms of total cost and computational time. (C) 2016 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

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A novel bat flower pollination algorithm for synthesis of linear antenna arrays

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NEURAL COMPUTING & APPLICATIONS 2018年第7期30卷 2269-2282页

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

In this paper, a novel algorithm, namely bat flower pollination (BFP) is proposed for synthesis of unequally spaced linear antenna array (LAA). The new method is a combination of bat algorithm (BA) and flower pollination algorithm (FPA). In BFP, both BA and FPA interact with each other to escape from local minima. The results of BFP for solving a set of 13 benchmark functions demonstrate its superior performance as compared to variety of well-known algorithms available in the literature. The novel proposed method is also used for the synthesis of unequally spaced LAA for single and multi-objective design. Simulation results show that BFP is able to provide better synthesis results than wide range of popular techniques like genetic algorithm, differential evolution, cuckoo search, particle swarm optimization, back scattering algorithm and others.

关键词： Antenna Bat algorithm flower pollination algorithm Linear antenna array Side lobe level

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Combined heat and power economic dispatch problem with binary method using flower pollination algorithm and differential evolution

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ELECTRICAL ENGINEERING 2023年第4期105卷 2161-2168页

作者： Mellal, Mohamed Arezki Khitous, Marwa Zemmouri, Meriem MHamed Bougara Univ Fac Technol LMSS Boumerdes Algeria

Nowadays, the need for electrical energy became crucial in the world. The co-generation plants, which simultaneously produce electrical and heat energies, are one of the alternative solutions to supply people and industry with both energies. The present work addresses the cost minimization of the nonconvex combined heat and power dispatch problem (CHPED). The nonconvex operating region is handled using the binary method, and the optimization problem is solved using two nature-inspired algorithms, namely the flower pollination algorithm (FPA) and the differential evolution (DE). Penalty functions are adopted to handle all the operating constraints, units' limits, and demands. The results obtained compare the algorithms and those of the literature. It is observed that the fuel cost obtained by the flower pollination algorithm (FPA) is less than the one obtained by the differential evolution (DE) and the particle swarm optimization (PSO).

关键词： Co-generations plants Optimization Binary method flower pollination algorithm Differential evolution

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Multi-Level Image Thresholding Using Modified flower pollination algorithm

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IEEE ACCESS 2018年 6卷 30508-30519页

作者： Shen, Liang Fan, Chongyi Huang, Xiaotao Natl Univ Def Technol Coll Elect Sci Changsha 410000 Hunan Peoples R China

Multilevel thresholding is an important approach for image segmentation which has drawn much attention during the past few years. Traditional methods for multilevel thresholding are computationally expensive, because they use the exhaustive searching strategy. To overcome the problem, metaheuristic algorithms are widely applied in this research area for searching the optimal thresholds recently. In this paper, a modified flower pollination algorithm, as a novel improved metaheuristic algorithm, is proposed for multi-level thresholding. Two modifications are proposed to improve the original FPA. First, a fitness Euclidean-distance ratio strategy is employed to modify the local pollination of the original FPA. Second, the global pollination in the original FPA is also biologically modified to improve exploration. Experiments are conducted between seven state-of-the-art metaheuristic algorithms and the proposed one. Both reallife images and remote sensing images are used in the experiments to test the performance of the involved algorithms. The experimental results significantly demonstrate the superiority of our method in terms of the objective function value, image quality measures, and convergence performance.

关键词： flower pollination algorithm image segmentation multilevel thresholding metaheuristic

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Solving nonlinear systems and unconstrained optimization problems by hybridizing whale optimization algorithm and flower pollination algorithm

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MATHEMATICS AND COMPUTERS IN SIMULATION 2021年 190卷 1342-1369页

作者： Tawhid, M. A. Ibrahim, A. M. Thompson Rivers Univ Fac Sci Dept Math & Stat Kamloops BC V2C 0C8 Canada Al Azhar Univ Fac Sci Dept Math Assiut Branch Assiut Egypt

This paper suggests a new hybrid algorithm by integrating two population-based algorithms: Whale Optimization algorithm (WOA) and flower pollination algorithm (FPA), to solve complex nonlinear systems and unconstrained optimization problems. WOFPA denotes the suggested algorithm, a hybrid Whale Optimization algorithm and flower pollination algorithm. Nonlinear systems can be cast into unconstrained optimization problems, called merit functions, where the optimal solutions for the merit functions are equivalent to the solutions of nonlinear systems. WOFPA aims to decrease the execution time and the complexity of WOA and FPA. WOFPA has the advantages of WOA and FPA;WOFPA is a high-quality algorithm to solve both problems, nonlinear systems and unconstrained optimization problems. For example, FPA may have a premature convergence in the local optima, and WOFPA subdues the disadvantage of FPA. Numerical experiments of 14 benchmarks nonlinear systems and 30 CEC 2014 benchmarks unconstrained optimization functions with various dimensions are employed to test the performance of WOFPA. To have a further investigation for the performance of WOFPA, WOFPA is compared with WOA, FPA, and other existing algorithms from the literature. Two non-parametric statistical tests, Wilcoxon statistical test and the Friedman test, are conducted for this study to check the performance of the proposed algorithms and other compared algorithms and the significance of our results. The experiment results demonstrate that WOFPA performs better than other algorithms in the literature by getting the optimum solutions for most nonlinear systems and optimization problems and proves its efficiency compared with other existing algorithms. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Hybridization algorithm Metaheuristics Whale optimization algorithm flower pollination algorithm Unconstrained optimization problems System of nonlinear equations

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An innovative flower pollination algorithm for continuous optimization problem

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APPLIED MATHEMATICAL MODELLING 2020年 83卷 237-265页

作者： Chen, Yang Pi, Dechang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

The flower pollination algorithm (FPA) is a relatively new swarm optimization algorithm that inspired by the pollination phenomenon of natural phanerogam. Since its proposed, it has received widespread attention and been applied in various engineering fields. However, the FPA still has certain drawbacks, such as inadequate optimization precision and poor convergence. In this paper, an innovative flower pollination algorithm based on cloud mutation is proposed (CMFPA), which adds information of all dimensions in the global optimization stage and uses the designed cloud mutation method to redistribute the population center. To verify the performance of the CMFPA in solving continuous optimization problems, we test twenty-four well-known functions, composition functions of CEC2013 and all benchmark functions of CEC2017. The results demonstrate that the CMFPA has better performance compared with other state-of-the-art algorithms. In addition, the CMFPA is implemented for five constrained optimization problems in practical engineering, and the performance is compared with state-of-the-art algorithms to further prove the effectiveness and efficiency of the CMFPA. (C) 2020 Elsevier Inc. All rights reserved.

关键词： flower pollination algorithm Cloud mutation Continuous optimization Engineering optimization problem

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Synthesis of flat-top beam pattern of linear antenna arrays with restricted side lobe level, VSWR and independent nulls using flower pollination algorithm

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INTERNATIONAL JOURNAL OF ELECTRONICS 2019年第12期106卷 1964-1977页

作者： Patidar, Hemant Mahanti, Gautam Kumar Muralidharan, R. Oriental Univ Dept Elect & Elect Engn Indore Madhya Pradesh India Natl Inst Technol Dept Elect & Commun Engn Durgapur India NUST Coll Engn Dept Elect & Comp Engn Muscat Oman

Inspired by the biological evolution and the natural selection, evolutionary algorithms have already shown their success in obtaining the solutions for many complex optimisation problems. The aim of this paper is to study the performance of flower pollination algorithm for the synthesis of the flat-top beam pattern in linear antenna arrays with restrictions on the peak side lobe level, ripple in the flat-top portion, and maximum Voltage Standing Wave Ratio along with the two independent nulls. Mutual coupling is considered between the parallel dipole antennas. This synthesis is obtained by the generation of current amplitudes with binary phase shifters of 0 and 180 degrees for the antenna elements. Matlab software is used for the simulations. The values of the different antenna parameters as well as the statistical parameters such as the global best fitness value, worst fitness value, mean and standard deviation from the optimisation process generated by the simulations are obtained. Two examples are presented in this paper with different number of antenna elements. The obtained results show the superiority of the flower pollination algorithm over Particle Swarm Optimisation algorithm. Moreover, the results obtained from simulations are validated by using FEKO software.

关键词： Antenna array flat-top beam pattern flower pollination algorithm ripple side lobe level Particle Swarm Optimisation algorithm voltage standing wave ratio

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