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Wireless sensor placement for structural monitoring using information-fusing firefly algorithm

SMART MATERIALS AND STRUCTURES 2017年第10期26卷

作者： Zhou, Guang-Dong Yi, Ting-Hua Xie, Mei-Xi Li, Hong-Nan Hohai Univ Coll Civil & Transportat Engn Nanjing 210098 Jiangsu Peoples R China Dalian Univ Technol Sch Civil Engn Dalian 116024 Peoples R China

Wireless sensor networks (WSNs) are promising technology in structural health monitoring (SHM) applications for their low cost and high efficiency. The limited wireless sensors and restricted power resources in WSNs highlight the significance of optimal wireless sensor placement (OWSP) during designing SHM systems to enable the most useful information to be captured and to achieve the longest network lifetime. This paper presents a holistic approach, including an optimization criterion and a solution algorithm, for optimally deploying self-organizing multi-hop WSNs on large-scale structures. The combination of information effectiveness represented by the modal independence and the network performance specified by the network connectivity and network lifetime is first formulated to evaluate the performance of wireless sensor configurations. Then, an information-fusing firefly algorithm (IFFA) is developed to solve the OWSP problem. The step sizes drawn from a Levy distribution are adopted to drive fireflies toward brighter individuals. Following the movement with Levy flights, information about the contributions of wireless sensors to the objective function as carried by the fireflies is fused and applied to move inferior wireless sensors to better locations. The reliability of the proposed approach is verified via a numerical example on a long-span suspension bridge. The results demonstrate that the evaluation criterion provides a good performance metric of wireless sensor configurations, and the IFFA outperforms the simple discrete firefly algorithm.

关键词： structural health monitoring optimal sensor placement firefly algorithm wireless sensor network information fusion

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Enhancing firefly algorithm with courtship learning

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INFORMATION SCIENCES 2021年 543卷 18-42页

作者： Peng, Hu Zhu, Wenhua Deng, Changshou Wu, Zhijian Jiujiang Univ Sch Informat Sci & Technol Jiujiang 332005 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

The firefly algorithm (FA) is a nature-inspired heuristic optimization algorithm based on the luminescence and attraction behavior of fireflies. Although the FA can effectively solve complex optimization problems, it suffers from premature convergence because of its simple full attraction model. However, in nature, fireflies exhibit luminous behavior to attract mates. The attractiveness between fireflies of opposite sexes depends not only on the light intensity they emit, but also on their individual size, location, and other factors. In the original FA, all fireflies are assumed to be the same, i.e., they have no gender-based difference, which is not true biologically. Therefore, in this paper, we proposed a novel courtship learning (CL) framework to enhance the performance of the FA. In the proposed framework, the population is divided into female and male subpopulations. The female archiving mechanism is adopted to select excellent fireflies, which are assumed to be female fireflies. When the selected male firefly emits light that is less bright than that of the current firefly, a female individual will be selected from the female archive to guide the movement of the selected male firefly. Comprehensive experiments are conducted on the CEC 2013 benchmark set and the proposed CL framework is integrated with other advanced FA variants to verify its effects. Experimental results confirm that the proposed framework significantly enhances the performance of the original FA and advanced FA variants. (C) 2020 Elsevier Inc. All rights reserved.

关键词： firefly algorithm Swarm intelligence algorithm Global optimization Courtship learning

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An Improved firefly algorithm With Specific Probability and Its Engineering Application

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IEEE ACCESS 2019年 7卷 57424-57439页

作者： Wang, Chunfeng Chu, Xinyue Henan Normal Univ Coll Math & Informat Sci Xinxiang 453007 Henan Peoples R China

firefly algorithm (FA) belongs to the swarm intelligence algorithm, which is famous for its strong exploration, a small number of parameter settings and effortless operation. However, there are some drawbacks in the searching process for FA, as the poor accuracy of the solution, high-computational time complexity, and doughty oscillation. These phenomenons are attributed to two factors: 1) in classical FA, the firefly, which is gloomier than others can be attracted by any one of them and 2) FA cannot fully utilize the information of objective function and its fitness. In this paper, to overcome these shortcomings, based on specific probability p(fit), a new modified firefly algorithm (pFA) is proposed. In this algorithm, for speeding up the convergence, the specific probability p(fit) determined by the value of fitness of the firefly is used to choose a neighbor among the better fireflies compared with the predefined firefly, which helps the predefined firefly to move toward a better direction. If there is no neighbor, the opposite learning strategy is employed to lead the firefly to move. The performance of pFA is tested on some well-known benchmark functions. The findings of the test show that pFA is outperformed to FA and some other state-of-the-art algorithms. Finally, we apply pFA to solve four engineering applications.

关键词： firefly algorithm specific probability opposite learning engineering application

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Class center-based firefly algorithm for handling missing data

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JOURNAL OF BIG DATA 2021年第1期8卷 1-14页

作者： Nugroho, Heru Utama, Nugraha Priya Surendro, Kridanto Inst Teknol Sch Elect Engn & Informat Bandung Indonesia

A significant advancement that occurs during the data cleaning stage is estimating missing data. Studies have shown that improper data handling leads to inaccurate analysis. Furthermore, most studies indicate the occurrence of missing data irrespective of the correlation between attributes. However, an adaptive search procedure helps to determine the estimates of the missing data when correlations between attributes are considered in the process. firefly algorithm (FA) implements an adaptive search procedure in the imputation of the missing data by determining the estimated value closest to others' value. Therefore, this study proposes a class center-based adaptive approach model for retrieving missing data by considering the attribute correlation in the imputation process (C3-FA). The result showed that the class center-based firefly algorithm (FA) is an efficient technique for obtaining the actual value in handling missing data with the Pearson correlation coefficient (r) and root mean squared error (RMSE) close to 1 and 0, respectively. In addition, the proposed method has the ability to maintain the true distribution of data values. This is indicated by the Kolmogorov-Smirnov test, which stated that the value of D-KS for most attributes in the dataset is generally closer to 0. Furthermore, the accuracy evaluation results using three classifiers showed that the proposed method produces good accuracy.

关键词： Missing data Correlation Imputation firefly algorithm Class center

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Optimized gray-scale image watermarking using DWT-SVD and firefly algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第17期41卷 7858-7867页

作者： Mishra, Anurag Agarwal, Charu Sharma, Arpita Bedi, Punam Univ Delhi Deendayal Upadhyay Coll Dept Elect Delhi 110007 India Univ Delhi Dept Comp Sci Delhi 110007 India Univ Delhi Deendayal Upadhyay Coll Dept Comp Sci Delhi 110007 India

This paper presents an optimized watermarking scheme based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). The singular values of a binary watermark are embedded in singular values of the LL3 sub-band coefficients of the host image by making use of multiple scaling factors (MSFs). The MSFs are optimized using a newly proposed firefly algorithm having an objective function which is a linear combination of imperceptibility and robustness. The PSNR values indicate that the visual quality of the signed and attacked images is good. The embedding algorithm is robust against common image processing operations. It is concluded that the embedding and extraction of the proposed algorithm is well optimized, robust and show an improvement over other similar reported methods. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Image watermarking firefly algorithm Perceptible quality Normalized cross-correlation Robustness Multiple scaling factors (MSFs)

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Improved Speciation-Based firefly algorithm in Dynamic and Uncertain Environments

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JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 2016年第3期32卷 661-676页

作者： Nasiri, Babak Meybodi, Mohammad Reza Islamic Azad Univ Dept Comp & Informat Technol Engn Qazvin Branch Qazvin 341851416 Iran Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran 158754413 Iran

Many real-world optimization problems are dynamic in nature. The applied algorithms in this environment can pose serious challenges, especially when the search space is multimodal with multiple, time-varying optima. To address these challenges, this paper proposed a speciation-based firefly algorithm to maintain the population diversity in different areas of the landscape. To improve the performance of the algorithm, multiple adaptation techniques have been used such as adapting the number of species, number of fireflies in each specie and number of active fireflies in each specie. A set of experiments are conducted to study the performance of the proposed algorithm on Moving Peaks Benchmark (MPB) which is currently the most well-known benchmark for evaluating algorithm in dynamic environments. The experimental results indicate that the proposed algorithm statistically performs better than several state-of-the-art algorithms in terms of offline-error.

关键词： firefly algorithm multi-swarm dynamic optimization speciation dynamic and uncertain environments

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Improved firefly algorithm with courtship learning for unrelated parallel machine scheduling problem with sequence-dependent setup times

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2022年第1期11卷 1-17页

作者： Huang, Xingwang Chen, Lingqing Zhang, Yuxin Su, Shubin Lin, Yangbin Cao, Xuhui Jimei Univ Coll Comp Engn 185 Yinjiang Rd Xiamen 361021 Peoples R China Jimei Univ Spatial Intelligence Lab 185 Yinjiang Rd Xiamen 361021 Peoples R China

The Unrelated Parallel Machines Scheduling Problem (UPMSP) with sequence-dependent setup times has been widely applied to cloud computing, edge computing and so on. When the setup times are ignored, UPMSP will be a NP problem. Moreover, when considering the sequence related setup times, UPMSP is difficult to solve, and this situation will be more serious in the case of high-dimensional. This work firstly select the maximum completion time as the optimization objective, which establishes a mathematical model of UPMSP with sequence-dependent setup times. In addition, an improved firefly algorithm with courtship learning is proposed. Finally, in order to provide an approximate solution in an acceptable time, the proposed algorithm is applied to solve the UPMSP with sequence-dependent setup times. The experimental results show that the proposed algorithm has competitive performance when dealing with UPMSP with sequence-dependent setup times.

关键词： firefly algorithm Courtship learning Unrelated parallel machines scheduling Makespan Sequence-dependent setup times

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A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy

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SOFT COMPUTING 2017年第18期21卷 5295-5308页

作者： Osaba, Eneko Yang, Xin-She Diaz, Fernando Onieva, Enrique Masegosa, Antonio D. Perallos, Asier Univ Deusto Deusto Inst Technol DeustoTech Av Univ 24 Bilbao 48007 Spain Middlesex Univ Sch Sci & Technol Hendon Campus London NW4 4BT England

A real-world newspaper distribution problem with recycling policy is tackled in this work. To meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries, variable costs and forbidden paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the literature. For this reason, a benchmark composed by 15 instances has been also proposed. In the design of this benchmark, real geographical positions have been used, located in the province of Bizkaia, Spain. For the proper treatment of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed. This application is the first application of the firefly algorithm to any rich vehicle routing problem. To prove that the proposed DFA is a promising technique, its performance has been compared with two other well-known techniques: an evolutionary algorithm and an evolutionary simulated annealing. Our results have shown that the DFA has outperformed these two classic meta-heuristics.

关键词： Rich vehicle routing problem firefly algorithm Newspaper delivery Combinatorial optimization Routing problems

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Enhanced salp swarm algorithm based on firefly algorithm for unrelated parallel machine scheduling with setup times

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APPLIED MATHEMATICAL MODELLING 2021年 94卷 285-305页

作者： Ewees, Ahmed A. Al-qaness, Mohammed A. A. Abd Elaziz, Mohamed Univ Bisha Dept E Syst Bisha 61922 Saudi Arabia Damietta Univ Dept Comp Dumyat 34511 Egypt Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt

Unrelated parallel machine scheduling problem (UPMSP) with sequence-dependent setup times has received more attention due to its various industrial and scheduling applications. However, the UPMSP is considered an NP-hard problem, even without setup times. Moreover, the sequence-dependent setup times presents more complexity, which makes finding an optimal solution is very hard. In this paper, a modified salp swarm algorithm (SSA) based on the firefly algorithm (FA) is proposed to enhance the quality of the solution of UPMSP. The proposed approach, called SSAFA, uses the operators of FA to improve the exploitation ability of SSA by working as a local search. We evaluate the proposed SSAFA using both small and large problem instances. Furthermore, extensive comparisons to several existing metaheuristic methods used to solve UPMSP problems have been carried out. The evaluation outcomes confirmed the competitive performance of the proposed SSAFA in all problem instances, using different performance measures. ? 2021 Elsevier Inc. All rights reserved.

关键词： Meta-heuristic algorithms Salp swarm algorithm firefly algorithm Unrelated parallel machine scheduling problem (UPMSP)

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Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso

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AGRICULTURAL WATER MANAGEMENT 2018年 208卷 140-151页

作者： Tao, Hai Diop, Lamine Bodian, Ansoumana Djaman, Koffi Ndiaye, Papa Malick Yaseen, Zaher Mundher Baoji Univ Arts & Sci Dept Comp Sci Baoji Shaanxi Peoples R China Univ Gaston Berger UFR Sci Agron Aquaculture & Technol Alimentaires BP 234 St Louis Senegal Univ Gaston Berger Lab Leidi Dynam Terr & Dev BP 234 St Louis Senegal New Mexico State Univ Dept Plant & Environm Sci Agr Sci Ctr Farmington POB 1018 Farmington NM 87499 USA Ton Duc Thang Univ Sustainable Dev Civil Engn Res Grp Fac Civil Engn Ho Chi Minh City Vietnam

Reference Evapotranspiration (ETo) is one of the major components of the hydrological cycle that is very essential in water resources planning, irrigation and drainage management and several other hydrology processes. In irrigation system and design, the prediction of ETo is vital and indispensable for the quantification of crop water needs. This study investigates the capabilities of hybridized fuzzy model with firefly algorithm (ANFIS-FA) for predicting daily reference evapotranspiration over Burkina Faso region. Metrological information at Bobo Dioulasso, Bur Dedougou, and Ouahigouya stations, in Sahelian, Sudano-Sahelian, and Sudanian zone, are used for modelling development. Six different climatic input variable combinations corresponding to 6 models are inspected. The daily Penman-Monteith reference evapotranspiration for the time-period (1998-2012) are used to train and test the models. Several numerical indicators in addition to Taylor diagram are considered to evaluate the performance of the models. Results indicated that the hybrid ANFIS-FA model outperformed the classical ANFIS-based model for all three stations and the model with full inputs climatic data gave the best results. Furthermore, ANFIS-FA is performed the best for Bur Dedougou (Sahalian-Soudanian region) and less at Ouahigouya (sahalian region). In quantitative terms and for instance Bur Dedougou station, ANFIS-FA model increased the prediction accuracy remarkably (SI = 0.043, R-2 = 0.97, MAPE = 0.035 and RMSE = 0.24) compared with ANFIS-based model (SI = 0.068, R-2 = 0.89, MAPE = 0.037 and RMSE = 0.378). Results revealed the influence of utilizing nature-inspired firefly algorithm to substantially improve performance of the classical ANFIS model optimization for the applied application. Also, the applied modelling strategy can bring a trustful expert intelligent system for predicting reference evapotranspiration at the west desert of Africa.

关键词： Reference evapotranspiration prediction firefly algorithm Hybrid models Agriculture management Burkina Faso region

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