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Deploying charging nodes in wireless rechargeable sensor networks based on improved firefly algorithm

COMPUTERS & ELECTRICAL ENGINEERING 2018年 72卷 719-731页

作者： Yang, Meng Wang, Aimin Sun, Geng Zhang, Ying Jilin Univ Coll Comp Sci & Technol Changchun 130012 Jilin Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Jilin Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

A wireless rechargeable sensor network (WRSN) consists of sensor nodes that can harvest energy from the wireless charging nodes (WCNs) for prolonging the network lifetime. This study deals with the WCN deployment optimization problem in WRSNs. We propose an optimization framework that simultaneously maximizes the coverage and the charging efficiency. Moreover, an improved firefly algorithm (IFA) is proposed for solving the WCN deployment optimization problem. IFA adopts a novel adaptive attractiveness factor and introduces a dynamic location update mechanism to enhance the performance of the conventional firefly algorithm (FA). We compare the proposed IFA with several benchmark algorithms in two different scenarios. Simulation results show that the proposed algorithm outperforms other comparative algorithms in both accuracy and convergence rate. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Wireless rechargeable sensor networks Wireless charger deployment Coverage Charging efficiency firefly algorithm

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Opposition and dimensional based modified firefly algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 44卷 168-176页

作者： Verma, Om Prakash Aggarwal, Deepti Patodi, Tejna Delhi Technol Univ Dept Informat Technol Delhi India

This paper presents the modified firefly algorithm (FA) originally proposed by Yang. firefly algorithm is based on the idealized behavior of the flashing characteristics of the fireflies. Though firefly is powerful in local search, it does not search well globally due to being trapped in local optimum. Due to this reason, the convergence is generally slow. The FA also doesn't give efficient solution in high dimensional problems. The proposed approach gives more efficient solution with reduced time complexity in comparison to original FA. Two modifications made are: (I) Opposition-based methodology is deployed where initialization of candidate solutions is done using opposition based learning to improve convergence rate of original FA, which includes initializing the opposite number of positions of each firefly. This also ensures efficient searching of the whole search space, ( 2) The dimensional-based approach is employed in which the position of each firefly is updated along different dimensions. This results in more optimal solution. This algorithm works for High Dimensionality problems, especially in terms of accuracy in finding the best optimal solution and in terms of fast convergence speed as well. Several complex multidimensional standard functions are employed for experimental verification. Experimental results include comparison with other Evolutionary algorithms which show that the Opposition and Dimensional based FA (ODFA) gives more accurate optimal solution with high convergence speed than the original FA and those achieved by existing methods. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： firefly algorithm Fitness function Modified firefly algorithm Opposition based learning Optimization

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Application of Dynamically Search Space Squeezed Modified firefly algorithm to a Novel Short Term Economic Dispatch of Multi-Generation Systems

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IEEE ACCESS 2021年 9卷 1918-1939页

作者： Liaquat, Sheroze Fakhar, Muhammad Salman Kashif, Syed Abdul Rahman Rasool, Akhtar Saleem, Omer Zia, Muhammad Fahad Padmanaban, Sanjeevikumar Natl Univ Comp & Emerging Sci Dept Elect Engn Lahore 54000 Pakistan Univ Engn & Technol Dept Elect Engn Lahore 54000 Pakistan Sharif Coll Engn & Technol Dept Elect Engn Lahore 55150 Pakistan Aalborg Univ Dept Energy Technol DK-6700 Esbjerg Denmark

The absence of the global best component in the update equation of the conventional firefly algorithm degrades its exploration properties. This research proposes multi-update position criteria to enhance the exploration properties of the conventional firefly technique while including the effect of the global best solution on the movement of the fireflies in the search space of the objective function. Moreover, the dynamic search space squeezing is applied to constrict the movement of the fireflies within the certain limits to avoid their oscillatory movement as the solution approaches towards the global best by determining the optimal trajectory for each firefly. The robustness of the suggested firefly algorithm is tested on a hybrid energy system consisting of thermal, hydroelectric, and Photovoltaic (PV) energy source. The intermittent nature of the PV energy source is explained using fractional integral polynomial model and Auto Regressive Integrated Moving Average (ARIMA) model. The main dispatch problem is successfully computed using both the modified firefly and the simple firefly algorithm by determining the optimal power share of each energy source for different scheduling intervals. The suggested operational strategy reduces the overall generation cost of the system while preserving the various system constraints. Due to the stochastic nature of the meta-heuristic techniques, the two suggested algorithms are compared statistically for different test cases using the independent t-test results. The statistical comparison suggests that the performance of the modified firefly is superior to its conventional counterpart as the evaluation parameters of the modified firefly converge to relatively lower value as compared to the parameters of the simple firefly algorithm.

关键词： Modified firefly algorithm auto regressive integrated moving average model firefly algorithm hybrid energy systems independent t-test results

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A novel cluster head selection using Hybrid Artificial Bee Colony and firefly algorithm for network lifetime and stability in WSNs

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CONNECTION SCIENCE 2022年第1期34卷 387-408页

作者： Sengathir, J. Rajesh, A. Dhiman, Gaurav Vimal, S. Yogaraja, C. A. Viriyasitavat, Wattana CVR Coll Engn Dept IT Rangareddi Telangana India CVR Coll Engn Dept ECE Rangareddi Telangana India Govt Bikram Coll Commerce Dept Comp Sci Patiala Punjab India Ramco Inst Technol Dept Artificial Intelligence & Data Sci Rajapalayam India Chulalongkorn Univ Dept Stat Business Informat Technol Div Fac Commerce & Accountancy Bangkok Thailand

Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issue of network lifetime, coverage area, scheduling and data aggregation. In particular, prolonging network lifetime confirms the success together with the energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are considered to be highly suitable for effectively utilising the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. In this paper, a Hybrid Modified Artificial Bee Colony and firefly algorithm (HMABCFA) -Based Cluster Head Selection is proposed for ensuring energy stabilisation, delay minimisation and inter-node distance reduction for improving the network lifetime. This proposed HMABCFA integrates the benefit of the firefly optimisation algorithm for generating a new position that which has the capability of replacing the position, which is not updated in the scout bee phase of ABC. This incorporation of firefly optimisation algorithm into the ABC algorithm prevents the limitations of premature convergence, slow convergence and the possibility of being trapped into the local point of optimality in the clustering process. The modified ABC-based clustering process is phenomenal in improving the feasible dimensions for enhancing the process of exploitation and exploration. The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches.

关键词： Network lifetime energy stability Modified Artificial Bee Colony algorithm firefly algorithm cluster head selection

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Automatic EEG channel selection for multiclass brain-computer interface classification using multiobjective improved firefly algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第4期82卷 5405-5433页

作者： Tiwari, Anurag Chaturvedi, Amrita IIT BHU Dept Comp Sci & Engn Varanasi Uttar Pradesh India

Multichannel Electroencephalography-based Brain-Computer Interface (BCI) systems facilitate a communicating medium between the human brain and the outside world. BCI systems aim to translate human intentions into computer-based control commands by decoding the respective brain patterns. Moreover, Electroencephalography (EEG) analysis involves high-dimensional features that increase the computational burden of applied signal processing approaches. To minimize this overload caused by a large set of channels, we propose an automatic EEG channel selection method for multiclass Motor-Imagery (MI) classification. In this study, we developed a hybrid channel ranking procedure using Fisher information and the objective firefly algorithm (FA). Firstly, the preprocessed neural signals are used to extract spatial-temporal features using the Regularized Common Spatial Pattern with Aggregation (RCSPA) method. Then, objective FA with two input variables (Spectral Entropy and Lyapunov exponent) is used to compute a weighted score for each channel in the neighborhood of a candidate solution. Finally, a novel Channel Set Relevance Index (CSRI) is developed to rank channels using their respective weighted score and Fisher information. The RCSPA features of highly ranked channels are employed to discriminate different MI-tasks using the Regularized Support Vector Machine (RSVM) classifier. The proposed approach is cross-validated on three publicly available BCI competition datasets (BCI Competition IV- 2008 - IIA, BCI Competition IV- dataset 1, BCI competition III - dataset IVa) with varying numbers of channels. The validation results show that the proposed method achieved a superior classification accuracy (83.97% on dataset 1, 80.85% on dataset 2, and 84.19% on dataset 3) with fewer channels than other baseline methods. In addition, our method significantly reduced the BCI preparation time, making it effective to conduct multiple experimental sessions for a large pool of subjects.

关键词： Brain-computer Interface firefly algorithm Spectral entropy Lyapunov exponent Regularized common spatial pattern with aggregation Fisher information Regularized support vector machine

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Improved Slime Mould algorithm based on firefly algorithm for feature selection: A case study on QSAR model

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 3期38卷 2407-2421页

作者： Ewees, Ahmed A. Abualigah, Laith Yousri, Dalia Algamal, Zakariya Yahya Al-qaness, Mohammed A. A. Ibrahim, Rehab Ali Abd Elaziz, Mohamed Damietta Univ Dept Comp Dumyat 34517 Egypt Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Fayoum Univ Fac Engn Dept Elect Engn Al Fayyum Egypt Univ Mosul Dept Stat & Informat Mosul Iraq Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China Zagazig Univ Dept Math Fac Sci Zagazig 44519 Egypt Nahda Univ Fac Comp Sci Bani Suwayf 62521 Egypt Acad Sci Res & Technol ASRT Cairo Egypt

Feature selection (FS) methods are necessary to develop intelligent analysis tools that require data preprocessing and enhancing the performance of the machine learning algorithms. FS aims to maximize the classification accuracy by minimizing the number of selected features. This paper presents a new FS method using a modified Slime mould algorithm (SMA) based on the firefly algorithm (FA). In the developed SMAFA, FA is adopted to improve the exploration of SMA, since it has high ability to discover the feasible regions which have optima solution. This will lead to enhance the convergence by increasing the quality of the final output. SMAFA is evaluated using twenty UCI datasets and also with comprehensive comparisons to a number of the existing MH algorithms. To further assess the applicability of SMAFA, two high-dimensional datasets related to the QSAR modeling are used. Experimental results verified the promising performance of SMAFA using different performance measures.

关键词： Feature selection Slime mould algorithm firefly algorithm QSAR

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Multi-Objectives firefly algorithm for Task Offloading in the Edge-Fog-Cloud Computing

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IEEE ACCESS 2024年 12卷 159561-159578页

作者： Saif, Faten A. Latip, Rohaya Hanapi, Zurina Mohd Kamarudin, Shafinah Kumar, A. V. Senthil Bajaher, Awadh Salem Gulf Coll Dept Informat Technol Hafar Al Batin 39952 Saudi Arabia Univ Putra Malaysia UPM Fac Comp Sci & Informat Technol Dept Commun Technol & Network Serdang 43400 Malaysia Hindusthan Coll Arts & Sci Dept MCA Coimbatore 641028 India

This study proposes a two-stage strategy to ensure the successful execution of critical tasks. In the first stage, an Enhanced Task Offloading (ETO) algorithm is introduced to determine the appropriate computing layer-edge, fog, or cloud-for offloading incomplete tasks. The algorithm makes this decision by assessing the availability of idle computing resources relative to the task's computational requirements. Additionally, it verifies the status of the server (on/off) before offloading;if the server is unavailable, the algorithm proceeds to check the next layer. In the second stage, the strategy employs a Multi-objective firefly (MFA) algorithm to assign the optimal computational device within the selected layer. Experimental simulations compare the proposed strategy with a benchmark task offloading algorithm. The results demonstrate the superiority of the proposed strategy, achieving reductions in energy consumption and delay and maximizing resource utilization compared to the baseline algorithms.

关键词： Internet of Things Delays Cloud computing Energy consumption Scheduling Edge computing Optimization Heuristic algorithms Servers Quality of service Task offloading optimization firefly algorithm metaheuristic edge-fog-cloud computing energy consumption transmission delay Pareto

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A new discrete double-population firefly algorithm for assembly sequence planning

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2016年第12期230卷 2229-2238页

作者： Zhang, Zaifang Yuan, Baoxun Zhang, Zhinan Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Siemens China Co Ltd Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China

Assembly sequence planning is a critical step of assembly planning in product digital manufacturing. It is a combinational optimization problem with strong constraints. Many studies devoted to propose intelligent algorithms for efficiently finding a good assembly sequence to reduce the manufacturing time and cost. Considering the unfavorable effects of penalty function in the traditional algorithms, a new discrete firefly algorithm is proposed based on a double-population search mechanism for the assembly sequence planning problem. The mechanism can guarantee the population diversity and enhance the local and global search capabilities by using the parallel evolution of feasible and infeasible solutions. All parts composed of the assembly are assigned as the firefly positions, and the corresponding movement direction and distance of each firefly are defined using vector operations. Three common objectives, including assembly stability, assembly polymerization and change number of assembly direction, are taken into account in the fitness function. The proposed approach is successfully applied in a real-world assembly sequence planning case. The sizes of feasible and infeasible populations are adequately discussed and compared, of which the optimal size combination is used for initializing the firefly algorithm. The application results validate the feasibility and effectiveness of the discrete double-population firefly algorithm for solving assembly sequence planning problem.

关键词： Assembly sequence planning double-population search firefly algorithm assembly direction assembly stability

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A novel multilevel color image segmentation technique based on an improved firefly algorithm and energy curve

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EVOLVING SYSTEMS 2023年第4期14卷 685-733页

作者： Guo, Qiuping Peng, Hao Yunnan Normal Univ Pan Asia Business Sch Kunming 650092 Yunnan Peoples R China

Color image multilevel thresholding segmentation based on metaheuristic optimization algorithm and histogram has been widely used in many fields, but histogram neglects the relationships between the neighborhood pixels and any correlation among gray levels of the image when selecting the optimum threshold values. Therefore, in this paper, a novel and efficient color image multilevel thresholding method is proposed which uses an energy function to generate the energy curve of an image by considering spatial contextual information of the image. The presented color image segmentation technique based on energy curve takes the between class variance, Tsallis entropy and Kapur's entropy as objective functions. In order to further enhance the segmentation performance, an improved firefly algorithm (IFA) is presented in this paper. The IFA algorithm based on energy function is used for color image multilevel thresholding problem and compared with modified firefly algorithm (MFA), cuckoo search (CS), grasshopper optimization algorithm (GOA), Harris hawks optimization (HHO), emperor penguin optimization (EPO) algorithms. The experimental results are presented in terms of optimal threshold value, optimal objective function values, peak signal to noise (PSNR), structural similarity index (SSIM), standard deviation of the objective values and statistical results. The experimental results show that the presented method outperforms the other algorithms and Kapur's entropy based on energy curve is better than between class variance and Tsallis entropy for color image multilevel thresholding segmentation.

关键词： Image segmentation Color image multilevel thresholding Energy curve firefly algorithm

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A new method in multimodal optimization based on firefly algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2016年第2期46卷 267-287页

作者： Nekouie, Nadia Yaghoobi, Mahdi Islamic Azad Univ Mashhad Branch Dept Comp Engn Mashhad Iran

Optimization has been one of significant research fields in the past few decades, most of the real-world problems are multimodal optimization problems. The prime target of multimodal optimization is to find multiple global and local optima of a problem in one single run. The multimodal optimization problems have drawn attention to evolutionary algorithms. firefly algorithm is a recently proposed stochastic optimization technique. This algorithm is a global search algorithm. On the other hand, because this algorithm has multimodal characteristics, it has the capacity and capability to change into multimodal optimization method. The aim of this article is to show that firefly algorithm is able to find multiple solutions in multimodal problems. Therefore, in this study, a new technique, is introduced for multimodal optimization. In the proposed algorithm, the multimodal optima are detected through separately evolving sub-populations. A stability criterion is used to determine the stability and instability of the sub-population. If a sub-population is regarded as stable, it has an optima stored in an external memory called Archive. After some iterations, the archive includes all of the optimums. The proposed algorithm utilizes a simulated annealing local optimization algorithm to increase search power, accuracy and speed of the algorithm. The proposed algorithm is tested on a set of criterion functions. The results show that the proposed algorithm has a high ability to find the multimodal optimal points.

关键词： firefly algorithm Simulated annealing algorithm Multimodal optimization

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