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Object-based feature extraction for hyperspectral data using firefly algorithm

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2020年第6期11卷 1277-1291页

作者： Shahdoosti, Hamid Reza Tabatabaei, Zahra Hamedan Univ Technol Dept Elect Engn Hamadan 65155 Hamadan Iran

Object-based classification methods can improve the accuracy of hyperspectral image classification due to the fact that they incorporate spatial information into the classification procedure by assigning neighboring pixels into the same class. In this paper, a new object-based feature extraction method is proposed which makes use of information theory to reduce the Bayes error. In this way, the proposed method exploits higher order statistics for feature extraction which are very effective for non Gaussian data such as hyperspectral images. The criterion to be minimized is composed of three mutual information terms. The first and second terms, consider the maximal relevance and minimal redundancy, respectively, while the third term takes into account the segmentation map containing disjoint spatial regions. To obtain the segmentation map, we apply the firefly clustering algorithm whose fitness function simultaneously considers the intra-distance between samples and their cluster centroids, and inter-distance between centroids of any two clusters. Our experimental results, performed using a variety of hyperspectral scenes, indicate that the proposed framework gives better classification results than some state-of the-art spectral-spatial feature extraction methods.

关键词： Feature extraction firefly algorithm Mutual information Higher order statistics Object-based classification

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A dynamic adaptive firefly algorithm with globally orientation

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MATHEMATICS AND COMPUTERS IN SIMULATION 2020年 174卷 76-101页

作者： Liu, Jingsen Mao, Yinan Liu, Xiaozhen Li, Yu Henan Univ Inst Intelligent Networks Syst Kaifeng Peoples R China Henan Univ Coll Software Kaifeng Peoples R China Henan Univ Inst Management Sci & Engn Kaifeng Peoples R China

This paper proposes a dynamic adaptive firefly algorithm to overcome the disadvantages of the standard firefly algorithm, to improve the convergence rate and solution precision, and to avoid the premature algorithm trapping at the local extreme. It has a global-oriented moving mechanism and can dynamically adjust the step size and attractiveness. First, through the adaptive deviation degree strategy of optimal distance combining with the Gaussian distribution, it optimizes the fixed step-factor to balance the exploration and excavation capabilities of the algorithm and improves the diversity of the population. Second, minimum attractiveness is introduced to the algorithm, and is adaptively changed with the number of iterations, which can avoid random walk due to lack of traction between fireflies. Finally, this paper improves the mobility mechanism based on the position of the current optimal firefly. It enables firefly move with global orientation and also expands the sharing of information between fireflies to improve the overall evolutionary optimization performance of the algorithm. Theoretical analysis proves the convergence and time complexity of the improved algorithm. The simulation results of several test functions and engineering constraint optimization problems show that the improved algorithm has better solution performance, and clearly improves the convergence speed and solution accuracy. (C) 2020 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： firefly algorithm Adaptive step size Gaussian distribution Speed reducer Three-bar truss

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Optimization of electrical discharge machining parameters on hardened die steel using firefly algorithm

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ENGINEERING WITH COMPUTERS 2015年第1期31卷 1-9页

作者： Raja, S. Bharathi Pramod, C. V. Srinivas Krishna, K. Vamshee Ragunathan, Arvind Vinesh, Somalaraju SASTRA Univ Sch Mech Engn Thanjavur 613401 Tamil Nadu India

Electric discharge machining (EDM) is one of the most widely used die-making processes especially in aerospace, automobile and electronics industries. The profile manufactured by EDM process should be dimensionally and geometrically accurate apart from good finish. This expectation is very much important as the die manufactured from EDM process is subjected to subsequent mass production. The material normally selected for die making will be superior in quality and hence time and cost of production will also be high. Selection of optimum EDM parameters may reduce the machining time along with maintaining required surface finish and dimensional accuracy. So there is a need to develop a technique for selecting the optimal EDM parameters to achieve the desired performance measures. In the present work, a recently developed firefly algorithm (FA) was implemented in the developed mathematical model based on the experiments conducted on an EDM. Investigations are also carried out to study the effect of EDM parameters such as current and pulse-on time on the surface roughness and machining time. The optimized machining parameters and the developed empirical relations are validated by confirmatory experiments. Machining parameters limits and desired surface finish are considered as practical constraints for both experimental and theoretical approaches. The predicted and actual machining time and surface roughness values reveals that FA is very much suitable for solving machining parameters optimization problems.

关键词： Electric discharge machining Machining parameters Machining time Surface roughness firefly algorithm

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Adaptive firefly algorithm with chaos for mechanical design optimization problems

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APPLIED SOFT COMPUTING 2015年 36卷 152-164页

作者： Baykasoglu, Adil Ozsoydan, Fehmi Burcin Dokuz Eylul Univ Fac Engn Dept Ind Engn TR-35397 Izmir Turkey

firefly algorithm (FA) is a newer member of bio-inspired meta-heuristics, which was originally proposed to find solutions to continuous optimization problems. Popularity of FA has increased recently due to its effectiveness in handling various optimization problems. To enhance the performance of the FA even further, an adaptive FA is proposed in this paper to solve mechanical design optimization problems, and the adaptivity is focused on the search mechanism and adaptive parameter settings. Moreover, chaotic maps are also embedded into AFA for performance improvement. It is shown through experimental tests that some of the best known results are improved by the proposed algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： firefly algorithm Chaotic maps Design optimization Non-linear programming

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Discrete Optimum Design of Truss Structures by an Improved firefly algorithm

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ADVANCES IN STRUCTURAL ENGINEERING 2014年第10期17卷 1517-1530页

作者： Baghlani, A. Makiabadi, M. H. Sarcheshmehpour, M. Shiraz Univ Technol Fac Civil & Environm Engn Shiraz Iran Sharif Univ Technol Fac Civil Engn Tehran Iran

This paper presents an improved firefly algorithm (FA) for fast optimization of truss structures with discrete variables. The enhanced accelerated firefly algorithm (AFA) is a simple, but very effective modification of FA. In order to investigate the performance and robustness of the proposed algorithm, some benchmark (structural optimization) problems are solved and the results are compared with FA and other algorithms. The results show that in some test cases, AFA not only finds lighter structures compared to other algorithms, but also converges faster. In the rest test cases, the optimal solutions are found with very less computational effort. The study also shows that the proposed AFA remarkably improves stability of the firefly algorithm in discrete design of truss structures.

关键词： truss structures firefly algorithm discrete variables size optimization

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

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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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Data Aggregation in Wireless Sensor Networks Using firefly algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2019年第1期104卷 307-324页

作者： Mosavvar, Islam Ghaffari, Ali Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran

The challenging issue of data aggregation in wireless sensor networks (WSNs) is of high significance for reducing network overhead and traffic. The majority of transmitted data by sensor nodes is repetitious and doing processes on them in many cases leads to increased power consumption and reduced network lifetime. Hence, sensor nodes should use such a pattern for data transmission which minimizes duplicate data. However, in cluster based WSN, cluster heads (CHs) consume more energy due to aggregating the data from cluster member nodes and transmitting the aggregated data to the sink. Therefore, the proper selection of CHs plays vital role for prolonging the lifetime of WSNs. In WSNs, cluster head selection is an optimization problem which is NP-hard. In this paper, using firefly algorithm, we proposed a method for aggregating data in WSNs. In the proposed method, sensor nodes are divided into several areas by using clustering. In each cluster, nodes are periodically active and inactive. Criteria such as energy and distance are taken into consideration for selecting active nodes. In this way, nodes with more remaining energy and more distance will be selected as active nodes. Simulation results, conducted in MATLAB 2016a, revealed that the proposed method was able to enhance quality of service parameters more than low energy adaptive clustering hierarchy and shuffled frog algorithm methods.

关键词： WSNs Power consumption Data aggregation firefly algorithm Clustering NP-hard

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Optimization of modular granular neural networks using a firefly algorithm for human recognition

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 64卷 172-186页

作者： Sanchez, Daniela Melin, Patricia Castillo, Oscar Tijuana Inst Technol Tijuana Mexico

In this paper a new optimization method for modular neural network (MNN) design using granular computing and a firefly algorithm is proposed. This method is tested with human recognition based on benchmark ear and face databases to verify the effectiveness and the advantages of the proposed method. Nowadays, there are a great number of optimization techniques, but it is very important to find an appropriate one that allows for better results depending on the area of application. For this reason, a comparison of techniques is presented in this paper, where the results achieved for ear recognition and face recognition by the proposed method are compared against a hierarchical genetic algorithm in order to know which of these techniques provides better results when a modular granular neural network is optimized and applied to pattern recognition mainly for human recognition. The parameters of modular neural networks that are being optimized are: the number of modules (or sub granules), percentage of data for the training phase, learning algorithm, goal error, number of hidden layers and their number of neurons. Simulation results show that the proposed approach combining the firefly algorithm with granular computing provides very good results in optimal design of MNNs. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Modular neural networks Granular computing firefly algorithm Human recognition Ear recognition Face recognition Pattern recognition

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Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion

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NEURAL COMPUTING & APPLICATIONS 2017年第9期28卷 2795-2808页

作者： Zhang, Long Shan, Linlin Wang, Jianhua Harbin Normal Univ Coll Comp Sci & Informat Engn Harbin 150025 Heilongjiang Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China Heilongjiang Univ Sch Art Harbin 150080 Heilongjiang Peoples R China

In this paper, we investigate feature subset selection problem by a new self-adaptive firefly algorithm (FA), which is denoted as DbFAFS. In classical FA, it uses constant control parameters to solve different problems, which results in the premature of FA and the fireflies to be trapped in local regions without potential ability to explore new search space. To conquer the drawbacks of FA, we introduce two novel parameter selection strategies involving the dynamical regulation of the light absorption coefficient and the randomization control parameter. Additionally, as an important issue of feature subset selection problem, the objective function has a great effect on the selection of features. In this paper, we propose a criterion based on mutual information, and the criterion can not only measure the correlation between two features selected by a firefly but also determine the emendation of features among the achieved feature subset. The proposed approach is compared with differential evolution, genetic algorithm, and two versions of particle swarm optimization algorithm on several benchmark datasets. The results demonstrate that the proposed DbFAFS is efficient and competitive in both classification accuracy and computational performance.

关键词： Feature selection firefly algorithm Mutual information Adaptive parameter

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Discrete firefly algorithm for Clustered Multi-Temperature Joint Distribution with Fuzzy Travel Times

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2018年第1期11卷 195-205页

作者： Lu, Sichao Wang, Xifu Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

This study proposes a mathematical model of clustered multi-temperature joint distribution in fuzzy environment. In this model, a Z-shaped function is used to depict customer satisfaction. For the imprecise model, triangular fuzzy numbers are used to represent travel times. By redefining the movement procedure of fireflies, two versions of discrete firefly algorithms are developed, which differ in the population initialization strategy. Finally, experiments are carried out and computational results are reported to confirm the effectiveness of the proposed algorithms.

关键词： MTJD distribution Z-shaped firefly algorithm Perishable food

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