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A novel firefly algorithm based on gender difference and its convergence

APPLIED SOFT COMPUTING 2019年 80卷 107-124页

作者： Wang, Chun-Feng Song, Wen-Xin Henan Normal Univ Coll Math & Informat Sci Xinxiang 453007 Henan Peoples R China

firefly algorithm is a population-based algorithm, because the search process is simple and easy to implement, it has received extensive attention from scholars. However, the algorithm has low accuracy and is easy to fall into local optimum. Therefore, an improved firefly algorithm based on gender difference (GDFA) is proposed to enhance the performance of the algorithm in this paper. In the basic firefly algorithm, fireflies have no gender distinction, which is not consistent with reality. According to this point, fireflies are divided into two subgroups based on gender. In addition, different update equations are designed for two subgroups on the basis of their moving ways. Male fireflies are guided by two randomly selected female fireflies to focus on global search and female fireflies move to the best male firefly to implement local search, which aim to balance the exploration and exploitation of the algorithm. To further improve the search accuracy, the chaos search is conducted near the best position of the population. Several simulation experiments and constrained optimization problems are carried out to verify the effectiveness of GDFA, and the time complexity of GDFA is analyzed. Finally, the convergence analysis of proposed algorithm is given based on Markov chain theory. (C) 2019 Elsevier B.V. All rights reserved.

关键词： firefly algorithm Gender difference Markov chain theory Optimization

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Data mining-based firefly algorithm for green vehicle routing problem with heterogeneous fleet and refueling constraint

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ARTIFICIAL INTELLIGENCE REVIEW 2023年第7期56卷 6557-6589页

作者： Behnamian, J. Ghadimi, M. Farajiamiri, M. Ali Sina Univ Dept Ind Engn Hamadan Iran Rhein Westfal TH Aachen Dept Operat Management Aachen Germany

One of the major reasons for the importance of green vehicle routing is that current distribution systems are not sustainable in the long run. Because currently, only conventional economic costs are considered and environmental impacts of logistics strategies are not considered to be an important issue. Green vehicle routing emphasizes the use of clean fuels as an important factor in reducing the environmental pollution. However, due to the shortage of fuel stations providing these types of fuels, careful planning is needed considering the location and time of refueling of vehicles. In this regard, here, it is assumed that vehicles have limited fuel and should go to refueling stations and so fuel stations have also been added to the nodes of the classic vehicle routing problem. In this paper, a mathematical formulation is presented for a green heterogeneous vehicle routing problem and a firefly algorithm is developed to solve it in large-size instances. Two approaches have been used in applying the firefly algorithm. The first approach is the basic firefly algorithm and the second one is the firefly algorithm based on data mining in which the decision tree method reduces solution space and makes smarter fireflies movement. Finally, we have shown that using data mining has a significant effect on improving the performance of the firefly algorithm and by using a decision tree, the efficiency of the algorithm is significantly improved.

关键词： Green vehicle routing problem Heterogeneous fleet of vehicles Fueling station firefly algorithm Data mining

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A nature-inspired firefly algorithm based approach for nanoscale leakage optimal RTL structure

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INTEGRATION-THE VLSI JOURNAL 2015年 51卷 46-60页

作者： Kougianos, Elias Mohanty, Saraju P. Univ N Texas NanoSyst Design Lab NSDL Denton TX 76207 USA Univ N Texas Dept Elect Engn Technol Denton TX 76207 USA Univ N Texas Dept Comp Sci & Engn Denton TX 76207 USA

Optimization of leakage power is essential for nanoscale CMOS (nano-CMOS) technology based integrated circuits for numerous reasons, including improving battery life of the system in which they are used as well as enhancing reliability. Leakage optimization at an early stage of the design cycle such as the register-transfer level (RTL) or architectural level provides more degrees of freedom to design engineers and ensures that the design is optimized at higher levels before proceeding to the next and more detailed phases of the design cycle. In this paper, an RTL optimization approach is presented that targets leakage-power optimization while performing simultaneous scheduling, allocation and binding. The optimization approach uses a nature-inspired firefly algorithm so that large digital integrated circuits can be effectively handled without convergence issues. The firefly algorithm optimizes the cost of leakage delay product (LDP) under various resource constraints. As a specific example, gate-oxide leakage is optimized using a 45 nm CMOS dual-oxide based pre-characterized datapath library. Experimental results over various architectural level benchmark integrated circuits show that average leakage optimization of 90% can be obtained. For a comparative perspective, an integer linear programming (ILP) based algorithm is also presented and it is observed that the firefly algorithm is as accurate as ILP while converging much faster. To the best of the authors' knowledge, this is the first ever paper that applies firefly based algorithms for RTL optimization. (C) 2015 Elsevier B.V. All rights reserved.

关键词： firefly algorithm Nano-CMOS Leakage power Gate leakage Low power synthesis Scheduling Binding RTL optimization Integer-linear programming

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Optimal sizing of off-grid hybrid energy system based on minimum cost of energy and reliability criteria using firefly algorithm

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SOLAR ENERGY 2019年 188卷 655-666页

作者： Sanajaoba, Sarangthem MSPCL Imphal 795001 Manipur India

Solar and wind energy based hybrid energy system (HES) have received considerable global attention for remote applications where extension of grid supply is infeasible. This paper attempts to develop an optimal hybrid energy system model using available solar and wind energy resources with battery storage for fulfilling the electrical needs of three un-electrified remote villages located in Senapati district of Manipur, India. A firefly algorithm based approach is used to find the optimal hybrid system configuration based on minimum cost of energy (COE) while satisfying a specified reliability criteria identified with loss of load probability (LOLP) reliability index. Further, system investigation has been carried out for optimal system feasibility at various values of LOLP. The optimal hybrid energy system is able to provide feasible solutions for LOLP values lying between 0 and 0.03. For LOLP value above 0.03, the system becomes infeasible as the COE increase with increase in LOLP.

关键词： Hybrid energy system firefly algorithm Cost of energy Loss of load probability, reliability

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Sensitivity of filter bank SCFDMA to carrier frequency offset and its compensation using firefly algorithm

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2018年第14期31卷 e3743-e3743页

作者： Deepak, P. M. Ali, C. K. Natl Inst Technol Calicut Dept Elect & Commun Engn Kozhikode Kerala India

This paper proposes an efficient wireless uplink strategy named filter bank single carrier frequency division multiple access. The proposed system combines the low peak-to-average power ratio of precoded orthogonal frequency division multiple access with the reduced out-of-band emission of filter bank multicarrier scheme. We present a complete mathematical analysis of the proposed model and derive the conditions to be fulfilled by the transmit filter for perfect data recovery. We further introduce a transmit filter design to enhance the peak-to-average power ratio and out-of-band emission characteristics of the proposed system. This paper also analyzes the sensitivity of the filter bank single carrier frequency division multiple access system to carrier frequency offset (CFO) and suggests a low complexity interference compensation scheme, under the assumption of perfect knowledge of CFO at the receiver. Later on, the assumption on CFO parameter is removed, and a blind interference compensation technique based on firefly algorithm is developed. Finally, a detailed simulation study is performed to substantiate the effectiveness of the proposed methods.

关键词： carrier frequency offset filter bank multicarrier firefly algorithm out-of-band emission peak-to-average power ratio precoded OFDMA

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A novel opposition based improved firefly algorithm for multilevel image segmentation

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第11期81卷 15521-15544页

作者： Sharma, Abhay Chaturvedi, Rekha Bhargava, Anuja MIT ADT Univ MIT SOE Pune Maharashtra India GLA Univ Mathura India

The data explosion caused by the Internet and its applications has given researchers immense scope for data analysis. A large amount of data is available in form of images. Image processing is required for better understandability of an image. Various image processing steps are available for improving the image in different application areas. Various applications like medical imaging, face recognition, biometric security, and traffic surveillance, etc. depend only on image and its analysis. This analysis in several applications is highly dependent on the outcome of image segmentation. This paper focuses on good segmentation through multi-level thresholding. In this research, the algorithm includes two modules related to Entropy and variance. The first module is concerned with the modified firefly algorithm (FA) with Kapur's, Tsallis, and Fuzzy Entropy. FA is used to optimize fuzzy parameters for obtaining optimal thresholds. The second module is derived from the principle of variance between two classes known as between variance or inter-cluster variance. The opposition-based the learning method is used for initializing the population of candidate solutions and levying flight and local search is implemented with FA. The various experiments have been performed on Berkeley and benchmark images with distinct threshold (i.e. 2, 3, 4, 5) values. The proposed algorithm has been estimated and compared with known metaheuristic optimization methods like particle swarm optimization (PSO) and electromagnetism optimization (EMO). The results have been assessed quantitatively and qualitatively by using parameters like Peak signal-to-noise ratio (PSNR), structured similarity index metric (SSIM), objective function values, and convergence curve. The algorithm proposed observed better experiment results than PSO, EMO in terms of persistency and quality.

关键词： Entropy Variance Segmentation Thresholding firefly algorithm Optimization

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A hybrid optimizer based on firefly algorithm and particle swarm optimization algorithm

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JOURNAL OF COMPUTATIONAL SCIENCE 2018年第May期26卷 488-500页

作者： Xia, Xuewen Gui, Ling He, Guoliang Xie, Chengwang Wei, Bo Xing, Ying Wu, Ruifeng Tang, Yichao East China Jiaotong Univ Sch Software Nanchang 330013 Jiangxi Peoples R China East China Jiaotong Univ Intelligent Optimizat & Informat Proc Lab Nanchang 330013 Jiangxi Peoples R China East China Jiaotong Univ Sch Econ & Management Nanchang 330013 Jiangxi Peoples R China Wuhan Univ Sch Comp Wuhan 430079 Hubei Peoples R China

As two widely used evolutionary algorithms, particle swarm optimization (PSO) and firefly algorithm (FA) have been successfully applied to diverse difficult applications. And extensive experiments verify their own merits and characteristics. To efficiently utilize different advantages of PSO and FA, three novel operators are proposed in a hybrid optimizer based on the two algorithms, named as FAPSO in this paper. Firstly, the population of FAPSO is divided into two sub-populations selecting FA and PSO as their basic algorithm to carry out the optimization process, respectively. To exchange the information of the two sub-populations and then efficiently utilize the merits of PSO and FA, the sub-populations share their own optimal solutions while they have stagnated more than a predefined threshold. Secondly, each dimension of the search space is divided into many small-sized sub-regions, based on which much historical knowledge is recorded to help the current best solution to carry out a detecting operator. The purposeful detecting operator enables the population to find a more promising sub-region, and then jumps out of a possible local optimum. Lastly, a classical local search strategy, i.e., BEGS Quasi-Newton method, is introduced to improve the exploitative capability of FAPSO. Extensive simulations upon different functions demonstrate that FAPSO is not only outperforms the two basic algorithm, i.e., FA and PSO, but also surpasses some state-of-the-art variants of FA and PSO, as well as two hybrid algorithms. (C) 2017 Elsevier B.V. All rights reserved.

关键词： firefly algorithm Particle swarm optimization Knowledge-based detecting Local search operator

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Array pattern optimization for a steerable circular isotropic antenna array using the firefly algorithm

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2017年第3期16卷 952-976页

作者： Banerjee, Sudipta Mandal, Durbadal NIT Durgapur Dept ECE Durgapur India

In this paper, a steerable isotropic circular array antenna is designed to reduce the side lobe level (SLL), using an evolutionary optimization technique. Particle swarm optimization and the firefly algorithm are used to reduce the SLL, as well as to steer the main beam in a specific direction. In this steerable circular array design, the amplitude excitations are optimized. The results show that the maximum SLL peak of the resultant patterns is as per requirements. Good performance is achieved in the array factor response and suppression of SLL for different numbers of array elements with different main beam steering angles.

关键词： Circular array antenna Isotropic element Array pattern Beam steering Side lobe level Evolutionary optimization Particle swarm optimization firefly algorithm

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Normalization and outlier removal in class center-based firefly algorithm for missing value imputation

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JOURNAL OF BIG DATA 2021年第1期8卷 1-18页

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

A missing value is one of the factors that often cause incomplete data in almost all studies, even those that are well-designed and controlled. It can also decrease a study's statistical power or result in inaccurate estimations and conclusions. Hence, data normalization and missing value handling are considered the major problems in the data pre-processing stage, while classification algorithms are adopted to handle numerical features. In cases where the observed data contained outliers, the missing value estimated results are sometimes unreliable or even differ greatly from the true values. Therefore, this study aims to propose the combination of normalization and outlier removals before imputing missing values on the class center-based firefly algorithm method (ON + C3FA). Moreover, some standard imputation techniques like mean, a random value, regression, as well as multiple imputation, KNN imputation, and decision tree (DT)-based missing value imputation were utilized as a comparison of the proposed method. Experimental results on the sonar dataset showed normalization and outlier removals effect in the methods. According to the proposed method (ON + C3FA), AUC, accuracy, F1-Score, Precision, Recall, and AUC-PR had 0.972, 0.906, 0.906, 0.908, 0.906, 0.61 respectively. The result showed combining normalization and outlier removals in C3-FA (ON + C3FA) was an efficient technique for obtaining actual data in handling missing values, and it also outperformed the previous studies methods with r and RMSE values of 0.935 and 0.02. Meanwhile, the D-ks value obtained from this technique was 0.04, which indicated that it could maintain the values or distribution accuracy.

关键词： Missing value Normalization Outliers Method Imputation Class center firefly algorithm

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Sensor Placement Optimization in Structural Health Monitoring Using Cluster-in-Cluster firefly algorithm

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ADVANCES IN STRUCTURAL ENGINEERING 2014年第8期17卷 1103-1115页

作者： Zhou, Guang-Dong Yi, Ting-Hua Li, Hong-Nan Hohai Univ Coll Civil & Transportat Engn Nanjing Jiangsu Peoples R China Dalian Univ Technol Sch Civil Engn Dalian Peoples R China Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China

The determination of the optimal sensor placement (OSP) is a significant task that must be completed before a structural health monitoring (SHM) system is implemented on a real structure. The firefly algorithm (FA) is a recently developed nature-inspired metaheuristic algorithm for continuous optimization problems. This paper proposes a cluster-in-cluster firefly algorithm (CiCFA) for the optimum SHM sensor deployment. First, the code defining position coordinates in basic FA is replaced with a one-dimensional binary coding system, and the Euclidean distance is replaced by the Hamming distance. Then, a movement scheme for a darker firefly approaching a lighter firefly is developed. Last, a cluster-in-cluster strategy is employed to improve the convergence speed. In addition, a self-adaptive dynamic penalty function is introduced to convert the constraint imposed by the limited wireless data transmission range in the optimal wireless sensor placement (OWSP) problem to an unconstrained optimization problem. To demonstrate the effectiveness and applicability of the proposed optimization method, numerical examples of wired sensor placement and wireless sensor placement are carried out. The results reveal that the CiCFA provides a better sensor configuration than the genetic algorithm with high efficiency and high stability and can be applied in the OWSP problem. The proposed CiCFA provides a promising alternative for solving the OSP problem in practical SHM systems.

关键词： structural health monitoring optimal sensor placement firefly algorithm wireless sensor network long-span bridge

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