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Optimization of the Return Loss of Differentially Fed Microstrip Patch Antenna Using ANN and firefly algorithm

WIRELESS PERSONAL COMMUNICATIONS 2015年第4期80卷 1547-1556页

作者： Kaur, Rupinder Rattan, Munish Guru Nanak Dev Engn Coll Dept Elect & Commun Engn Ludhiana Punjab India

Themicrostrip patch antenna that have more than two feed points or lines is known as differential fed microstrip patch antenna. In this paper, firefly algorithm (FA) and artificial neural network (ANN) has been applied to a 'Flower' shaped differentially fed microstrip patch antenna for optimizing the return loss. This new optimization method is much faster than conventional optimization methods. FA is the new nature-inspired algorithm which is based on the flashing behavior of fireflies in the summer sky in the hot and humid regions. To validate the ability of FA, the results obtained from FA are compared with that obtained using genetic algorithm (GA) and ANN, and it has been observed that FA performs better as compared to GA.

关键词： Microstrip patch antenna firefly algorithm Genetic algorithm Neural networks Objective function Optimization Return loss

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A large-scale multi-objective firefly algorithm based on reward and punishment mechanisms and adaptive dimensional reorganization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第5期27卷 5933-5951页

作者： Pan, Ningkang Lv, Li Xiao, Renbin Wang, Hui Cui, Zhihua Nanchang Inst Technol Sch Informat Engn Nanchang Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan Peoples R China

Multi-objective optimization problems with large-scale variables are the focus and difficulty of current research in the field of multi-objective evolutionary algorithms. In order to cope with the problem of "dimensional catastrophe", this paper proposes a large-scale multi-objective firefly algorithm based on reward and punishment mechanisms and adaptive dimensional reorganization. First, a reward and punishment mechanisms is proposed to make full use of the information of the solution itself, so that the population can break the constraint and rapidly approach the Pareto frontier, effectively improving the convergence of the population;then, an adaptive dimensional reorganization mechanism is proposed to select individuals with large differences according to different iteration periods to interact and learn from each other, effectively improving the diversity of the population. In the experimental part, to verify the effectiveness of the algorithm, the UF series and the variable expanded ZDT series, 15 test problems are selected for simulation experiments and compared with 8 advanced large-scale optimization algorithms, the results show that L-MOFA-RA has the advantages of fast convergence speed and high convergence accuracy, and the comprehensive experimental results show that compared with the comparison algorithm L-MOFA-RA has better optimization performance. The comprehensive experimental results show that L-MOFA-RA has better optimization performance compared to the comparison algorithm.

关键词： firefly algorithm Large-scale multi-objective optimization Cauchy mutation Reward and punishment mechanisms Dimensional reorganization

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A return-cost-based binary firefly algorithm for feature selection

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INFORMATION SCIENCES 2017年 418卷 561-574页

作者： Zhang, Yong Song, Xian-fang Gong, Dun-wei China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Qingdao Univ Sci & Technol Sch Informat Sci & Technol Qingdao 266061 Shandong Peoples R China

Various real-world applications can be formulated as feature selection problems, which have been known to be NP-hard. In this paper, we propose an effective feature selection method based on firefly algorithm (FFA), called return-cost-based binary FFA (Rc-BBFA). The proposed method has the capability of preventing premature convergence and is particularly efficient attributed to the following three aspects. An indicator based on the return-cost is first defined to measure a firefly's attractiveness from other fireflies. Then, a Pareto dominance-based strategy is presented to seek the attractive one for each firefly. Finally, a binary movement operator based on the return-cost attractiveness and the adaptive jump is developed to update the position of a firefly. The experimental results on a series of public datasets show that the proposed method is competitive in comparison with other feature selection algorithms, including the traditional algorithms, the GA-based algorithm, the PSO-based algorithm, and the FFA-based algorithms. (C) 2017 Published by Elsevier Inc.

关键词： firefly algorithm Feature selection Return-cost Pareto dominance Binary movement

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firefly algorithm with Various Randomization Parameters: An Analysis

Firefly Algorithm with Various Randomization Parameters: An ...

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4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Raja, Nadaradjane Sri Madhava Manic, K. Suresh Rajinikanth, V. St Josephs Coll Engn Dept Elect & Instrumentat Madras 600119 Tamil Nadu India Taylors Univ Sch Engn Subang Selangor Malaysia

ISBN: (纸本)9783319037530;9783319037523

In recent years, metaheuristic algorithms are widely employed to provide optimal solutions for engineering optimization problems. In this work, a recent metaheuristic firefly algorithm (FA) is adopted to find optimal solution for a class of global benchmark problems and a PID controller design problem. Until now, few research works have been commenced with FA. The updated position in a firefly algorithm mainly depends on parameters such as attraction between fireflies due to luminance and randomization operator. In this paper, FA is analyzed with various randomization search strategies such as Levy Flight (LF) and Brownian Distribution (BD). The proposed method is also compared with the other randomization operator existing in the literature. The performance assessment between LF and BD based FA are carried using prevailing parameters such as search time and accuracy in optimal parameters. The result evident that BD based FA provides better optimization accuracy, whereas LF based FA provides faster convergence.

关键词： firefly algorithm Levy Flight Brownian Distribution Global benchmark problem Performance measure

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firefly algorithm and Grey Wolf Optimizer for Constrained Real-Parameter Optimization

Firefly Algorithm and Grey Wolf Optimizer for Constrained Re...

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Joint World Congress of the International-Fuzzy-Systems-Assoc (IFSA) and the Annual Conference of the North-American-Fuzzy-Information-Proc-Soc (NAFIPS) / 12th International Workshop on Constraint Programming and Decision Making (CoProd)

作者： Rodriguez, Luis Castillo, Oscar Garcia, Mario Soria, Jose Tijuana Inst Technol Tijuana BC Canada

ISBN: (纸本)9783030219208;9783030219192

The main goal of this paper is to present the performance of two popular algorithms, the first is the firefly algorithm (FA) and the second one is the Grey Wolf Optimizer (GWO) algorithm for complex problems. In this case the problems that we are presenting are of the CEC 2017 Competition on Constrained Real-Parameter Optimization in order to realize a brief analysis, study and comparison between the FA and GWO algorithms respectively.

关键词： Grey Wolf Optimizer firefly algorithm Constraints Complex problems Study Optimization

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firefly algorithm for Beamforming Design in RIS-aided Communication Systems 97

Firefly Algorithm for Beamforming Design in RIS-aided Commun...

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97th IEEE Vehicular Technology Conference (VTC-Spring)

作者： Le, Tuan Anh Yang, Xin-She Middlesex Univ Fac Sci & Technol London NW4 4BT England

ISBN: (纸本)9798350311143

This paper studies a non-convex power minimization problem for reconfigurable-intelligent-surfaces-aided communication systems whose constraints are multivariate functions of two independent optimization variables, i.e., active and passive beamforming vectors. A widely adopted alternative optimization (AO) approach approximates the originally non-convex problem by two convex sub-optimization problems where each suboptimization problem deals with one variable considering the other variable as a constant. The solution for the original problem is obtained by iteratively solving these sub-optimization problems. Although the AO approach converts the original NP-hard optimization problem to two convex sub-problems, the solutions attained by this method may not be the global optimal solution due to the approximation process as well as the inherent non-convexity of the original problem. To overcome the issue, this paper adopts a nature-inspired optimization approach and introduces a novel firefly algorithm (FA) to simultaneously solve for two independent optimization variables of the originally nonconvex optimization problem. Computational complexity analyses are provided for the proposed FA and the AO approaches. Simulation results reveal that the proposed FA approach prevails its AO counterpart in obtaining a better solution for the under-studied optimization problem with the same order of computational complexity.

关键词： firefly algorithm nature inspired optimization transmit beamforming reconfigurable intelligent surfaces

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Group influence based improved firefly algorithm for Design Space Exploration of Datapath resource allocation

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APPLIED INTELLIGENCE 2019年第6期49卷 2084-2100页

作者： Rajmohan, Shathanaa Natarajan, Ramasubramanian Natl Inst Technol Comp Sci & Engn Tiruchirappalli Tamil Nadu India

firefly algorithm which is a recent addition to the evolutionary algorithms, has shown good performance for many multi-objective optimization problems. In this paper, we propose a novel firefly algorithm for Design Space Exploration of Datapath resource allocation. The Datapath resource allocation problem is NP-Complete and the design space has vast number of design points. To explore the design space in feasible time, the problem is solved using an improved firefly algorithm. In particular, meeting the constraints presented by different parameters of interest is evaluated as cost based fitness and then solved. The proposed approach modifies firefly algorithm on four fronts: 1. A new strategy called Group-Influence based attraction, is used for updating fireflies during evolution;2. To generate diverse and quality initial population, Opposition Based Learning is incorporated to population initialization;3. In addition to exploration, in order to refine exploitation, firefly algorithm is hybridized with Tabu search;4. Tabu search is updated with Levy flights for finding nearby solutions. The proposed algorithm is compared with other meta-heuristic algorithms with respect to Quality-of-Results and exploration time. Experimental results show that the proposed algorithm outperforms other existing algorithms for standard benchmark instances.

关键词： firefly algorithm Group-influence Datapath resource allocation Design space exploration

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Stochastic firefly algorithm enabled fast charging of solar hybrid electric vehicles

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AIN SHAMS ENGINEERING JOURNAL 2021年第1期12卷 529-539页

作者： Goswami, Anik Sadhu, Pradip Kumar Indian Sch Mines Indian Inst Technol Dept Elect Engn Dhanbad Bihar India

Urbanization and population growth has led to an increase in the per unit energy consumption. As majority of the energy is derived from fossil fuel based sources, they have created adverse effects on the environment. Efforts to safeguard the environment have led to the development of hybrid electric vehicles. Proliferation of EV in the markets depends on factors like price, battery technology, economy and development of fast charging stations. Fast charging stations are an important factor in EV market penetration. This paper focusses in designing fast charging solar hybrid EV charging station. To decrease dependence on the grid power and to increase profitability, the charging station includes a 100 kW solar power plant with storage. The paper presents a comprehensive model of the hybrid solar charging station. To determine the load on the station, a stochastic model is used to predict the arrival time, the SOC and the charging demand. Stochastic firefly algorithm (SFA) is used for MPPT control to obtain maximum power from the solar power plant and to ensure fast charging of the station batteries. The station is designed to supply power from the batteries during off-peak hours and from the grid during high peak hours. The paper also presents a multi-objective planning using SFA to minimize the investment cost and to increase the profit of the charging station. Results show that using SFA enables fast charging of the batteries and also increases the profit of the charging station. Higher profit will encourage utilities to invest in charging station and hence this will help in higher penetration of EVs in the market. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-ncnd/4.0/).

关键词： Electric vehicles Fast charging station MPPT firefly algorithm Photovoltaic system

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Kernel machines and firefly algorithm based dynamic modulus prediction model for asphalt mixes considering aggregate morphology

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CONSTRUCTION AND BUILDING MATERIALS 2018年 159卷 408-416页

作者： Singh, Dharamveer Maheshwari, Saurabh Zaman, Musharraf Commuri, Sesh Indian Inst Technol Civil Engn Dept Mumbai 400076 Maharashtra India Univ Oklahoma Norman OK 73019 USA Univ Nevada Dept Elect & Biomed Engn Reno NV 89557 USA

Artificial Intelligence algorithm support vector regression (SVR) has proved successful in outperforming conventional Witczak and ANN models for estimation of dynamic modulus (E*) of asphalt mixes. However, there were two issues related to the development of E* prediction models that the present study addresses. Firstly, since aggregates occupy almost 95% by weight of HMA, it is quite possible that the morphology of these aggregates play an important role in influencing the E* values. To address this issue, aggregate shape parameters, namely, angularity, sphericity, texture and form were used with aggregate gradation for stiffness estimation. Secondly, to fine tune the hyper-parameters firefly algorithm (FA) was coupled with SVR. E* tests of 20 HMA mixes having different sources, sizes of aggregates, and volumetric properties were conducted at 4 temperatures and 6 frequencies. Aggregate shape parameters were measured using the automated aggregate image measurement system (AIMS). SVR-FA models were developed that predicted the E* with an R-2 of 0.98. SVR-FA models were compared with SVR and ANN models for E* prediction. Further, a sensitivity analysis was conducted to identify the important input parameters. Lastly, an approach for formulation of SVR-FA model equations for direct prediction of HMA stiffness is also discussed. FA proved instrumental in improving the efficiency of SVR by optimizing the hyper-parameters with lesser manual effort. Finally, it was concluded that SVR-FA algorithm is capable of successfully predicting the E* values using the aggregate shape parameters. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Dynamic modulus Support vector regression firefly algorithm Cumulative shape index factor

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Forecasting annual foreign tourist arrivals to China by incorporating firefly algorithm into fractional non-homogenous discrete Grey model

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KYBERNETES 2022年第2期51卷 676-693页

作者： Tang, Xiaozhong Xie, Naiming Hu, Aqin Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing Peoples R China Huangshan Vocat & Tech Coll Dept Ind & Finance Huangshan Peoples R China

Purpose Accurate foreign tourist arrivals forecasting can help public and private sectors to formulate scientific tourism planning and improve the allocation efficiency of tourism resources. This paper aims to address the problem of low prediction accuracy of Chinese inbound tourism demand caused by the lack of valid historical data. Design/methodology/approach A novel hybrid Chinese inbound tourism demand forecasting model combining fractional non-homogenous discrete grey model and firefly algorithm is constructed. In the proposed model, all adjustable parameters of the fractional non-homogenous discrete grey model are optimized simultaneously by the firefly algorithm. Findings The data sets of annual foreign tourist arrivals to China are used to verify the validity of the proposed model. Experimental results show that the proposed method is effective and can be used as a useful predictor for the prediction of Chinese inbound tourism demand. Originality/value The method proposed in this paper is effective and can be used as a feasible approach for forecasting the development trend of Chinese inbound tourism.

关键词： China Forecast Tourism demand Fractional non-homogenous discrete grey model firefly algorithm

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