Hybridizing Invasive Weed Optimization with Firefly Algorithm for Multi-Robot Motion Planning

Panda, Mohit Ranjan; Dutta, Shubham; Pradhan, Saroj Kumar

doi:10.1007/s13369-017-2794-6

Cited by 30 publications

(14 citation statements)

References 20 publications

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“…31 and 32 . Average travelled trajectory path deviation (ATTPD) (Das et al 2016 ; Panda et al 2018 ; Bakdi 2017 ) for each movement is computed with respect to actual path ( ) and computed path ( ) using the following equation. …”

Section: Results Analysismentioning

confidence: 99%

Prevention of Covid-19 affected patient using multi robot cooperation and Q-learning approach: a solution

et al. 2021

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Section: Results Analysismentioning

confidence: 99%

Prevention of Covid-19 affected patient using multi robot cooperation and Q-learning approach: a solution

et al. 2021

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“…The FF algorithm is advantageous in dealing with non-linear and multi-model optimization problems [37]. Nonetheless, the FF algorithm encounters a problem of getting trapped in the local minima [38]. On the other hand, the PSO algorithm is well known due to its simplicity and ease of the implementation mechanism in the optimization area [39].…”

Section: Proposed Hybridization Of Hfpsomentioning

confidence: 99%

“…Thus, one of the ways to minimize this problem is to hybridize these two algorithms. In this work, the FF and PSO algorithms are combined to prevent the premature convergence of every algorithm, avoid trapping at local optima, and to balance the exploitation process and exploration process [38]. In Figure 6, the flowchart of the proposed HFPSO algorithm which is used by this work is shown.…”

Section: Proposed Hybridization Of Hfpsomentioning

confidence: 99%

Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)

et al. 2019

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Solar power generation deals with uncertainty and intermittency issues that lead to some difficulties in controlling the whole grid system due to imbalanced power production and power demand. The forecasting of solar power is an effort in securing the integration of renewable energy into the grid. This work proposes a forecasting model called WT-ANFIS-HFPSO which combines the wavelet transform (WT), adaptive neuro-fuzzy inference system (ANFIS) and hybrid firefly and particle swarm optimization algorithm (HFPSO). In the proposed work, the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data. The HFPSO is the hybridization of the firefly (FF) and particle swarm optimization (PSO) algorithm, which is employed in optimizing the premise parameters of the ANFIS to increase the accuracy of the model. The results obtained from WT-ANFIS-HFPSO are then compared with several other forecasting strategies. From the comparative analysis, the WT-ANFIS-HFPSO showed superior performance in terms of statistical error analysis, confirming its reliability as an excellent forecaster of hourly solar power data.

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“…Here, only the energy conservation problem is addressed and fails to consider the end‐to‐end delay. The firefly algorithm has shortcoming by acquiring trapped in the local minima as it is based upon the light intensity for attraction and also suffered by premature convergence 14,15 …”

Section: Introductionmentioning

confidence: 99%

Energy efficient and delay aware clustering in mobile adhoc network: A hybrid fruit fly optimization algorithm and whale optimization algorithm approach

Saminathan¹,

Renukadevi²

2022

Concurrency and Computation

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The energy efficient and delay are the two important optimization issues in the mobile adhoc network (MANET), where the nodes move randomly at any direction with limited battery life, resulting in occasional change of network topology. In this article, a hybrid fruit fly optimization algorithm and whale optimization algorithm (FOA-WOA) is proposed for energy efficient with delay aware cluster head (CH) selection. The major objective of the proposed method is "to solve the problems of energy efficient with delay and develop a clustering mechanism". The performance of the hybrid FOA-WOA is evaluated based on packet delivery ratio (PDR), delay, energy consumption, and throughput. Moreover, the proposed method is analyzed with two existing algorithms, like ant colony optimization (ACO) and genetic algorithm (GA). The experimental results show that the proposed method attains 11.6% better than ACO and 1.8% better than GA based on packet delivery ratio, 57.6% better than ACO and 27.3% better than GA based on delay and 15.3% better than ACO and 36.4% better than GA based on energy consumption.

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Hybridizing Invasive Weed Optimization with Firefly Algorithm for Multi-Robot Motion Planning

Cited by 30 publications

References 20 publications

Prevention of Covid-19 affected patient using multi robot cooperation and Q-learning approach: a solution

Prevention of Covid-19 affected patient using multi robot cooperation and Q-learning approach: a solution

Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)

Energy efficient and delay aware clustering in mobile adhoc network: A hybrid fruit fly optimization algorithm and whale optimization algorithm approach

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