Synergism of Firefly Algorithm and Q-Learning for Robot Arm Path Planning

Sadhu, Arup Kumar; Konar, Amit; Bhattacharjee, Tanuka; Das, Swagatam

doi:10.1016/j.swevo.2018.03.014

Cited by 75 publications

(30 citation statements)

References 16 publications

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“…An improvised FF that involves the Q‐learning, a model‐free reinforcement algorithm, which tells an agent what action to take under the specific circumstances, is proposed and the optimal parameter values for each FF of a population are learnt . Tighzert et al introduces a set of new compact FF algorithms with minimal computational costs to a legged robot with a specific application.…”

Section: Aco and Ffmentioning

confidence: 99%

“…An improvised FF that involves the Q-learning, a model-free reinforcement algorithm, which tells an agent what action to take under the specific circumstances, is proposed and the optimal parameter values for each FF of a population are learnt. 36 Tighzert et al 37 introduces a set of new compact FF algorithms with minimal computational costs to a legged robot with a specific application. A new three-dimensional path planning method for autonomous underwater vehicles using a modified FF algorithm is proposed in order to improve the speed of convergence in FF algorithm by adjusting the brightness and attraction parameters.…”

Section: Aco and Ffmentioning

confidence: 99%

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Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Gangadharan

Salgaonkar

2020

Engineering Reports

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Metaheuristic algorithms such as ant colony optimization (ACO) and firefly (FF) have been successfully employed to solve the optimization problems such as robot motion planning in dynamic environments. The systematic plantation of rubber trees on a rectangular grid motivated us to explore application of grid search algorithms. We compared the ACO and FF algorithms in various scenarios by changing simulation parameters like density of the environment, land size, number of robots simultaneously available, and hillock plantations. In all different scenarios, we evaluated the performance of ACO and FF in terms of path length and time of execution, we found that later is outperforming the former. Regression equations are framed to establish the contributions of different parameters. Statistical significance of the results has been in favor of this hypothesis. The shortest path on a plain land is the relatively simplest scenario, while the Hamiltonian on a concave surface is arguably the most difficult. The novelty of this work lies in the very idea of an autonomous robot for the rubber tapping and then path optimization by employing soft computing techniques. This proposal of rubber harvesting robot if implemented for latex collection, has a potential to drive the rubber farming and allied businesses to scale up the economy of the coastal areas of India, say for example, Kerala.

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Section: Aco and Ffmentioning

confidence: 99%

Section: Aco and Ffmentioning

confidence: 99%

Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Gangadharan

Salgaonkar

2020

Engineering Reports

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“…One can note that the proposed TEO metaheuristic has worthily attained the lowest average ranks compared to the remaining methods. A statistical analysis has been performed to highlight the importance of the TEO-based tuning method over other algorithms [32]. The Friedman test for six competitor algorithms (m = 6) and four indices (l = 4) provides the computed value χ 2 F = 12.14 of the χ-distribution.…”

Section: Statistical Analysis and Comparisonmentioning

confidence: 99%

“…Thus, the proposed TEO is found to be the most effective one having the best average Friedman ranking as given in Table 6. However, a Bonferroni-Dunn post-hoc test is called to investigate whether or not the proposed TEO algorithm is significantly better than another algorithm at the above considered level of confidence [32]. The corresponding Critical Differences (CD) of the reported algorithms at the confidence levels α = 0.05 (95% significance level) and α = 0.1 (90% significance level) are computed as CD 0.05 = 3.17 and CD 0.1 = 2.88, respectively.…”

Section: Statistical Analysis and Comparisonmentioning

confidence: 99%

“…The corresponding Critical Differences (CD) of the reported algorithms at the confidence levels α = 0.05 (95% significance level) and α = 0.1 (90% significance level) are computed as CD 0.05 = 3.17 and CD 0.1 = 2.88, respectively. Figure 16 illustrates the graphical representation of the Bonferroni-Dunn test considering the TEO as the control algorithm [32,33]. level of confidence.…”

Section: Statistical Analysis and Comparisonmentioning

confidence: 99%

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Direct Power Control Optimization for Doubly Fed Induction Generator Based Wind Turbine Systems

Alhato

2019

MCA

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This study presents an intelligent metaheuristics-based design procedure for the Proportional-Integral (PI) controllers tuning in the direct power control scheme for 1.5 MW Doubly Fed Induction Generator (DFIG) based Wind Turbine (WT) systems. The PI controllers’ gains tuning is formulated as a constrained optimization problem under nonlinear and non-smooth operational constraints. Such a formulated tuning problem is efficiently solved by means of the proposed Thermal Exchange Optimization (TEO) algorithm. To evaluate the effectiveness of the introduced TEO metaheuristic, an empirical comparison study with the homologous particle swarm optimization, genetic algorithm, harmony search algorithm, water cycle algorithm, and grasshopper optimization algorithm is achieved. The proposed TEO algorithm is ensured to perform several desired operational characteristics of DFIG for the active/reactive power and DC-link voltage simultaneously. This is performed by solving a multi‐objective function optimization problem through a weighted‐sum approach. The proposed control strategy is investigated in MATLAB/environment and the results proved the capabilities of the proposed control system in tracking and control under different scenarios. Moreover, a statistical analysis using non-parametric Friedman and Bonferroni–Dunn’s tests demonstrates that the TEO algorithm gives very competitive results in solving global optimization problems in comparison to the other reported metaheuristic algorithms.

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A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks

Nayak¹,

Naik

Pelusi

et al. 2019

Nature-Inspired Computation in Data Mining and Machine Learning

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Synergism of Firefly Algorithm and Q-Learning for Robot Arm Path Planning

Cited by 75 publications

References 16 publications

Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Ant colony optimization and firefly algorithms for robotic motion planning in dynamic environments

Direct Power Control Optimization for Doubly Fed Induction Generator Based Wind Turbine Systems

A Comprehensive Review and Performance Analysis of Firefly Algorithm for Artificial Neural Networks

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