Hybrid Particle Swarm Optimization-Firefly algorithm (HPSOFF) for combinatorial optimization of non-slicing VLSI floorplanning

Sivaranjani, P.; Kumar, Ashavani

doi:10.3233/jifs-152551

Cited by 15 publications

(5 citation statements)

References 24 publications

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“…Reference [21] used Particle Swarm Optimization (PSO) algorithm, FA algorithm was introduced in [22], and Artificial Bee Colony (ABC) algorithm was used in [23]; thus we compare the above algorithms and the parameters of each optimized algorithms are based on above references. To validate the effectiveness of the given IFA, the comparative convergence results and computation time under mixed load condition at 18h are compared, and the results are shown in Figures 11(a) It can be known from Figure 11, compared with PSO, ABC, and FA algorithms, that the main advantages of the IFA are the efficiency of multiobjective optimization, because it uses mainly real random numbers and it is based on the global communication among the swarming particles (the fireflies); when the iterations are around 50, the IFA method owns the minimum network loss (less than 200kW) and voltage deviation (about 0.04), while other methods need to take about 100 iterations, which means that the best results can be obtained by the proposed control method with the fewer iterations.…”

Section: Verification Of Control Methods and Comparisonmentioning

confidence: 99%

“…Equations (20) to (22) indicate that the firefly parameters , , and play significant roles in the optimization process. If these parameters can be randomly changed in the feasible region, the global optimization ability could be improved.…”

Section: Improved Firefly Algorithm Optimizationmentioning

confidence: 99%

“…At each iterative step, the light intensity and the attractiveness of each firefly are calculated. LI of each firefly is compared with light intensity of all other fireflies and the positions of the fireflies are updated using (22). The firefly with the largest LI is considered as the global optimal after completing an adequate number of iterations.…”

Section: Optimal Power Factor Regulation Of Dispersed Windmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Power Factor Regulation of Dispersed Wind Farms under Diverse Load and Stochastic Wind Conditions Based on Improved Firefly Algorithm

Liu

Wang

Cui

et al. 2018

Mathematical Problems in Engineering

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Aiming at the instability of voltage and large network loss of dispersed wind farms (DWFs) integration into distribution network (DN), an optimal power factor regulation method based on improved firefly algorithm is proposed. Firstly, the generalized load model based on the static voltage characteristics is analyzed. Then reactive power capabilities of DWFs are thoroughly discussed and the influences of DWFs with variable power factor on network loss and voltage profile are presented. In order to reduce network loss and enhance power quality, optimal power factor regulation of DWFs based on improved firefly algorithm (IFA) is proposed. Finally, results of the benchmark IEEE-33 node system show the feasibilities and validities of the proposed method and the superiorities of proposed IFA are revealed by comparison with the existing algorithms.

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Section: Verification Of Control Methods and Comparisonmentioning

confidence: 99%

Section: Improved Firefly Algorithm Optimizationmentioning

confidence: 99%

Section: Optimal Power Factor Regulation Of Dispersed Windmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Power Factor Regulation of Dispersed Wind Farms under Diverse Load and Stochastic Wind Conditions Based on Improved Firefly Algorithm

Liu

Wang

Cui

et al. 2018

Mathematical Problems in Engineering

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“…FA has been studied and implemented to solve various optimization problems in engineering and science. The fault detection in robots [15], economic emission dispatched problem [16], reliability-redundancy optimization [17], mixed variable structural optimization problem [18], cooperative networking problem [19], combinatorial optimization problem [20], learning from demonstration problem [21], and the dynamic environment problem [22] are a few of them. The FA has shown great performance and created a good impact in the category of the population-based algorithm.…”

Section: Review Of Literaturementioning

confidence: 99%

Self-Directed Mobile Robot Navigation Based on Functional Firefly Algorithm (FFA)

Patle,

Patel,

Jha

et al. 2023

Eng

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This paper proposes an optimized mobile robot navigation strategy using a functional firefly algorithm (FFA) and choice function. This approach has two key advantages: first, the linear objective function performs efficiently with the single degree and finite-order polynomial time operation, and second, the cartesian constraint performs compactly with the chosen degree of freedom on the finite interval. This functional approach optimizes the size of operational parameters in context with key size, operation time, and a finite range of verification. The choice function achieves parameter order (size) reduction. The attraction characteristic of fireflies is represented by the choice function for optimizing the choice between low and high intensities of fireflies. In 2D and 3D environments, the proposed robot navigation performs well in an uncertain environment with static and dynamic obstacles. This efficiency includes the robot’s speed as determined by the choice function’s minimum path lengths. The collision-free path is achieved by the non-void family of non-void sets. The obtained results are optimal in terms of path length and navigational time. The proposed controller is also compared with the other existing controllers, and it is observed that the FFA gives the shortest path in less time for the same environmental condition.

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“…It is a good idea that make up for FA with PSO in terms of performance. Thus, Sivaranjani and Kumar [28] proposed hybrid particle swarm optimization-firefly algorithm (HPSOFA) to solve combinatorial optimization of non-slicing VLSI floorplanning. In FA, except the darker firefly, the brighter firefly almost does not move, only the brightest one move at random.…”

Section: Introductionmentioning

confidence: 99%

An Improved Firefly Algorithm With Specific Probability and Its Engineering Application

Wang¹,

Chu²

2019

IEEE Access

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Firefly algorithm (FA) belongs to the swarm intelligence algorithm, which is famous for its strong exploration, a small number of parameter settings and effortless operation. However, there are some drawbacks in the searching process for FA, as the poor accuracy of the solution, high-computational time complexity, and doughty oscillation. These phenomenons are attributed to two factors: 1) in classical FA, the firefly, which is gloomier than others can be attracted by any one of them and 2) FA cannot fully utilize the information of objective function and its fitness. In this paper, to overcome these shortcomings, based on specific probability p fit , a new modified firefly algorithm (pFA) is proposed. In this algorithm, for speeding up the convergence, the specific probability p fit determined by the value of fitness of the firefly is used to choose a neighbor among the better fireflies compared with the predefined firefly, which helps the predefined firefly to move toward a better direction. If there is no neighbor, the opposite learning strategy is employed to lead the firefly to move. The performance of pFA is tested on some well-known benchmark functions. The findings of the test show that pFA is outperformed to FA and some other state-of-the-art algorithms. Finally, we apply pFA to solve four engineering applications.

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Hybrid Particle Swarm Optimization-Firefly algorithm (HPSOFF) for combinatorial optimization of non-slicing VLSI floorplanning

Cited by 15 publications

References 24 publications

Optimal Power Factor Regulation of Dispersed Wind Farms under Diverse Load and Stochastic Wind Conditions Based on Improved Firefly Algorithm

Optimal Power Factor Regulation of Dispersed Wind Farms under Diverse Load and Stochastic Wind Conditions Based on Improved Firefly Algorithm

Self-Directed Mobile Robot Navigation Based on Functional Firefly Algorithm (FFA)

An Improved Firefly Algorithm With Specific Probability and Its Engineering Application

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