scholarly journals Delay Bound Optimization in NoC Using a Discrete Firefly Algorithm

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1507
Author(s):  
Gaoming Du ◽  
Chao Tian ◽  
Zhenmin Li ◽  
Duoli Zhang ◽  
Chuan Zhang ◽  
...  
Keyword(s):  
Firefly Algorithm ◽  
Optimization Method ◽  
Video Object ◽  
Worst Case ◽  
Delay Bound ◽  
Proposed Model ◽  
Discrete Firefly Algorithm ◽  
Firefly Optimization ◽  
On Chip ◽  
Bound Optimization

The delay bound in system on chips (SoC) represents the worst-case traverse time of on-chip communication. In network on chip (NoC)-based SoC, optimizing the delay bound is challenging due to two aspects: (1) the delay bound is hard to obtain by traditional methods such as simulation; (2) the delay bound changes with the different application mappings. In this paper, we propose a delay bound optimization method using discrete firefly optimization algorithms (DBFA). First, we present a formal analytical delay bound model based on network calculus for both unipath and multipath routing in NoCs. We then set every flow in the application as the target flow and calculate the delay bound using the proposed model. Finally, we adopt firefly algorithm (FA) as the optimization method for minimizing the delay bound. We used industry patterns (video object plane decoder (VOPD), multiwindow display (MWD), etc.) to verify the effectiveness of delay bound optimization method. Experiments show that the proposed method is both effective and reliable, with a maximum optimization of 42.86%.

FIRELY OPTIMIZATION METHOD FOR SOLVING COMBINATORIAL LOGIC PROB- LEMS ON GRAPHS

2021 ◽  
Vol 115 ◽  
pp. 14-19
Author(s):  
Vladimir Kureichik ◽  
◽  
Victoria Bova ◽  
Vladimir Kureichik Jr. ◽  
◽  
...  
Keyword(s):  
Time Complexity ◽  
Optimization Method ◽  
Worst Case ◽  
Local Optima ◽  
Search Area ◽  
Firefly Optimization

The article describes the solution of NP-complex combinatorial-logical problems on graphs. The authors pro-pose to use a bioinspired approach based on a modified firefly optimization method. As a modification of the proposed approach, the work introduces procedures for dynamically changing the search area, which allows avoiding local optima. A series of tests and experiments have shown that this method is promising. The time complexity of the developed algorithm in the best case ≈ O(nlogn), in the worst case - О(n2).

Improved PID rate control of a quadrotor with a convexity-based surrogated model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aziz Kaba
Keyword(s):  
Monte Carlo ◽  
Rate Control ◽  
Firefly Algorithm ◽  
Convex Combination ◽  
Optimization Method ◽  
Hybrid Structure ◽  
Content Type ◽  
Error Metrics ◽  
Aerial Vehicle ◽  
Firefly Optimization

Purpose The purpose of this paper is to develop, extend and propose an improved proportional integral derivative (PID) rate control of a quadrotor unmanned aerial vehicle based on a convexity-based surrogated firefly algorithm. Design/methodology/approach An improved PID controller structure is proposed for the rate dynamics of the quadrotor. Optimality of the controller is ensured by a recent, simple yet efficient firefly optimization method. The hybrid structure is further enhanced with a convexity-based surrogated model function. Findings Monte Carlo, transient response, error metrics and histogram distribution analyzes are conducted to show the performance of the proposed controller. The performance of the proposed method is evaluated under various convex combination values to further investigate the effect of the proposed surrogated model. According to the results, the proposed method is capable of controlling the rate quadrotor dynamics with the steady-state error of 0.0023 (rad/s) for P, −0.0024 (rad/s) for Q and 0 (rad/s) for the R state, respectively. Also, the least mean objective value is achieved at = 0 value of convexity in Monte Carlo trials. Originality/value The originality of this paper is to propose an improved PID rate controller with a convexity-based surrogated firefly algorithm.

A SVD and Modified Firefly Optimization Based Robust Digital Image Watermarking Technique

2017 ◽  
Vol 7 (7) ◽  
pp. 268
Author(s):  
Chauhan Usha ◽  
Singh Rajeev Kumar
Keyword(s):  
Digital Media ◽  
Firefly Algorithm ◽  
Image Watermarking ◽  
Singular Values ◽  
Robust Watermarking ◽  
Scaling Factors ◽  
Geometric Attacks ◽  
Host Image ◽  
Firefly Optimization ◽  
Value Decomposition

Digital Watermarking is a technology, to facilitate the authentication, copyright protection and Security of digital media. The objective of developing a robust watermarking technique is to incorporate the maximum possible robustness without compromising with the transparency. Singular Value Decomposition (SVD) using Firefly Algorithm provides this objective of an optimal robust watermarking technique. Multiple scaling factors are used to embed the watermark image into the host by multiplying these scaling factors with the Singular Values (SV) of the host image. Firefly Algorithm is used to optimize the modified host image to achieve the highest possible robustness and transparency. This approach can significantly increase the quality of watermarked image and provide more robustness to the embedded watermark against various attacks such as noise, geometric attacks, filtering attacks etc.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition for corporate bankruptcy prediction

2020 ◽  
pp. 1-17
Author(s):  
Dongqi Yang ◽  
Wenyu Zhang ◽  
Xin Wu ◽  
Jose H. Ablanedo-Rosas ◽  
Lingxiao Yang ◽  
...  
Keyword(s):  
Fuzzy Clustering ◽  
Rapid Development ◽  
Optimization Method ◽  
Selection Method ◽  
Bankruptcy Prediction ◽  
Ensemble Model ◽  
Composition Method ◽  
Classifier Selection ◽  
Multi Stage ◽  
Proposed Model

With the rapid development of commercial credit mechanisms, credit funds have become fundamental in promoting the development of manufacturing corporations. However, large-scale, imbalanced credit application information poses a challenge to accurate bankruptcy predictions. A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition is proposed herein by combining the fuzzy clustering-based classifier selection method, the random subspace (RS)-based classifier composition method, and the genetic algorithm (GA)-based classifier compositional optimization method to achieve accuracy in predicting bankruptcy among corporates. To overcome the inherent inflexibility of traditional hard clustering methods, a new fuzzy clustering-based classifier selection method is proposed based on the mini-batch k-means algorithm to obtain the best performing base classifiers for generating classifier compositions. The RS-based classifier composition method was applied to enhance the robustness of candidate classifier compositions by randomly selecting several subspaces in the original feature space. The GA-based classifier compositional optimization method was applied to optimize the parameters of the promising classifier composition through the iterative mechanism of the GA. Finally, six datasets collected from the real world were tested with four evaluation indicators to assess the performance of the proposed model. The experimental results showed that the proposed model outperformed the benchmark models with higher predictive accuracy and efficiency.

scholarly journals Mathematical Modeling and Discrete Firefly Algorithm to Optimize Scheduling Problem with Release Date, Sequence-Dependent Setup Time, and Periodic Maintenance

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Mustafa A. Qamhan ◽  
Ammar A. Qamhan ◽  
Ibrahim M. Al-Harkan ◽  
Yousef A. Alotaibi
Keyword(s):  
Firefly Algorithm ◽  
Programming Model ◽  
Setup Time ◽  
Mixed Integer ◽  
Release Date ◽  
Sequence Dependent ◽  
Sequence Dependent Setup Time ◽  
Periodic Maintenance ◽  
Discrete Firefly Algorithm ◽  
Sequence Dependent Setup

An evolutionary discrete firefly algorithm (EDFA) is presented herein to solve a real-world manufacturing system problem of scheduling a set of jobs on a single machine subject to nonzero release date, sequence-dependent setup time, and periodic maintenance with the objective of minimizing the maximum completion time “makespan.” To evaluate the performance of the proposed EDFA, a new mixed-integer linear programming model is also proposed for small-sized instances. Furthermore, the parameters of the EDFA are regulated using full factorial analysis. Finally, numerical experiments are performed to demonstrate the efficiency and capability of the EDFA in solving the abovementioned problem.

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