A new firefly algorithm with mean condition partial attraction

Xu, Ge Ning; Zhang, Tingwei; Lai, Qiang

doi:10.1007/s10489-021-02642-6

Cited by 16 publications

(7 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 20 benchmark functions used to test algorithm performance in this paper are shown in Table 1, 36,37 including unimodal functions (F1-F7), multimodal functions (F8-F14) and hybrid functions (F15-F20). And experimented in different dimensions (D=10 and D=30).…”

Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

Zhang

Song

et al. 2022

Advances in Mechanical Engineering

View full text Add to dashboard Cite

To advance the calculation performance of the battle royale optimization algorithm (BRO), a hybrid improved BRO algorithm (HBC) is proposed in this paper. The level mechanism of the chicken swarm optimization algorithm (CSO) is integrated into the BRO algorithm to divide all into elite players and ordinary players, and the level relationship of different players is established. Then, an elite player update method of random exploring and directional update in a small range is proposed to improve the development ability. The update method of ordinary players improved, the update mechanism of elite random guidance is introduced to make full use of the excellent location information in the population. The performance verification experiment of the HBC algorithm is carried out on 20 benchmark functions and a practical project. Comparing with several other algorithms, the computational performance of the HBC algorithm is the best. Furthermore, the HBC algorithm is applied to solve the inverse kinematics of the 7R 6DOF robot. The experimental results show that the HBC algorithm effectively improves the average convergence accuracy and reduces the running time, compared with the BRO algorithm. This fully shows that the HBC algorithm is more competitive in stability, calculation accuracy, and speed.

show abstract

Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

Zhang

Song

et al. 2022

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…Notwithstanding the outstanding performance of original FA for many benchmarks [38] and practical challenges [39], findings of previous studies suggest that the basic FA shows some deficiencies in terms of insufficient exploration and inadequate intensificationdiversification balance [40][41][42]. The lack of diversification is particularly emphasized in early iterations, when, in some runs, the algorithm is not able to converge to optimal search space regions, and ultimately worse mean values are obtained.…”

Section: Motivation For Improvementsmentioning

confidence: 98%

“…where t and t + 1 denote current and next iteration, respectively, while the T is the maximum iteration number in one run of an algorithm. It is also worth noting that the previous studies show that FA exploitation abilities are efficient in tackling various kinds of tasks, and FA is known as metaheuristic, with robust exploitation capabilities [40][41][42].…”

Section: Motivation For Improvementsmentioning

confidence: 99%

Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization

et al. 2021

View full text Add to dashboard Cite

Swarm intelligence techniques have been created to respond to theoretical and practical global optimization problems. This paper puts forward an enhanced version of the firefly algorithm that corrects the acknowledged drawbacks of the original method, by an explicit exploration mechanism and a chaotic local search strategy. The resulting augmented approach was theoretically tested on two sets of bound-constrained benchmark functions from the CEC suites and practically validated for automatically selecting the optimal dropout rate for the regularization of deep neural networks. Despite their successful applications in a wide spectrum of different fields, one important problem that deep learning algorithms face is overfitting. The traditional way of preventing overfitting is to apply regularization; the first option in this sense is the choice of an adequate value for the dropout parameter. In order to demonstrate its ability in finding an optimal dropout rate, the boosted version of the firefly algorithm has been validated for the deep learning subfield of convolutional neural networks, with respect to five standard benchmark datasets for image processing: MNIST, Fashion-MNIST, Semeion, USPS and CIFAR-10. The performance of the proposed approach in both types of experiments was compared with other recent state-of-the-art methods. To prove that there are significant improvements in results, statistical tests were conducted. Based on the experimental data, it can be concluded that the proposed algorithm clearly outperforms other approaches.

show abstract

“…The intensity of light or brightness is found using the objective problem function of the firefly's attractiveness β. (2) Modified Firefly Algorithm decreases the randomness to reduce the probability [44] [45]. Test time is minimized by exchanging and compressing tests in one system.…”

Section: Literature Reviewmentioning

confidence: 99%

Test Scheduling and Test Time Minimization of System-on-Chip Using Modified BAT Algorithm

et al. 2022

View full text Add to dashboard Cite

System-on-Chip (SoC) is a structure in which semiconductor components are integrated into a single die. As a result, testing time should be reduced to achieve a low cost for each chip. Effective test scheduling can reduce the SoC testing time, which is more challenging due to its complexity. In this paper, the modified BAT algorithm-based test scheduling is proposed. Testing is carried out on the SoC ITC'02 benchmark circuits. The Modified Bat method is a recently heuristic algorithm that performs global optimization by imitating bat echolocation. Compared to other state-of-the-art algorithms, the Modified BAT Optimization method reduces testing time on SoCs. This paper improves the algorithm's exploration process by adjusting the equation for bat loudness (A0) and pulse emission rate (r). The modified BAT algorithm converges to the optimal solution faster. It has been used in 14 international standard test functions. The test results indicate that the modified BAT algorithm has a fast convergence speed, which minimizes the testing time compared to other evolutionary algorithms on the ITC'02 SoC benchmark circuits.

show abstract

A new firefly algorithm with mean condition partial attraction

Cited by 16 publications

References 39 publications

A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

A hybrid improved BRO algorithm and its application in inverse kinematics of 7R 6DOF robot

Performance of a Novel Chaotic Firefly Algorithm with Enhanced Exploration for Tackling Global Optimization Problems: Application for Dropout Regularization

Test Scheduling and Test Time Minimization of System-on-Chip Using Modified BAT Algorithm

Contact Info

Product

Resources

About