Optimal Performance and Application for Firework Algorithm Using a Novel Chaotic Approach

Luo, Weilin; Jin, Haoran; Li, Hao; Fang, Xi; Zhou, Rui

doi:10.1109/access.2020.3004430

Cited by 14 publications

(10 citation statements)

References 41 publications

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“…According to the results of previous studies [ 1 , 3 , 23 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 ], the quality of speech blind source separation could be improved by swarm intelligence algorithms, but it is rare to find a study about multi-groups with random linear mixed signals. In their studies, the quality, convergence speed, and convergence accuracy of BSS were significantly improved by enhancing the swarm intelligence optimization algorithm.…”

Section: Discussionmentioning

confidence: 99%

Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

Liu

et al. 2021

Sensors

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In recent years, separating effective target signals from mixed signals has become a hot and challenging topic in signal research. The SI-BSS (Blind source separation (BSS) based on swarm intelligence (SI) algorithm) has become an effective method for the linear mixture BSS. However, the SI-BSS has the problem of incomplete separation, as not all the signal sources can be separated. An improved algorithm for BSS with SI based on signal cross-correlation (SI-XBSS) is proposed in this paper. Our method created a candidate separation pool that contains more separated signals than the traditional SI-BSS does; it identified the final separated signals by the value of the minimum cross-correlation in the pool. Compared with the traditional SI-BSS, the SI-XBSS was applied in six SI algorithms (Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Sine Cosine Algorithm (SCA), Butterfly Optimization Algorithm (BOA), and Crow Search Algorithm (CSA)). The results showed that the SI-XBSS could effectively achieve a higher separation success rate, which was over 35% higher than traditional SI-BSS on average. Moreover, SI-SDR increased by 14.72 on average.

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Section: Discussionmentioning

confidence: 99%

Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

Liu

et al. 2021

Sensors

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“…To solve the above problem, some scholars use chaos search to optimize the initialization sequence [ 21 , 22 , 30 , 31 , 32 ]. Although the diversity and ergodicity of the population are improved to a certain extent, the chaotic map is greatly affected by the initial solution, and the inappropriate initial solution will lead to negative optimization of the algorithm [ 33 ].…”

Section: Spsoa Search Algorithmmentioning

confidence: 99%

“…Perform telescopic translation on Equation (12) and introduce amplitude, telescopic, and translational factors to obtain:

where L represents the amplitude gain, and a and b represent the expansion and translation factors. Figure 2 b,d shows the iterative comparison between SOA2 with different parameters and basic SOA under the Sphere test function [ 33 ].…”

Section: Spsoa Search Algorithmmentioning

confidence: 99%

Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy

Xia

Ding

Zhang

et al. 2022

Entropy

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This paper aims to present a novel hybrid algorithm named SPSOA to address problems of low search capability and easy to fall into local optimization of seagull optimization algorithm. Firstly, the Sobol sequence in the low-discrepancy sequences is used to initialize the seagull population to enhance the population’s diversity and ergodicity. Then, inspired by the sigmoid function, a new parameter is designed to strengthen the ability of the algorithm to coordinate early exploration and late development. Finally, the particle swarm optimization learning strategy is introduced into the seagull position updating method to improve the ability of the algorithm to jump out of local optimization. Through the simulation comparison with other algorithms on 12 benchmark test functions from different angles, the experimental results show that SPSOA is superior to other algorithms in stability, convergence accuracy, and speed. In engineering applications, SPSOA is applied to blind source separation of mixed images. The experimental results show that SPSOA can successfully realize the blind source separation of noisy mixed images and achieve higher separation performance than the compared algorithms.

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“…As a new type of swarm intelligence optimization algorithm, the firework algorithm (FWA) [23][24][25][26][27] is inspired by the fireworks explosion process of traditional Chinese festivals. In the process of FWA, each spark produced by fireworks and explosions is regarded as a feasible solution.…”

Section: B Basic Firework Algorithmmentioning

confidence: 99%

Radar Main-Lobe Jamming Suppression Based on Adaptive Opposite Fireworks Algorithm

Luo¹,

Jin²,

Li³

et al. 2021

IEEE Open J. Antennas Propag.

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Optimal Performance and Application for Firework Algorithm Using a Novel Chaotic Approach

Cited by 14 publications

References 41 publications

Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

Improved Swarm Intelligent Blind Source Separation Based on Signal Cross-Correlation

Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy

Radar Main-Lobe Jamming Suppression Based on Adaptive Opposite Fireworks Algorithm

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