A novel hybrid dynamic fireworks algorithm with particle swarm optimization

Zhu, Fang; Chen, Debao; Zou, Feng

doi:10.1007/s00500-020-05308-6

Cited by 18 publications

(13 citation statements)

References 33 publications

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“…To verify the improvement of the proposed method to MCKD algorithm, according to Literature [27], [32], [33], the IFWA and MCKD parameter settings are shown in Table 6.…”

Section: B Ifwa-mckdmentioning

confidence: 99%

Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks

Luo

Zhou

et al. 2021

IEEE Access

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Rolling bearings, as the main components of the large industrial rotating equipment, usually work under complex conditions and are prone to break down. It can provide a certain theoretical basis for identifying the sub-health state of the industrial equipment by the analysis from the incipient weak signals. Thus, a subhealth recognition offline algorithm based on Refined Composite Multiscale Dispersion Entropy (RCMDE) and Deep Belief Network-Extreme Learning Machine (DBN-ELM) optimized by Improved Firework Algorithm (IFWA) is proposed. First of all, in light of the drawbacks that it is easy to fall into local optima and cross the boundary for exploding fireworks in Firework Algorithm (FWA), Cauchy mutation and adaptive dynamic explosion radius factor coefficient is introduced into IFWA. Secondly, Maximum Correlation Kurtosis Deconvolution (MCKD) optimized by the improved parameters is used to process the incipient vibration signals with nonlinearity, nonstationary, and IFWA is used to adaptively adjust to the period and the filter length in MCKD(IFWA-MCKD). Then, each sequence of signals is further extracted the feature-RCMDE to rich sample diversity. Finally, combining the powerful unsupervised learning capability from DBN and the generalization capability from ELM, DBN-ELM can be established. What's more, in order to avoid the interference of human on the parameters, IFWA is used to optimize the number of hidden nodes in DBN-ELM, and the IFWA-DBN-ELM is established. It shows that the algorithm has the higher sub-health recognition accuracy, better robustness and generalization, which has a better industrial application prospect.

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“…To verify the improvement of the proposed method to MCKD algorithm, according to Literature [27], [32], [33], the IFWA and MCKD parameter settings are shown in Table 6.…”

Section: B Ifwa-mckdmentioning

confidence: 99%

Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks

Luo

Zhou

et al. 2021

IEEE Access

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“…In the early development period of PSO algorithm, the main work is to solve SOPs, and that is also the original intention of PSO. During this period, many scholars put forward a variety of schemes to upgrade the performance of PSO algorithm, which also has a profound impact on the follow-up research work on MOPs (Huang et al 2019;Zhu et al 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Multi-Objective Particle Swarm Optimization Using Three-Stage Strategy

Huang¹,

Zhang²

2021

Preprint

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It is one of the crucial problems in solving multi-objective problems (MOPs) that balance the convergence and diversity of the algorithm to obtain an outstanding Pareto optimal solution set. In order to elevate the performance further and improve the optimization efficiency of multi-objective particle swarm optimization (MOPSO), a novel adaptive MOPSO using a three-stage strategy (tssAMOPSO) is proposed in this paper, which can effectively balance the exploration and exploitation of the population and facilitate the convergence and diversity of MOPSO. Firstly, an adaptive flight parameter adjustment, formulated by the convergence contribution of nondominated solutions, can ameliorate the convergence and diversity of the algorithm enormously. Secondly, the population carries out the three-stage strategy of optimization in each iteration, namely adaptive optimization, decomposition, and Gaussian attenuation mutation. The three-stage strategy remarkably promotes the diversity and efficiency of the optimization process. Moreover, the convergence of three-stage optimization strategy is analyzed. Then, memory interval is equipped with particles to record the recent positions, which vastly improves the reliability of personal best selection. In the maintenance of external archive, the proposed fusion index can enhance the quality of nondominated solutions directly. Finally, comparative experiments are designed by a series of benchmark instances to verify the performance of tssAMOPSO. Experimental results show that the proposed algorithm achieves admirable performance compared with other contrast algorithms.

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“…As an excellent algorithm, FWA has been used in hybridization with many other algorithms in recent years. Zhu et al hybridized the firework algorithm with the particle swarm algorithm to form DFWPSO, which performed competitively and effectively in numerical optimization problems [25]. Yue et al proposed a new hybrid algorithm called FWGWO based on gray wolf optimizer and firework algorithm and achieved excellent results in global optimization [26].…”

Section: Introductionmentioning

confidence: 99%

GPOFWA: A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

Dong

Zou

et al. 2022

Preprint

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This paper proposes a novel hybrid optimization algorithm named GPOFWA, which integrates political optimizer (PO) with fireworks algorithm (FWA) to solve numerical and engineering optimization problems. The original PO uses subgroup optimal solutions such as party leaders and constituency winners to guide the movement of the search agent. However, the number of such subgroup optimal solutions is limited, which leads to insufficient global exploration capabilities of PO. In addition, the recent past-based position updating strategy (RPPUS) of PO lacks effective verification of the updated candidate solutions, which reduces the convergence speed of the algorithm. The proposed hybrid algorithm uses the spark explosion mechanism in FWA to perform explosion spark and Gauss explosion spark operations on the subgroup optimal solutions (party leader and constituency winner) respectively based on the greedy strategy, which optimizes the subgroup optimal solution and enhances the exploitative ability of the algorithm. Moreover, Gaussian explosion sparks are also used to correct the candidate solutions after RPPUS, which makes up for the shortcomings of the original PO. In addition, a new subgroup optimal solution called the Converged Mobile Center (CMC) based on two-way consideration is designed to guide the movement of search agents. We test the presented hybrid algorithm on 30 well-known benchmark test functions and three engineering optimization problems. The experimental results show that GPOFWA is superior to nine state-of-the-art methods in terms of the quality of the resulting solution.

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A novel hybrid dynamic fireworks algorithm with particle swarm optimization

Cited by 18 publications

References 33 publications

Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks

Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks

Adaptive Multi-Objective Particle Swarm Optimization Using Three-Stage Strategy

GPOFWA: A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

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