HFADE-FMD: a hybrid approach of fireworks algorithm and differential evolution strategies for functional module detection in protein-protein interaction networks

Ji, Junzhong; Xiao, Hanghang; Yang, Cuicui

doi:10.1007/s10489-020-01791-4

Cited by 13 publications

(3 citation statements)

References 54 publications

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“…Zhu et al [40] proposed a hybrid sparrow search algorithm (DEH-SSA) by integrating the DE algorithm with the sparrow search algorithm to address issues such as slow convergence, poor stability, and easy trapping of the sparrow search algorithm. Ji [41] proposed a hybrid method (HFADE-FMD) that combines the fireworks algorithm and the differential evolution strategy. Abraham [42] combined the firefly algorithm (FA) with the DE algorithm.…”

Section: Combination With Other Algorithmsmentioning

confidence: 99%

Multi-population multi-strategy differential evolution algorithm with dynamic population size adjustment

Xue,

Liu,

Deng

et al. 2023

Preprint

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Differential Evolution (DE) algorithm is a type of global optimization algorithm based on population searching for the optimal solution. It has characteristics such as fast convergence speed, simple and understandable algorithm, few parameters, and high stability. To further enhance its exhibition, we propose a differential evolution algorithm based on subpopulation adaptive scale and multi-adjustment strategy (ASMSDE). The algorithm divides population into three sub-populations based on the fitness values, the different operation strategies are adopted according to their characteristics. The superior population and inferior population adopt respectively Gaussian disturbance and Levy flights. The task of the intermediate population is to maintain the overall diversity of the population. The sizes of the three sub-populations are adaptively adjusted based on the evolutionary results to accommodate changes in individual differences during the evolution process. With the number of iterations increases and the differences between individuals decrease, adopt single population model instead of multi-population model in the later stage of evolution. To evaluate the performance of the ASMSDE algorithm, it is compared with other advanced algorithms using benchmark function optimizations. Experimental results demonstrate that the ASMSDE algorithm outperforms the comparison algorithms in most cases, validating its effectiveness and ability to handle local optima situations.

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Section: Combination With Other Algorithmsmentioning

confidence: 99%

Multi-population multi-strategy differential evolution algorithm with dynamic population size adjustment

Xue,

Liu,

Deng

et al. 2023

Preprint

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“…The fireworks algorithm (FWA) is a young swarm intelligence algorithm proposed by Tan [12], which has shown promising capability to solve complex optimization problems [13][14][15][16][17][18][19][20]. By simulating the phenomenon of firework explosion, FWA conducts local search by implementing the explosion operation [21].…”

Section: Introductionmentioning

confidence: 99%

OLFWA: A novel fireworks algorithm with new explosion operator and two stages information utilization

Fan,

Zhou,

Han

et al. 2023

Information Sciences

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“…To cover shortages of the existing methods, fireworks algorithm is studied in this paper to solve low-carbon VRP. Fireworks algorithm [26] is a metaheuristic algorithm first presented by Professor Tan Ying of Peking University in 2010. The fireworks algorithm is inspired by the natural phenomenon that fireworks explode in the night sky to generate sparks and illuminate the surrounding area.…”

Section: Introductionmentioning

confidence: 99%

A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

Shen

You

et al. 2022

Complex Syst. Model. Simul.

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A constrained multi-objective optimization model for the low-carbon vehicle routing problem (VRP) is established. A carbon emission measurement method considering various practical factors is introduced. It minimizes both the total carbon emissions and the longest time consumed by the sub-tours, subject to the limited number of available vehicles. According to the characteristics of the model, a region enhanced discrete multi-objective fireworks algorithm is proposed. A partial mapping explosion operator, a hybrid mutation for adjusting the sub-tours, and an objective-driven extending search are designed, which aim to improve the convergence, diversity, and spread of the non-dominated solutions produced by the algorithm, respectively. Nine low-carbon VRP instances with different scales are used to verify the effectiveness of the new strategies. Furthermore, comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon VRP. It provides a promising scalability to the problem size.

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HFADE-FMD: a hybrid approach of fireworks algorithm and differential evolution strategies for functional module detection in protein-protein interaction networks

Cited by 13 publications

References 54 publications

Multi-population multi-strategy differential evolution algorithm with dynamic population size adjustment

Multi-population multi-strategy differential evolution algorithm with dynamic population size adjustment

OLFWA: A novel fireworks algorithm with new explosion operator and two stages information utilization

A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

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