A sinusoidal social learning swarm optimizer for large-scale optimization

Liu, Nengxian; Pan, Jeng‐Shyang; Chu, Shu-Chuan; Hu, Pei

doi:10.1016/j.knosys.2022.110090

Cited by 8 publications

(1 citation statement)

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“…Zhang et al [39] proposed a reinforced memory strategy for PSO, ensuring diverse particle leaders while maintaining a balance between global exploration and local exploitation. Furthermore, Lin et al [40] applied a new learning strategy to PSO, adapting it for large-scale optimization problems and demonstrating its effectiveness in FS.…”

Section: Introductionmentioning

confidence: 99%

Enhanced slime mould algorithm with backtracking search algorithm: global optimization and feature selection

Wang,

Chen,

Zou

et al. 2024

Preprint

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The Slime Mould Algorithm (SMA), renowned for its swarm-based approach, encounters challenges, particularly in maintaining a balance between exploration and exploitation, leading to a trade-off that impacts its optimization performance. The simple structure and limited hyperparameters of SMA contribute to difficulties in effectively navigating the exploration-exploitation trade-off, with a drawback being its poor ability for exploration. To address these challenges and enhance SMA, this paper introduces BSSMA, an improved variant that incorporates the Backtracking Search Algorithm (BSA). The introduction of the \(phaseratio\) parameter aims to synergize BSA and SMA, capitalizing on the strengths of both algorithms while mitigating their individual drawbacks, including SMA's poor exploration ability. BSA facilitates a thorough exploration, dispersing search agents widely across the solution space, ensuring significant diversity. These search agents then transition to SMA to further refine the search for optimal solutions while addressing SMA's exploration limitations. Evaluating the performance of BSSMA involves comparisons with 12 other meta-heuristic algorithms (MAs) and 10 advanced MAs using the CEC2017 benchmark functions. Experimental results showcase that the enhanced BSSMA outperforms SMA in terms of convergence speed and accuracy, specifically addressing the challenges associated with balancing exploration and exploitation trade-offs, including SMA's poor exploration ability. Additionally, to demonstrate BSSMA's effectiveness in practical engineering applications, a binary version (bBSSMA) is developed for feature selection (FS) using a V-shaped transfer function. Comparative experiments with seven other binary MA variants reveal that bBSSMA selects fewer features, attains higher classification accuracy, and demands less computational time. These results affirm the effectiveness of bBSSMA for practical feature selection applications.

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Section: Introductionmentioning

confidence: 99%