A multi-strategy spider wasp optimizer based on grouping and dimensional symmetry method with a time-varying weight

Feng, Zhiyu; Zhu, Donglin; Guo, Huaiyu; Sun, Gaoji; Zhou, Changjun

doi:10.1007/s13042-024-02216-1

Cited by 1 publication

(1 citation statement)

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“…In response to the issue of unstable optimization performance and susceptibility to stagnation in the Harris hawks optimization (HHO) algorithm, a novel integrated improved HHO (IIHHO) algorithm has been proposed and demonstrated to hold promising prospects for numerical optimization and engineering applications (Ouyang et al, 2024). Feng et al (2024) incorporated the adaptive group-based strategy application (AGSA), dimensional symmetric distance optimization strategy (DSD), and time-varying weight (TW) into the spider wasp optimizer (SWO), substantially augmenting its performance. Its performance improvement did not come at the cost of increased complexity.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Manta Ray Foraging Optimization Algorithm of Variants and Applications

Zheng,

Wang,

et al. 2024

International Journal of Swarm Intelligence Research

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This review paper delves into the original MRFO and its variants, focusing on single-objective algorithms including but not limited to hybrid algorithms, learning strategies, multiple populations and dynamic parameter adjustment, highlighting the improvements made to enhance the algorithm's efficiency in global optimization, accelerate convergence rates, and improve its capacity to evade local optima. MRFO has emerged as an effective tool for solving complex optimization problems across various domains, including energy optimization, biomedical field, engineering problems, and others. A comprehensive analysis of applications of MRFO in different fields is provided, emphasizing its adaptability and efficacy. The paper concludes with a discussion on the challenges faced by MRFO and potential future research directions, aiming to consolidate the current research status and guide future investigations.

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Section: Introductionmentioning

confidence: 99%