Tehnički vjesnik, Vol. 32 No. 6, 2025.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20240721001872
Adaptive Swarm Intelligence: A Time-Varying Fitness Guided Optimization Algorithm for Dynamic Search Strategies
Yali Chu
; Changchun University of Technology, School of Mathematics and Statistics, Changchun 130000, China
Xuming Han
; Jinan University, College of Information Science and Technology, Guangzhou 510632, China
*
Zhiquan Liu
; Jinan University, College of Cyber Security, Guangzhou 510632, China
Ting Zhou
; Jinan University, College of Information Science and Technology, Guangzhou 510632, China
* Dopisni autor.
Sažetak
Existing swarm intelligence algorithms have utilized fitness to select individuals and developed search strategies based on their locations. However, these algorithms overlook the dynamic variations in fitness over time, limiting the flexibility to adjust search step sizes and reducing optimization performance. To address this, a Time-varying Fitness Guided Optimization Algorithm (TFGOA) is proposed. TFGOA guides individuals to effectively adapt for global and local searches by quantifying time-varying fitness characteristics. Specifically, TFGOA proposes an individual time-varying fitness factor to detect real-time variations in performance. Based on this factor, a time-varying fitness guided search mechanism is developed. This mechanism flexibility adjusts the search behavior for each individual, thereby identifying potential optimal solutions more accurately. TFGOA is tested on the CEC2017, CEC2020, and CEC2022 benchmarks and is comprehensively compared with seven state-of-the-art algorithms. Experimental results demonstrate that TFGOA outperforms the comparison algorithms in terms of convergence and computational time. TFGOA is also tested on welded beam, gear train, and three-bar truss design problems, showing superiority over other comparison algorithms in terms of solution accuracy.
Ključne riječi
fitness; search strategy; swarm intelligence optimization
Hrčak ID:
337713
URI
Datum izdavanja:
31.10.2025.
Posjeta: 430 *