Heliyon, Год журнала: 2024, Номер 10(20), С. e39308 - e39308
Опубликована: Окт. 1, 2024
Язык: Английский
Scientific Reports, Год журнала: 2024, Номер 14(1)
Опубликована: Дек. 30, 2024
The study suggests a better multi-objective optimization method called 2-Archive Multi-Objective Cuckoo Search (MOCS2arc). It is then used to improve eight classical truss structures and six ZDT test functions. aims minimize both mass compliance simultaneously. MOCS2arc an advanced version of the traditional (MOCS) algorithm, enhanced through dual archive strategy that significantly improves solution diversity performance. To evaluate effectiveness MOCS2arc, we conducted extensive comparisons with several established algorithms: MOSCA, MODA, MOWHO, MOMFO, MOMPA, NSGA-II, DEMO, MOCS. Such comparison has been made various performance metrics compare benchmark efficacy proposed algorithm. These comprehensively assess algorithms' abilities generate diverse optimal solutions. statistical results demonstrate superior evidenced by Additionally, Friedman's & Wilcoxon's corroborate finding consistently delivers compared others. show highly effective improved algorithm for structure optimization, offering significant promising improvements over existing methods.
Язык: Английский
Процитировано
17
Alexandria Engineering Journal, Год журнала: 2025, Номер 120, С. 296 - 317
Опубликована: Фев. 18, 2025
Язык: Английский
Процитировано
3
Energy Reports, Год журнала: 2024, Номер 12, С. 3883 - 3903
Опубликована: Окт. 3, 2024
Язык: Английский
Процитировано
4
Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 144, С. 110069 - 110069
Опубликована: Янв. 20, 2025
Язык: Английский
Процитировано
0
Drones, Год журнала: 2025, Номер 9(2), С. 118 - 118
Опубликована: Фев. 5, 2025
In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach find a balanced solution that mitigates effects of subjective weighting, utilizing multi-objective optimization algorithm address complex planning issues involve multiple machines. Here, we introduce an advanced mathematical model cooperative path among UAVs in urban logistics scenarios, employing non-dominated sorting black-winged kite (NSBKA) this challenge. To evaluate efficacy NSBKA, was benchmarked against other algorithms using Zitzler, Deb, and Thiele (ZDT) test problems, Thiele, Laumanns, Zitzler (DTLZ) functions from conference on evolutionary computation 2009 (CEC2009) three types problems. Comparative analyses statistical results indicate proposed outperforms all 22 functions. verify capability NSBKA addressing multi-UAV problem model, applied solve problem. Simulation experiments five show can obtain reasonable collaborative set UAVs. Moreover, based generally superior terms energy saving, safety, computing efficiency during planning. This affirms effectiveness meta-heuristic dealing with objective cooperation problems further enhances robustness competitiveness NSBKA.
Язык: Английский
Процитировано
0
Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown
Опубликована: Фев. 9, 2025
Язык: Английский
Процитировано
0
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Фев. 11, 2025
Abstract The Parrot Optimizer (PO) has recently emerged as a powerful algorithm for single-objective optimization, known its strong global search capabilities. This study extends PO into the Multi-Objective (MOPO), tailored multi-objective optimization (MOO) problems. MOPO integrates an outward archive to preserve Pareto optimal solutions, inspired by behavior of Pyrrhura Molinae parrots. Its performance is validated on Congress Evolutionary Computation 2020 (CEC’2020) benchmark suite. Additionally, extensive testing four constrained engineering design challenges and eight popular confined unconstrained test cases proves MOPO’s superiority. Moreover, real-world helical coil springs automotive applications conducted depict reliability proposed in solving practical Comparative analysis was performed with seven published, state-of-the-art algorithms chosen their proven effectiveness representation current research landscape-Improved Manta-Ray Foraging Optimization (IMOMRFO), Gorilla Troops (MOGTO), Grey Wolf (MOGWO), Whale Algorithm (MOWOA), Slime Mold (MOSMA), Particle Swarm (MOPSO), Non-Dominated Sorting Genetic II (NSGA-II). results indicate that consistently outperforms these across several key metrics, including Set Proximity (PSP), Inverted Generational Distance Decision Space (IGDX), Hypervolume (HV), (GD), spacing, maximum spread, confirming potential robust method addressing complex MOO
Язык: Английский
Процитировано
0
Mathematics, Год журнала: 2025, Номер 13(5), С. 817 - 817
Опубликована: Фев. 28, 2025
Multi-objective optimization problems often face challenges in balancing solution accuracy, computational efficiency, and convergence speed. Many existing methods struggle with achieving an optimal trade-off between exploration exploitation, leading to premature or excessive costs. To address these issues, this paper proposes a chaotic decomposition-based approach that leverages the ergodic properties of maps enhance performance. The proposed method consists three key stages: (1) sequence initialization, which generates diverse population global search while reducing costs; (2) chaos-based correction, integrates three-point operator (TPO) local improvement (LIO) refine Pareto front balance exploration–exploitation trade-offs; (3) Tchebycheff updating, ensuring efficient toward solutions. validate effectiveness method, we conducted extensive experiments on suite benchmark compared its performance several state-of-the-art methods. evaluation metrics, including inverted generational distance (IGD), (GD), spacing (SP), demonstrated achieves competitive accuracy efficiency. While maintaining feasibility, our provides well-balanced improved diversity stability. results establish algorithm as promising alternative for solving multi-objective problems.
Язык: Английский
Процитировано
0
KSCE Journal of Civil Engineering, Год журнала: 2025, Номер unknown, С. 100214 - 100214
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0
Results in Engineering, Год журнала: 2024, Номер unknown, С. 103670 - 103670
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
2