Truss sizing optimum design using a metaheuristic approach: connected banking system DOI Creative Commons

Mehrdad Nemati,

Yousef Zandi, Jamshid Sabouri

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(20), P. e39308 - e39308

Published: Oct. 1, 2024

Language: Английский

Application of the 2-archive multi-objective cuckoo search algorithm for structure optimization DOI Creative Commons
Ghanshyam G. Tejani, Nikunj Mashru, Pinank Patel

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 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.

Language: Английский

Citations

17
Optimization of truss structures with two archive-boosted MOHO algorithm DOI
Ghanshyam G. Tejani, Sunil Kumar Sharma, Nikunj Mashru

et al.

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 120, P. 296 - 317

Published: Feb. 18, 2025

Language: Английский

Citations

3
Solving optimal power flow frameworks using modified artificial rabbit optimizer DOI Creative Commons
Noor Habib Khan, Yong Wang, Raheela Jamal

et al.

Energy Reports, Journal Year: 2024, Volume and Issue: 12, P. 3883 - 3903

Published: Oct. 3, 2024

Language: Английский

Citations

4
A Transformer-based Multi-Platform Sequential Estimation Fusion DOI
Xiaowen Zhai, Yanbo Yang, Zhunga Liu

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 144, P. 110069 - 110069

Published: Jan. 20, 2025

Language: Английский

Citations

0
A Multi-Objective Black-Winged Kite Algorithm for Multi-UAV Cooperative Path Planning DOI Creative Commons

X.B. Liu,

Fufu Wang,

Yu Liu

et al.

Drones, Journal Year: 2025, Volume and Issue: 9(2), P. 118 - 118

Published: Feb. 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.

Language: Английский

Citations

0
Recent advances in Multi-objective Cuckoo Search Algorithm, its variants and applications DOI
Sharif Naser Makhadmeh, Mohammed A. Awadallah, Sofian Kassaymeh

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 9, 2025

Language: Английский

Citations

0
An efficient multi-objective parrot optimizer for global and engineering optimization problems DOI Creative Commons

Mohammed R. Saad,

Marwa M. Emam,

Essam H. Houssein

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 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

Language: Английский

Citations

0
A Chaotic Decomposition-Based Approach for Enhanced Multi-Objective Optimization DOI Creative Commons

Javad Alikhani Koupaei,

M. J. Ebadi

Mathematics, Journal Year: 2025, Volume and Issue: 13(5), P. 817 - 817

Published: Feb. 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.

Language: Английский

Citations

0
Multi-objective Optimization in the Renewal of Historic and Cultural Neighborhoods: Application to Shenyang's Bagua Street Using an Improved NSGA-II Algorithm DOI Creative Commons
Jie Ren, Yuxin Zhang

KSCE Journal of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 100214 - 100214

Published: March 1, 2025

Language: Английский

Citations

0
Leader Selection Based Multi-Objective Flow Direction Algorithm (MOFDA): A novel approach for engineering design problems DOI Creative Commons
Nima Khodadadi,

Mohammad Ehteram,

Hojat Karami

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103670 - 103670

Published: Dec. 1, 2024

Language: Английский

Citations

2