The artificialhummingbirdalgorithm (AHA) is a recently introduced versatile metaheuristic optimizer that simulates flight patterns and intelligent foraging skills of hummingbirds. It has gained widespread recognitio...
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The artificialhummingbirdalgorithm (AHA) is a recently introduced versatile metaheuristic optimizer that simulates flight patterns and intelligent foraging skills of hummingbirds. It has gained widespread recognition for its simplicity and adaptability to a wide range of optimization problems. However, the limited ability of the algorithm to establish the exploration-exploitation balance leads to getting stuck in local solution traps and premature convergence. To eliminate these drawbacks, this study introduces an enhanced artificial hummingbird algorithm (enAHA) based on a dynamic fitness-distance balance (dFDB) operator. dFDB offers the opportunity to precisely balance exploration and exploitation throughout the optimization process with its dynamically adjustable weight coefficient. The convergence rate of the developed enAHA is tested on CEC 2020 and CEC 2022 benchmark problems. The enAHA and the original AHA results are statistically analyzed with the Wilcoxon signed-rank test. As per Wilcoxon test results, the proposed enAHA outperforms the original AHA algorithm for 70 %, 50 %, and 70 % of the CEC 2020 problems in 30-, 50-, and 100-dimensional optimization, respectively. In the CEC 2022 test suite, the enAHA showed a success rate of 58.33 % and 91.66 % with 10- and 20-dimensions. Moreover, the optimization capacity of enAHA is compared with the 29 state-of-the-art optimizers using the Friedman-rank test. Accordingly, the proposed enAHA algorithm ranked 1st, while the original AHA ranked 9th among the 30 competing algorithms. Furthermore, the practicability of the enAHA is validated on three engineering design problems: i) single -diode solar cell (SDSC) parameter extraction, ii) double-diode solar cell (DDSC) parameter estimation, and iii) optimization of pressure vessel design. The developed method provided minimum RMSE values of 7.730064E-04 for the SDSC and 7.422194E-04 for the DDSC. The enAHA algorithm achieved the best cost with a value of 5885.332773
作者：
Wang, HongChen, DaWu, LihuiZhang, JieDonghua Univ
Coll Mech Engn Shanghai Peoples R China Donghua Univ
Inst Artificial Intelligence Minist Educ Engn Res Ctr Artificial Intelligence Text Ind Shanghai Peoples R China Donghua Univ
Inst Artificial Intelligence Shanghai Engn Res Ctr Ind Big Data & Intelligent S Shanghai Peoples R China Shanghai Inst Technol
Coll Mech Engn Shanghai Peoples R China
Wire-bonding machine scheduling in Semiconductor Assembly and Testing (SAT) involves thousands of lots and hundreds of machines, challenging high-quality solutions in a reasonable time. This paper proposes a framework...
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Wire-bonding machine scheduling in Semiconductor Assembly and Testing (SAT) involves thousands of lots and hundreds of machines, challenging high-quality solutions in a reasonable time. This paper proposes a framework named the enhanced artificial hummingbird algorithm with Knowledge Learning and Progressive Fusion Decomposition (EAHA-KLPFD), integrating knowledge learning and progressive fusion decomposition. It is employed for rational decomposition, preserves solution space integrity, accelerates solving, and enhances search efficiency. Otherwise, the enhanced artificial hummingbird algorithm proposed improves subproblem precision. Experiments demonstrate that the framework excels in accuracy and speed and provides an effective solution for wire-bonding machine scheduling in SAT system.
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