Abstract
| Original language | English |
|---|---|
| Journal | Computer Methods in Applied Mechanics and Engineering |
| Volume | 398 |
| DOIs | |
| Publication status | Published - 2022 |
Keywords
- Artificial hummingbird algorithm
- Convergence and diversity
- Dynamic elimination-based crowding distance
- Engineering design problems
- Multi-objective optimization
- Non-dominated sorting
- HTTP
- Optimal systems
- Pareto principle
- Crowding distance
- Multi objective
- Multi-objectives optimization
- Non-dominated Sorting
- Pareto optimal solutions
- Real-world
- Multiobjective optimization
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In: Computer Methods in Applied Mechanics and Engineering, Vol. 398, 2022.
Research output: Contribution to journal › Article › peer-review
TY - JOUR
T1 - An effective multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems
T2 - Computer Methods in Applied Mechanics and Engineering
AU - Zhao, W.
AU - Zhang, Z.
AU - Mirjalili, S.
AU - Wang, L.
AU - Khodadadi, N.
AU - Mirjalili, S.
N1 - Export Date: 11 July 2022 CODEN: CMMEC Correspondence Address: Wang, L.; School of Water Conservancy and Hydropower, Hebei, China; email: [email protected] Funding details: SLRC2019022 Funding details: National Natural Science Foundation of China, NSFC, 11972144, 12072098 Funding text 1: This work was supported in part by National Natural Science Foundation of China ( 11972144 and 12072098 ), and One Hundred Outstanding Innovative Scholars of Colleges and Universities in Hebei Province of China ( SLRC2019022 ). References: Shefaei, A., Vahid-Pakdel, M.J., Mohammadi-Ivatloo, B., Application of a hybrid evolutionary algorithm on reactive power compensation problem of distribution network (2018) Comput. Electr. 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PY - 2022
Y1 - 2022
N2 - Artificial hummingbird algorithm (AHA) is a recently developed bio-based metaheuristic and it shows superior performance in handling single-objective optimization problems. Despite the merit, this algorithm can only solve problems with one objective. To solve complex multi-objective optimization problems, including engineering design problems, a multi-objective AHA (MOAHA) is developed in this study. In MOAHA, an external archive is employed to save Pareto optimal solutions, and a dynamic elimination-based crowding distance (DECD) method is developed to maintain this archive to effectively preserve the population diversity. In addition, a non-dominated sorting strategy is merged with MOAHA to construct a solution update mechanism, which effectively refines Pareto optimal solutions for improving the convergence of the algorithm. The superior results over 7 competitors on 28 benchmark functions in terms of convergence, diversity and solution distribution are demonstrated with a suite of comprehensive tests. The MOAHA algorithm is also applied to 5 real-world engineering design problems with multiple objectives, demonstrating its superiority in handling challenging real-world multi-objective problems with unknown true Pareto optimal solutions and fronts. The source code of MOAHA is publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/113535-moaha-multi-objective-artificial-hummingbird-algorithm and https://seyedalimirjalili.com/aha. © 2022 Elsevier B.V.
AB - Artificial hummingbird algorithm (AHA) is a recently developed bio-based metaheuristic and it shows superior performance in handling single-objective optimization problems. Despite the merit, this algorithm can only solve problems with one objective. To solve complex multi-objective optimization problems, including engineering design problems, a multi-objective AHA (MOAHA) is developed in this study. In MOAHA, an external archive is employed to save Pareto optimal solutions, and a dynamic elimination-based crowding distance (DECD) method is developed to maintain this archive to effectively preserve the population diversity. In addition, a non-dominated sorting strategy is merged with MOAHA to construct a solution update mechanism, which effectively refines Pareto optimal solutions for improving the convergence of the algorithm. The superior results over 7 competitors on 28 benchmark functions in terms of convergence, diversity and solution distribution are demonstrated with a suite of comprehensive tests. The MOAHA algorithm is also applied to 5 real-world engineering design problems with multiple objectives, demonstrating its superiority in handling challenging real-world multi-objective problems with unknown true Pareto optimal solutions and fronts. The source code of MOAHA is publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/113535-moaha-multi-objective-artificial-hummingbird-algorithm and https://seyedalimirjalili.com/aha. © 2022 Elsevier B.V.
KW - Artificial hummingbird algorithm
KW - Convergence and diversity
KW - Dynamic elimination-based crowding distance
KW - Engineering design problems
KW - Multi-objective optimization
KW - Non-dominated sorting
KW - HTTP
KW - Optimal systems
KW - Pareto principle
KW - Crowding distance
KW - Multi objective
KW - Multi-objectives optimization
KW - Non-dominated Sorting
KW - Pareto optimal solutions
KW - Real-world
KW - Multiobjective optimization
U2 - 10.1016/j.cma.2022.115223
DO - 10.1016/j.cma.2022.115223
M3 - Article
SN - 0045-7825
VL - 398
JO - Computer Methods in Applied Mechanics and Engineering
JF - Computer Methods in Applied Mechanics and Engineering
ER -