An effective multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems

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 pro...

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Vydáno v:Computer methods in applied mechanics and engineering Ročník 398; s. 115223
Hlavní autoři: Zhao, Weiguo, Zhang, Zhenxing, Mirjalili, Seyedali, Wang, Liying, Khodadadi, Nima, Mirjalili, Seyed Mohammad
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.08.2022
Elsevier BV
Témata:
Algorithms
Archives & records
Artificial hummingbird algorithm
Convergence
Convergence and diversity
Crowding
Design engineering
Design optimization
Dynamic elimination-based crowding distance
Engineering design problems
Heuristic methods
Multi-objective optimization
Multiple objective analysis
Non-dominated sorting
Pareto optimization
Pareto optimum
Source code
Dynamic elimination-based crowding distance
Multi-objective optimization
Artificial hummingbird algorithm
Engineering design problems
Convergence and diversity
Non-dominated sorting
ISSN:0045-7825, 1879-2138
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Shrnutí: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. •Multi-objective artificial hummingbird algorithm (MOAHA) is proposed.•Dynamic elimination-based crowding distance is used to produce a well-distributed PF.•A solution update mechanism using NDS is employed to update non-dominated solutions.•A visit table maintenance mechanism is employed in three foraging strategies.•It shows superior results over 7 competitors on 28 functions and 5 engineering cases.
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ISSN:0045-7825
1879-2138
DOI:10.1016/j.cma.2022.115223