.png){kind=icon}
Return
An effective multi-objective bald eagle search algorithm for solving engineering design problems
.png){kind=icon}
OA
AI
DOI：10.1016/j.asoc.2023.110585.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, a multi-objective bald eagle search algorithm (MOBES) is proposed. The MOBES introduces an archive mechanism to store the non-dominated solutions obtained by the algorithm. When the archive overflows, remove the most crowded solutions by using the roulette method. The MOBES also adds elite selection strategy to guide other individuals to optimize by selecting elite individuals in the population. The efficiency of MOBES is validated on CEC 2020 benchmark functions, and the results demonstrate that the proposed algorithm is more efficient than its competitors in terms of convergence, diversity and distribution of solutions. The MOBES is also applied to two-objective, tri-objective and four-objective engineering design problems in real world. The results show its superiority in handling challenging multi-objective optimization problems with unknown true Pareto optimal solutions and fronts, and it is more competitive than other algorithms.& COPY; 2023 Elsevier B.V. All rights reserved.
Keywords:
Multi-objective bald eagle search algorithm
Pareto optimal solutions
Benchmark function
Engineering design problems
Metaheuristic
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
Organization
Cited Papers
Diversity and Functional Potential of Soil Bacterial Communities in Different Types of Farmland Shelterbelts in Mid-Western Heilongjiang, China.png)
ForestsOAAI
ForestsIF0
OAAIShareSaveMoSSE: a novel hybrid multi-objective meta-heuristic algorithm for engineering design problemsSOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
ShareSaveShareSaveShareSave
ShareSave
View more