Paper
27 September 2024 Study of a hybrid algorithm based on NSGA-II and WOA for multiobjective problems
Xingping Zhang, Huan Pang, Baoqi Chen, Tong Luo, Linwei Xia
Author Affiliations +
Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 132810H (2024) https://doi.org/10.1117/12.3050754
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China
Abstract
In engineering practice, multi-objective optimization problems (Multi-objective Optimization Problems, MOPs) are common, but multi-objective optimization problems usually cannot be solved directly. In order to solve the multi- objective optimization problem, the goals to be optimized are usually analyzed, and the problems to be studied are transformed into computable mathematical models by using mathematical theories and methods. At the same time, the mathematical model and calculation method are studied, and a more appropriate algorithm is selected to further solve the mathematical model established. After obtaining all feasible schemes that meet the research objectives, the best scheme is selected according to the needs of the research objectives. In the process of multi-objective function optimization, the problem of constraint complexity and high dimension is encountered, so the choice of the algorithm has a great impact on the accuracy of the solution. Therefore, in this paper, the hybrid algorithm of NSGA-II and WOA is adopted to solve the optimization scheduling problem, which is a mixture of NSGA-II and WOA. The hybrid algorithm of NSGA-II and WOA can obtain the optimal solution set in each calculation and select the optimal solution.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Xingping Zhang, Huan Pang, Baoqi Chen, Tong Luo, and Linwei Xia "Study of a hybrid algorithm based on NSGA-II and WOA for multiobjective problems", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 132810H (27 September 2024); https://doi.org/10.1117/12.3050754
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KEYWORDS
Mathematical optimization

Bubbles

Algorithm testing

Distance measurement

Mathematical modeling

Power supplies

Engineering

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