Multi-Strategy-Improved Growth Optimizer and Its Applications

Axioms, Год журнала: 2024, Номер 13(6), С. 361 - 361

Опубликована: Май 28, 2024

The growth optimizer (GO) is a novel metaheuristic algorithm designed to tackle complex optimization problems. Despite its advantages of simplicity and high efficiency, GO often encounters localized stagnation when dealing with discretized, high-dimensional, multi-constraint To address these issues, this paper proposes an enhanced version called CODGBGO. This incorporates three strategies enhance performance. Firstly, the Circle-OBL initialization strategy employed quality initial population. Secondly, exploration implemented improve population diversity algorithm’s ability escape local optimum traps. Finally, exploitation utilized convergence speed accuracy algorithm. validate performance CODGBGO, it applied solve CEC2017, CEC2020, 18 feature selection problems, 4 real engineering experiments demonstrate that CODGBGO effectively addresses challenges posed by offering promising approach.

Язык: Английский

CMPSO: A novel co-evolutionary multigroup particle swarm optimization for multi-mission UAVs path planning

Advanced Engineering Informatics, Год журнала: 2024, Номер 63, С. 102923 - 102923

Опубликована: Ноя. 26, 2024

Язык: Английский