An Atomic Retrospective Learning Bare Bone Particle Swarm Optimization

Zhou, Guoyuan; Guo, Jia; Ke, Yan; Zhou, Guangyu; Li, Bowen

doi:10.1007/978-3-031-36622-2_14

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the dimension increases, the distance between valid data points becomes extremely dispersed, thus affecting the convergence speed and stability of the algorithm. When solving high-dimensional optimization problems, although the Honey Badger Algorithm (HBA) [32] and Atomic Retrospective Learning Bare Bone Particle Swarm Optimization (ARBBPSO) [33] converged fast in local search, the global search capability is insufficient and it is easy to fall into local optimums. Due to the insufficient exploitation ability, the Chimp optimization algorithm (ChOA) [34] which was inspired by behavior of the chimp showed low convergence accuracy in high-dimensional optimization problems.…”

Section: Introductionmentioning

confidence: 99%

A novel meta-heuristic algorithm for high-dimensional problems: Rhinopithecus Swarm Optimization

Zhou,

Wang,

Zhou

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

This paper introduces a novel meta-heuristic algorithm know as Rhinopithe-cus Swarm Optimization (RSO) to deal with the high-dimensional problems. The RSO is inspired by the different social behaviors of different subgroups in the rhinopithecus swarm. This algorithm categorizes the population into king, matures, adolescences and infancy based on fitness values before each iteration. At iterations they are given different search methods including vertical migration , concerted search and mimicry due to their social division of labor. The experiment was independently conducted 36 times on the highest dimension recommended by CEC2017. The results show that comprehensive performance of RSO is higher than 8 well-known meta-heuristic algorithms including DBO, BWO, SSA, AVOA, WOA, ARBBPSO, GTO, and HHO. RSO ranked first in the CEC2017 with a score of 1.655 on 29 benchmark functions using Ferideman test, which is 42.8% better than the second-ranked algorithm SSA. Overall, it 1 is concluded that RSO outperforms well-known algorithms and can better solve high-dimensional optimization problems

show abstract

Section: Introductionmentioning

confidence: 99%

A novel meta-heuristic algorithm for high-dimensional problems: Rhinopithecus Swarm Optimization

Zhou,

Wang,

Zhou

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

A rhinopithecus swarm optimization algorithm for complex optimization problem

Zhou,

Wang,

Zhou

et al. 2024

Sci Rep

View full text Add to dashboard Cite

This paper introduces a novel meta-heuristic algorithm named Rhinopithecus Swarm Optimization (RSO) to address optimization problems, particularly those involving high dimensions. The proposed algorithm is inspired by the social behaviors of different groups within the rhinopithecus swarm. RSO categorizes the swarm into mature, adolescent, and infancy individuals. Due to this division of labor, each category of individuals employs unique search methods, including vertical migration, concerted search, and mimicry. To evaluate the effectiveness of RSO, we conducted experiments using the CEC2017 test set and three constrained engineering problems. Each function in the test set was independently executed 36 times. Additionally, we used the Wilcoxon signed-rank test and the Friedman test to analyze the performance of RSO compared to eight well-known optimization algorithms: Dung Beetle Optimizer (DBO), Beluga Whale Optimization (BWO), Salp Swarm Algorithm (SSA), African Vultures Optimization Algorithm (AVOA), Whale Optimization Algorithm (WOA), Atomic Retrospective Learning Bare Bone Particle Swarm Optimization (ARBBPSO), Artificial Gorilla Troops Optimizer (GTO), and Harris Hawks Optimization (HHO). The results indicate that RSO exhibited outstanding performance on the CEC2017 test set for both 30 and 100 dimension. Moreover, RSO ranked first in both dimensions, surpassing the mean rank of the second-ranked algorithms by 7.69% and 42.85%, respectively. Across the three classical engineering design problems, RSO consistently achieves the best results. Overall, it can be concluded that RSO is particularly effective for solving high-dimensional optimization problems.

show abstract

An Atomic Retrospective Learning Bare Bone Particle Swarm Optimization

Cited by 2 publications

References 17 publications

A novel meta-heuristic algorithm for high-dimensional problems: Rhinopithecus Swarm Optimization

A novel meta-heuristic algorithm for high-dimensional problems: Rhinopithecus Swarm Optimization

A rhinopithecus swarm optimization algorithm for complex optimization problem

Contact Info

Product

Resources

About