.png){kind=icon}
.png){kind=icon}
Return
Multi-task snake optimization algorithm for global optimization and planar kinematic arm control problem
.png){kind=icon}
OA
AI
DOI：10.7717/peerj-cs.2688.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-task optimization (MTO) algorithms aim to simultaneously solve multiple optimization tasks. Addressing issues such as limited optimization precision and high computational costs in existing MTO algorithms, this article proposes a multi-task snake optimization (MTSO) algorithm. The MTSO algorithm operates in two phases: first, independently handling each optimization problem; second, transferring knowledge. Knowledge transfer is determined by the probability of knowledge transfer and the selection probability of elite individuals. Based on this decision, the algorithm either transfers elite knowledge from other tasks or updates the current task through self-perturbation. Experimental results indicate that, compared to other advanced MTO algorithms, the proposed algorithm achieves the most accurate solutions on multitask benchmark functions, the five-task and 10-task planar kinematic arm control problems, the multitask robot gripper problem, and the multitask car side-impact design problem.
Keywords:
Multi-task optimization
Snake optimization
Multitask snake optimization algorithm
Planar kinematic arm control problem
Intelligence algorithm
Journal
IF:
2.5
Papers:
3.4K
Citations:
6.9K
Organization
No organization information available