Multi-task snake optimization algorithm for global optimization and planar kinematic arm control problem.

Area of Science:

• Engineering
• Computer Science
• Optimization Algorithms

Background:

• Existing multi-task optimization (MTO) algorithms face challenges with limited precision and high computational demands.
• There is a need for more efficient and accurate MTO methods to solve complex, interconnected problems.

Purpose of the Study:

• To propose a novel multi-task snake optimization (MTSO) algorithm.
• To address the limitations of existing MTO approaches, specifically improving optimization precision and reducing computational costs.

Main Methods:

• The proposed MTSO algorithm employs a two-phase approach: independent problem handling and knowledge transfer.
• Knowledge transfer is governed by probabilities of transfer and elite individual selection, enabling either cross-task elite knowledge sharing or self-perturbation for task updates.

Main Results:

• The MTSO algorithm demonstrated superior accuracy compared to advanced MTO algorithms on multitask benchmark functions.
• Significant improvements were observed in complex applications, including planar kinematic arm control, robot gripper design, and car side-impact design problems.

Conclusions:

• The MTSO algorithm offers a more accurate and computationally efficient solution for multi-task optimization problems.
• Its effective knowledge transfer mechanism contributes to enhanced performance across diverse and challenging engineering and computational tasks.