Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies

Area of Science:

• Artificial Intelligence
• Swarm Intelligence
• Optimization Algorithms

Background:

• The Salp Swarm Algorithm (SSA) is a nature-inspired metaheuristic optimization technique.
• Original SSA faces challenges with exploration-exploitation balance, slow convergence, and local optima stagnation.

Purpose of the Study:

• To develop a modified Salp Swarm Algorithm (VC-SSA) addressing SSA's limitations.
• To enhance exploitation, exploration, convergence speed, and global search balance.

Main Methods:

• Introduced a velocity clamping mechanism to improve exploitation and accuracy.
• Incorporated a reduction factor to enhance exploration and accelerate convergence.
• Developed an adaptive weight mechanism for a balanced search strategy.

Main Results:

• VC-SSA demonstrated superior performance on benchmark test problems and CEC 2017 tasks.
• Experimental results showed VC-SSA outperforming canonical SSA, its variants, and other metaheuristics.
• VC-SSA achieved optimal results in mobile robot path planning, proving its practical applicability.

Conclusions:

• VC-SSA effectively overcomes the limitations of the standard SSA.
• The proposed modifications significantly enhance optimization capabilities.
• VC-SSA is a promising tool for complex optimization problems, including mobile robot path planning.